Master of Science in Computer Science

Fully Online
18 months
2250 hours | 90 ECTS
Degree
Scaler Neovarsity
Accreditation:
EQF7

About

The course teaches students comprehensive and specialised subjects in computer science; it teaches students cutting edge engineering skills to solve real-world problems using computational thinking and tools, as well as soft skills in communication, collaboration, and project management that enable students to succeed in real-world business environments. Most of this program is case (or) project-based where students learn by solving real-world problems end to end. This program has core courses that focus on computational thinking and problems solving from first principles. The core courses are followed by specialization courses that teach various aspects of building real-world systems. This is followed by more advanced courses that focus on research level topics, which cover state of the art methods. The program also has a capstone project at the end, wherein students can either work on building end to end solutions to real world problems (or) work on a research topic. The program also focuses on teaching the students the “ability to learn” so that they can be lifelong learners constantly upgrading their skills. Students can choose from a spectrum of courses to specialize in a specific sub-area of Computer Science like Artificial Intelligence and Machine Learning, Cloud Computing, Software

Engineering, or Data Science, etc.

Supporting your global mobility
Supporting your global mobility

Global Recognition

Woolf degrees align with major international qualification frameworks, ensuring global recognition and comparability. Earn your degree in the most widely recognized accreditation system in the world.

Learn More About Degree Mobility

Our accreditation through the Malta Further and Higher Education Authority (MFHEA) provides a solid foundation for credential recognition worldwide.

Success stories
Success stories

How students have found success through Woolf

"As a working parent, I needed something flexible and manageable. Woolf’s structure fit me perfectly. I was nervous at first, balancing work, parenting, and midnight classes, but the support, resources, and sense of community kept me going."
Andreia Caroll
Clinical Research Nurse
“Woolf and Scaler’s hands-on Master’s program gave me the practical skills and confidence I was missing after my undergraduate degree. Real projects, professional tools, and mentorship transformed how I think, build, and solve problems — leading me to a career as a Software Engineer.”
Bhavya Dhiman
Master’s in Computer Science
"Woolf provided me flexibility, a strong community, and high quality education. It really broadened my perspective and significantly improved my communication skills. I graduated not just more knowledgeable, but also more confident and well-rounded."
Brian Etemesi
Software Engineer
“Woolf’s flexible, accredited program gave me structure, community, and the confidence to grow. From landing my dream internship to winning a hackathon, Woolf opened doors and shaped both my career and mindset.”
Dominion Yusuf
Higher Diploma in Computer Science
"As a working parent, I needed something flexible and manageable. Woolf’s structure fit me perfectly. I was nervous at first, balancing work, parenting, and midnight classes, but the support, resources, and sense of community kept me going."
Andreia Caroll
Clinical Research Nurse
“Woolf and Scaler’s hands-on Master’s program gave me the practical skills and confidence I was missing after my undergraduate degree. Real projects, professional tools, and mentorship transformed how I think, build, and solve problems — leading me to a career as a Software Engineer.”
Bhavya Dhiman
Master’s in Computer Science
"Woolf provided me flexibility, a strong community, and high quality education. It really broadened my perspective and significantly improved my communication skills. I graduated not just more knowledgeable, but also more confident and well-rounded."
Brian Etemesi
Software Engineer
“Woolf’s flexible, accredited program gave me structure, community, and the confidence to grow. From landing my dream internship to winning a hackathon, Woolf opened doors and shaped both my career and mindset.”
Dominion Yusuf
Higher Diploma in Computer Science
• Develop advanced, innovative, and multi-disciplinary problem-solving skills • Communicate computer science methods and tools clearly and unambiguously to specialised and non-specialised audiences • Develop advanced abilities related to computer science operational procedures and implement them in response to changing environments • Critically evaluate alternative approaches to solving real world engineering and technological problems using cutting edge techniques in computer science on the basis of academic scholarship and case studies, demonstrating reflection on social and ethical responsibilities • Formulate technological judgments and plans despite incomplete information by integrating knowledge and approaches from various computer science domains including machine learning, distributed computing, and cloud computing. • Enquire critically into the theoretical strategies for solving real-world problems using computational thinking and tools. • Develop new skills in response to emerging knowledge and techniques and demonstrate leadership skills and innovation in complex and unpredictable contexts Apply their technological abilities to produce innovative solutions to real-world problems and implement techniques learned in the course

Course Structure

Relational Databases
125 hours | 5 ECTS

About

This is a core and foundational course which aims to equip the student with the ability to model, design, implement and query relational database systems for real- world data storage & processing needs. Students would start with diagrammatic tools (ER-diagram) to map a real world data storage problem into entities, relationships and keys. Then, they learn to translate the ER-diagram into a relational model with tables. SQL is then introduced as a de facto tool to create, modify, append, delete, query and manipulate data in a relational database. Due to SQL’s popularity, the course spends considerable time building the ability to write optimized and complex queries for various data manipulation tasks. The module exposes students to various real world SQL examples to build solid practical knowledge. Students then move on to understanding various trade-offs in modern relational databases like the ones between storage space and latency. Designing a database would need a solid understanding of normal forms to minimize data duplication, indexing for speedup and flattening tables to avoid complex joins in low-latency environments. These real-world database design strategies are discussed with practical examples from various domains. Most of this course uses the opensource MySQL database and cloud-hosted relational databases (like Amazon RDS) to help students apply the concepts learned on real databases via assignments.

Teachers

Devaguptapu Venkata Abhinav
Devaguptapu Venkata Abhinav
Mahesh Jagadeesha
Mahesh Jagadeesha

Intended learning outcomes

Knowledge
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Acquire knowledge of SQL as tool to create, modify, append, delete, query and manipulate data in a relational database
  • Develop a specialised knowledge of key strategies related to Relational Databases
  • Critically evaluate diverse scholarly views on relational databases
  • Develop a critical knowledge of relational databases
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Creatively apply Relational Databases methods to develop critical and original solutions for computational problems
  • Apply an in-depth domain-specific knowledge and understanding to Relational Databases
  • Autonomously gather material and organise it into a coherent presentation or essay
Competencies
  • Act autonomously in identifying research problems and solutions related to Relational Databases
  • Apply a professional and scholarly approach to research problems pertaining to Relational Databases
  • Create synthetic contextualised discussions of key issues related to Relational Databases
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Relational Databases
  • Demonstrate self-direction in research and originality in solutions developed for Relational Databases
  • Efficiently manage interdisciplinary issues that arise in connection to implementation and query of relational databases
Introduction to Problem-Solving Techniques: Part 1
125 hours | 5 ECTS

About

The ability to solve problems is a skill, and just like any other skill, the more one practices, the better one gets. So how exactly does one practice problem solving? Learning about different problem-solving strategies and when to use them will give a good start. Problem solving is a process. Most strategies provide steps that help you identify the problem and choose the best solution. Building a toolbox of problem-solving strategies will improve problem solving skills. With practice, students will be able to recognize and choose among multiple strategies to find the most appropriate one to solve complex problems. The course will focus on developing problem-solving strategies such as abstraction, modularity, recursion, iteration, bisection, and exhaustive enumeration. The course will also introduce arrays and some of their real-world applications, such as prefix sum, carry forward, subarrays, and 2-dimensional matrices. Examples will include industry-relevant problems and dive deeply into building their solutions with various approaches, recognizing each’s limitations (i.e when to use a data structure and when not to use a data structure). By the end of this course a student can come up with the best strategy which can optimize both time and space complexities by choosing the best data structure suitable for a given problem

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Acquire knowledge of various methods for structuring data in arrays
  • Critically evaluate diverse scholarly views on the appropriateness of various problem-solving strategies
  • Develop a specialised knowledge of key strategies related to structuring data
  • Develop a critical understanding of problem-solving strategies in computing
  • Critically assess the relevance of theories of problem-solving for business applications in the domain of software development
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Creatively apply various programming methods to develop critical and original solutions to computational problems
  • Autonomously gather material and organise it into a coherent presentation or essay
  • Apply an in-depth domain-specific knowledge and understanding to problem solving
Competencies
  • Apply a professional and scholarly approach to research problems pertaining to data structures
  • Solve problems and be prepared to take leadership decisions related to applying problem-solving heuristics
  • Act autonomously in identifying research problems and solutions related to arrays and their real-world applications
  • Create synthetic contextualised discussions of key issues related to problem-solving, and moving from algorithmic to heuristic problem-solving strategies.
  • Demonstrate self-direction in research and originality in solutions developed for solving problems related to data structures
  • Efficiently manage interdisciplinary issues that arise in connection to problem solving
Introduction to Computer Programming: Part 1
125 hours | 5 ECTS

About

This course helps students translate advanced mathematical/statistical/scientific concepts into code. This is a module for writing code to solve real-world problems. It introduces programming concepts (such as control structures, recursion, classes and objects) assuming no prior programming knowledge, to make this course accessible to advanced professionals from scientific fields like Biology, Physics, Medicine, Chemistry, Civil & Mechanical Engineering etc. After building a strong foundation for converting scientific knowledge into programming concepts, the course advances to dive deeply into Object-Oriented Programming and its methodologies. It also covers when and how to use inbuilt-data structures like 1-Dimensional and 2-Dimensional Arrays before introducing the concepts of computational complexity to help students write optimized code using appropriate data structures and algorithmic design methods. The module can be taught to allow students to learn these concepts using a modern programming language such as Java or Python. The course offers students the ability to identify and solve computer programming problems in scientific fields

at a graduate level.

Teachers

Daggubati Hari Krishna
Daggubati Hari Krishna
Devaguptapu Venkata Abhinav
Devaguptapu Venkata Abhinav

Intended learning outcomes

Knowledge
  • Critically evaluate diverse scholarly views on computational complexity
  • Acquire knowledge of various methods for structuring data
  • Develop a specialised knowledge of key strategies related to Object-Oriented Programming
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Develop a critical understanding of a modern programming language such as Java or Python
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding to computer programming
  • Creatively apply various programming methods to develop critical and original solutions to computational problems
  • Autonomously gather material and organise it into a coherent presentation or essay
Competencies
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of computer programming
  • Apply a professional and scholarly approach to research problems pertaining to computational complexity
  • Demonstrate self-direction in research and originality in solutions developed for modern programming languages
  • Efficiently manage interdisciplinary issues that arise in connection to data structured in 1- and 2-dimensional arrays
  • Create synthetic contextualised discussions of key issues related to converting scientific knowledge into programming concepts, and how to instantiate these using Object-Oriented methods
  • Act autonomously in identifying research problems and solutions related to Object-Oriented programming
Mathematics for Computer Science
125 hours | 5 ECTS

About

Mathematics and computer science are closely related fields. Problems in computer science are often formalized and solved with mathematical methods. It is likely that many important problems currently facing computer scientists will be solved by researchers skilled in algebra, analysis, combinatorics, logic, and/or probability theory, as well as computer science. This course covers discrete mathematics for computer science and engineering. Topics may include asymptotic notation and growth of functions; permutations and combinations; counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions. Students will be able to explain and apply the basic methods of discrete (noncontinuous) mathematics in computer science. They will be able to use these methods in subsequent courses in the design and analysis of algorithms, computability theory, software engineering, and computer systems. The focus of the course is real-world problems and applications often found in business and industry.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a specialised knowledge of evaluating and describing algorithmic performance using tools from discrete mathematics
  • Acquire knowledge of various methods for optimizing algorithm design
  • Develop a critical understanding of discrete mathematics as a tool in software development
  • Critically assess the relevance of theories of recursivity and induction for business applications in the domain of computational problem-solving
  • Critically evaluate diverse scholarly views on the appropriateness of various mathematical approaches to software development problems
Skills
  • Autonomously gather material and organise it into a coherent presentation or essay
  • Apply an in-depth domain-specific knowledge and understanding of discrete mathematics to algorithmic designs
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Creatively apply various programming methods to most efficiently implement state machines in algorithmic design
Competencies
  • Create synthetic contextualised discussions of key issues related to applications of discrete mathematics in computer science
  • Act autonomously in identifying research problems and solutions related to the real-world application of discrete mathematics
  • Solve problems and be prepared to take leadership decisions related to applying discrete mathematics to optimizing algorithms
  • Efficiently manage interdisciplinary issues that arise in connection to permutations and combinations in algorithm design
  • Apply a professional and scholarly approach to research problems pertaining to the growth of functions
  • Demonstrate self-direction in research and originality in solutions developed for solving problems related to discrete probability
Product Analytics
125 hours | 5 ECTS

About

This course teaches students how to analyse the ways users engage with a service. This method, called product analytics, helps businesses track and analyse user data. Students will learn more deeply what is required to move a product from idea to implementation, through to launch, and then on to iterative improvements. The course teaches how to measure progress, validate or update product hypotheses, and present product learnings. Also, students will gain experience in making informed decisions, as well as how to present findings and make an analytics-informed business case to win support for a product

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically evaluate diverse scholarly views on assessing user behaviours
  • Develop a specialised knowledge of frameworks for measuring user engagement, such as diagnostics, key performance indicators (KPI), and other metrics
  • Critically assess the relevance of theories of user behaviour for product development
  • Acquire knowledge of various methods for testing hypotheses about the viability of a product and about how users engage with it
  • Develop a critical understanding of product design and development
Skills
  • Creatively apply various visual and written methods for proposing a technical solution to a real-world problem to other technical and managerial-level audiences, and for documenting that solution
  • Apply an in-depth domain-specific knowledge and understanding of system design and implementation in business
  • Autonomously gather material and organise it into a coherent presentation or essay
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
Competencies
  • Create synthetic contextualised discussions of key issues related to product sense, and how to tell whether a product is worth bringing to market.
  • Solve problems and be prepared to take leadership decisions related to developing data-informed business cases about bringing products to market and iterating upon them.
  • Apply a professional and scholarly approach to research problems pertaining to measuring user engagement.
  • Act autonomously in identifying research problems and solutions related to product analytics.
  • Efficiently manage interdisciplinary issues that arise in connection to designing a product and bringing it to market
  • Demonstrate self-direction in research and originality in testing and validating hypotheses about a product and its users.
Further Studies in Data Science and Data Analytics
125 hours | 5 ECTS

About

This advanced graduate class addresses a unique topic on a rotating basis in order to keep the program at the forefront of scholarly research and industry practice. Every year the academic staff member will approve of a new topic to be covered. The bibliography will contain not less than 8 peer-reviewed articles or scholarly publications reflecting the current topic. Though the exact topic will vary, the emphasis of this module is practical, domain- specific issues in data science. Topics might include data handling, big data management systems, optimization, sparse signal recovery, principal component analysis, or deeper explorations of text mining, natural language processing, computer vision, or other topics introduced in other modules. Often, Further Studies in Data Science and Data Analytics will extend, complicate, or otherwise deepen the topic taken on in its predecessor course, Studies in Data Science and Data Analytics, giving students who elect this sequence to develop genuine expertise in a specific domain

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically evaluate diverse scholarly views on the utility of data science in a given domain
  • Develop a critical understanding of how data science works in a specific domain
  • Develop a specialised knowledge of strategies for working with large data from varied sources
  • Acquire knowledge of various methods for deploying data science algorithms to solve domain-specific problems
  • Critically assess the relevance of theories of data science or data analytics in solving practical, domain-specific problems
Skills
  • Apply an in-depth domain-specific knowledge and understanding of data science and data analytics to a practical problem
  • Creatively apply various visual and written methods for proposing a technical solution to a real-world problem to other technical and managerial-level audiences, and for documenting that solution
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Autonomously gather material and organise it into a coherent presentation or essay
Competencies
  • Solve problems and be prepared to take leadership decisions related to developing a data-informed approach to a domain-specific problem
  • Efficiently manage interdisciplinary issues that arise in connection to the practice of data science at scale
  • Apply a professional and scholarly approach to research problems pertaining to data science or data analytics in a specific domain
  • Create synthetic contextualised discussions of key issues related to a practical data science or data analytics problem
  • Act autonomously in identifying research problems and solutions related to massive data sets
  • Demonstrate self-direction in research and originality in solutions developed to solve practical data problems
Studies in Data Science and Data Analytics
125 hours | 5 ECTS

About

This advanced graduate class addresses a unique topic on a rotating basis in order to keep the program at the forefront of scholarly research and industry practice. Every year the academic staff member will approve of a new topic to be covered. The bibliography will contain not less than 8 peer-reviewed articles or scholarly publications reflecting the current topic. Though the exact topic will vary, the emphasis of this module is practical, domain- specific issues in data science. Topics might include data handling, big data management systems, optimization, sparse signal recovery, principal component analysis, or deeper explorations of text mining, natural language processing, computer vision, or other topics introduced in other modules

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a critical understanding of how data science works in a specific domain
  • Develop a specialised knowledge of strategies for working with large data from varied sources
  • Critically evaluate diverse scholarly views on the utility of data science in a given domain
  • Acquire knowledge of various methods for deploying data science algorithms to solve domain-specific problems
  • Critically assess the relevance of theories of data science or data analytics in solving practical, domain-specific problems
Skills
  • Creatively apply various visual and written methods for proposing a technical solution to a real-world problem to other technical and managerial-level audiences, and for documenting that solution
  • Autonomously gather material and organise it into a coherent presentation or essay
  • Apply an in-depth domain-specific knowledge and understanding of data science and data analytics to a practical problem
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
Competencies
  • Solve problems and be prepared to take leadership decisions related to developing a data-informed approach to a domain-specific problem
  • Act autonomously in identifying research problems and solutions related to massive data sets
  • Demonstrate self-direction in research and originality in solutions developed to solve practical data problems
  • Apply a professional and scholarly approach to research problems pertaining to data science or data analytics in a specific domain
  • Create synthetic contextualised discussions of key issues related to a practical data science or data analytics problem.
  • Efficiently manage interdisciplinary issues that arise in connection to the practice of data science at scale
Foundations of Machine Learning
125 hours | 5 ECTS

About

This course focuses on building basic classification and regression models and understanding these models rigorously both with a mathematical and an applicative focus. It opens with a basic introduction to high dimensional geometryof points, distance-metrics, hyperplanes and hyperspheres. Then, it introduces the mathematical formulation of logistic regression to find a separating hyperplane. Vector calculus and gradient descent (GD)-based algorithms are explored to learn to solve the optimization problem, including computational variations of GD like mini-batch and stochastic gradient descent. The course also covers other popular classification and regression methods like k-Nearest Neighbours, Naive Bayes, Decision Trees, Linear Regression etc, to show how each of these techniques performs under various real-world situations like the presence of outliers, imbalanced data, multi class classification etc. Lectures on bias and variance tradeoff and various techniques to avoid overfitting and underfitting are incorporated. Algorithms are taught from a Bayesian viewpoint along with geometric intuition. This course would be heavily hands-on where students apply all these classical techniques to real world problems

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a critical understanding of classification and regression machine learning problems
  • Acquire knowledge of various methods for solving both classification and regression problems, such as k-nearest neighbours, naïve Bayes, decision trees, and linear regression
  • Critically evaluate diverse scholarly views on Bayesian and geometric methods for evaluating algorithms
  • Critically assess the relevance of theories of machine learning in the realm of software engineering
  • Develop a specialised knowledge of the use of gradient descent and related algorithms for optimising solutions
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding of the use cases of machine learning algorithms in business
  • Autonomously gather material and organise it into a coherent presentation or essay
  • Creatively apply various visual and written methods for explaining machine learning solutions to expert and, where possible, nontechnical audiences
Competencies
  • Efficiently manage interdisciplinary issues that arise in connection to over- and underfitting of data to models
  • Demonstrate self-direction in research and originality in solutions developed to account for imbalanced data
  • Create synthetic contextualised discussions of key issues related to classification and regression problems
  • Solve problems and be prepared to take leadership decisions related to selecting algorithms for machine learning problems
  • Apply a professional and scholarly approach to research problems pertaining to gradient descent-based algorithms
  • Act autonomously in identifying research problems and solutions related to the classification problems in machine learning
Advanced Python Programming
125 hours | 5 ECTS

About

builds on introductory programming courses to illustrate object-oriented programming concepts, database design in Python, and the basics of Machine Learning with Python libraries. Students will learn how to solve problems in Python, develop design patterns in Python code, develop internet applications with Python, and collaborate with other students to implement projects. The course introduces advanced features such as decorators and generators, as well as a thorough exploration of the Python development environment. This course is designed to prepare students for an entry-level developer position.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically evaluate diverse scholarly views on developing design patterns in Python
  • Critically assess the relevance of theories of statistical analysis in the realm of software engineering
  • Develop a critical understanding of programming in Python for object-oriented design
  • Acquire knowledge of various methods for using Python libraries for machine learning
  • Develop a specialized knowledge of mathematically-oriented Python libraries such as NumPy, SciPy, and Pandas beyond an introductory level
Skills
  • Creatively apply various visual and written methods for developing meaningful visualisations of mathematical data sets
  • Autonomously gather material and organise it into a coherent presentation or essay
  • Apply an in-depth domain-specific knowledge and understanding of the importance of data analysis in business
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
Competencies
  • Apply a professional and scholarly approach to research problems pertaining to object-oriented programming in Python
  • Demonstrate self-direction in research and originality in solutions developed for real-world problems using Python libraries and algorithms
  • Solve problems and be prepared to take leadership decisions related to the implementation of web applications in Python
  • Act autonomously in identifying research problems and solutions related to the developing in Python
  • Create synthetic contextualised discussions of key issues related to problem-solving in Python
  • Efficiently manage interdisciplinary issues that arise in connection to translating mathematical ideas and solutions into code
Statistical Programming
125 hours | 5 ECTS

About

This module focuses on representing statistical techniques in code, and may be

conducted in Python, R, or another relevant language. Such languages provide

libraries that can handle a wide variety of statistical techniques like linear and

nonlinear modeling, classical statistical tests, time-series analysis, classification,

clustering and graphical techniques, and is highly extensible.

Learning to work in statistically-oriented programming language environments can

equip you with the following skills among many others:

  1. An effective way of data handling (using arrays for example) and storing

data in a structured manner.

  1. Expertise in diverse tools and libraries for Data Analysis

  2. Ability to present complex data in a graphical and visual format for easy

understanding of the data and further solutions.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically assess the relevance of theories of statistical analysis for business applications in the realm of software engineering
  • Critically evaluate diverse scholarly views on the graphical presentation of complex data
  • Acquire knowledge of various methods for structuring data in arrays
  • Develop a critical understanding of a statistical programming language and its use in cleaning and analysing data
  • Develop a specialised knowledge of statistical techniques such as linear and nonlinear modelling, time-series analysis, and clustering
Skills
  • Autonomously gather material and organise it into a coherent presentation or essay
  • Apply an in-depth domain-specific knowledge and understanding of the importance of data analysis in business
  • Creatively apply various visual and written methods for developing meaningful visualisations of complex data sets
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
Competencies
  • Act autonomously in identifying research problems and solutions related to statistical methods in programming
  • Create synthetic contextualised discussions of key issues related to handling and storing data
  • Solve problems and be prepared to take leadership decisions related to selecting tools and libraries for data analysis
  • Efficiently manage interdisciplinary issues that arise in connection to structuring data
  • Apply a professional and scholarly approach to research problems pertaining to statistical tests, modelling, and visualisations
  • Demonstrate self-direction in research and originality in solutions developed for presenting complex data in graphical or visual formats
Distributed Systems with High-Level System Design
125 hours | 5 ECTS

About

A distributed system is an application that executes a collection of protocols to coordinate the actions of multiple processes on a network, such that all components cooperate together to perform a single or small set of related tasks. Goals of a Distributed System: ● Transparency -> End user does not know what lies behind and how the system is working internally. ● Scalability - > Refers to the growth of the system. ● Availability -> Refers to the system's uptime. The module will carefully examine three case studies, with attention to such topics as: ● Basics of High Level System Design and consistent Hashing ● Caching ● CAP Theorem ● Replication and Master-Slave

● NoSQL

● Differences between SQL and NoSQL

● Multi Master

● Apache Zookeeper & Apache Kafka

● Case Study on ElasticSearch

● AWS S3 and Quad Trees

● Design Distributed Crawler

● Microservices and Containerisation

● Hotstar & IRCTC System design

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically evaluate diverse scholarly views on containerisation as a system architecture strategy
  • Critically assess the relevance of theories of distributed system design for business applications in the realm of software engineering
  • Acquire knowledge of various methods for optimising the tradeoffs between consistency and availability in the presence of partitions
  • Develop a specialised knowledge of hashing and caching strategies in distributed systems
  • Develop a critical understanding of software architecture design
Skills
  • Creatively apply various visual and written methods for developing high-level system architecture designs
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding of the importance of scalability in software engineering
  • Autonomously gather material and organise it into a coherent presentation or essay
Competencies
  • Act autonomously in identifying research problems and solutions related to implementing SQL and NoSQL designs
  • Apply a professional and scholarly approach to research problems pertaining to tradeoffs between consistency and availability when distributed systems are partitioned
  • Demonstrate self-direction in research and originality in solutions developed for search across distributed environments
  • Solve problems and be prepared to take leadership decisions related to designing distributed systems that can scale
  • Create synthetic contextualised discussions of key issues related to designing system architecture that is capable of scaling
  • Efficiently manage interdisciplinary issues that arise in connection to microservices and containerisation
Computer Systems and Their Fundamentals
125 hours | 5 ECTS

About

This core course equips the student with knowledge of database management systems, operating systems and computer networks. At the end of the course, students will have a critical understanding of the architecture of computers and networks, as well has how programs interact with these. Students begin with mapping data storage problems (as they had done in Relational Databases) to understand how data is stored in a distributed network, and related issues such as concurrency. Subsequently, students cover operating systems with an overview of process scheduling, process synchronisation and memory management techniques with disk scheduling. The module concludes with computer networks, where we will be discussing all of the computer network layers and their protocols in detail.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically evaluate diverse scholarly views on the appropriateness of various approaches to memory management in operating systems
  • Critically assess the relevance of theories of database design for business applications in the domain of software engineering
  • Develop a critical understanding of relational database strategies, process and memory management in operating systems, and computer network protocols
  • Develop a specialised knowledge of optimising relational database performance in low-latency environments
  • Acquire knowledge of various methods for troubleshooting computer network layers
Skills
  • Creatively apply various programming methods to most efficiently design databases that perform well under specified constraints
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding of the importance of relational databases in modern software engineering
  • Autonomously gather material and organise it into a coherent presentation or essay
Competencies
  • Act autonomously in identifying research problems and solutions related to the real-world application of relational databases
  • Solve problems and be prepared to take leadership decisions related to relational database design to solve computational and business problems
  • Create synthetic contextualised discussions of key issues related to the optimal design and use of databases, operating systems, and computer networks
  • Apply a professional and scholarly approach to research problems pertaining to the design of databases in low-latency environments
  • Efficiently manage interdisciplinary issues that arise in connection to process management in operating systems
  • Demonstrate self-direction in research and originality in solutions developed for optimising performance of computer networks
Data Structures
125 hours | 5 ECTS

About

This course is aimed to build a strong foundational knowledge of data structures (DS) used extensively in computing. The module starts with introducing time and space complexity notations and estimation for code snippets. This helps students be able to make trade-offs between various Data Structures while solving real world computational problems. The module introduces most widely used basic data structures like Dynamic arrays, multi-dimensional arrays, Lists, Strings, Hash Tables, Binary Trees, Balanced Binary Trees, Priority Queues and Graphs. The module discusses multiple implementation variations for each of the above data-structures along with trade-offs in space and time for each implementation. In this course, students implement these data-structures from scratch to gain a solid understanding of their inner workings. Students are also introduced to how to use the built-in data-structures available in various programming languages/libraries like Python/NumPy/C++ STL/Java/JavaScript. Students solve real-world problems where they must use an optimal DS to solve a computational problem at hand.

Teachers

Devaguptapu Venkata Abhinav
Devaguptapu Venkata Abhinav
Gujjula Sashank Reddy
Gujjula Sashank Reddy

Intended learning outcomes

Knowledge
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Acquire knowledge widely used basic data structures like Dynamic arrays, multi- dimensional arrays, Lists, Strings, Hash Tables, Binary Trees, Balanced Binary Trees, Priority Queues and Graphs
  • Critically evaluate diverse scholarly views on data structures
  • Develop a specialized knowledge of key strategies related to Data Structures and their usage in computer science
  • Develop a critical knowledge of Data Structures and their implementation
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding of Data Structures
  • Autonomously gather material and organize it into coherent data structures
  • Apply data structures in a creative way to develop original, critical solutions to real world problems.
Competencies
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Data Structures and their implementation
  • Act autonomously in identifying research problems and solutions related to Data Structures and their implementation
  • Apply a professional and scholarly approach to research problems pertaining to Data Structures and their implementation
  • Efficiently manage interdisciplinary issues that arise in connection to Data Structures and their implementation
  • Demonstrate self-direction in research and originality in solutions developed for Data Structures and their implementation
  • Create synthetic contextualized discussions of key issues related to Data Structures and the different approaches to their implementation
Design and Analysis of Algorithms
125 hours | 5 ECTS

About

This is a foundational and mandatory course which aims to build student’s ability to apply various algorithmic design methods to provide an optimal solution to computational problems. This course starts with time and space complexity analysis of divide and conquer algorithms using recursion-tree based methods and Master’s theorem. Students would also learn about amortized time and space complexity analysis for randomized/probabilistic algorithms. Various algorithmic design strategies would be introduced via real world examples and problems. Students would learn when, where and how to optimally use Divide and Conquer, Dynamic programming (top-down and button-up), Greedy, Backtracking and Randomization strategies with examples. The module uses various practical examples from Array manipulations, Sorting, Searching, String manipulations, Tree & Graphs traversals, Graph path-finding, Spanning Trees etc., to introduce the above algorithmic strategies in action. Students would implement many of the above algorithmic design methods from scratch as part of the assignments. The module also introduces how some of these popular algorithms are readily available via popular libraries in various programming languages

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a specialized knowledge of key strategies related to design and analysis of algorithms
  • Acquire knowledge of various algorithmic design methods
  • Develop a critical knowledge of design and analysis of algorithms
  • Critically evaluate diverse scholarly views on design and analysis of algorithms
  • Critically assess the relevance of theories for business applications in the domain of technology
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding to design and analysis of algorithms
  • Autonomously gather material and organize it into a coherent presentation or essay
  • Creatively apply various algorithmic design methods to develop critical and original solutions to computational problems
Competencies
  • Act autonomously in identifying research problems and solutions related to design and analysis of algorithms
  • Efficiently manage interdisciplinary issues that arise in connection to design and analysis of algorithms
  • Apply a professional and scholarly approach to research problems pertaining to design and analysis of algorithms
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of design and analysis of algorithms
  • Demonstrate self-direction in research and originality in solutions developed for design and analysis of algorithms
  • Create synthetic contextualized discussions of key issues related to design and analysis of algorithms to provide solutions to computational problems
Back End Development
125 hours | 5 ECTS

About

This is a foundational course on building server-side (or backend) applications using popular JavaScript runtime environments like Node.js. Students will learn event driven programming for building scalable backend for web applications. The module teaches various aspects of Node.js like setup, package manager, client- server programming and connecting to various databases and REST APIs. Most of these concepts would be covered in a hands-on manner with real world examples and applications built from scratch using Node.js on Linux servers. This course also provides an introduction to Linux server administration and scripting with special focus on web-development and networking. Students learn to use Linux monitoring tools (like Monit) to track the health of the servers. The module also provides an introduction to Express.js which is a popular light-weight framework for Node.js applications. Given the practical nature of this course, this would involve building actual website backends via assignments/projects for ecommerce, online learning and/or photo-sharing.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a critical knowledge of Back End Development
  • Acquire knowledge of key aspects of Node.js like setup, package manager, client-server programming and connecting to various databases and REST
  • Critically evaluate diverse scholarly views on Back End Development
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Develop a specialised knowledge of key strategies related to Back End Development
Skills
  • Autonomously gather material and organise it into coherent problem sets or presentations
  • Creatively apply Back End Development tools to develop critical and original solutions for computational problems
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding to Back End Development applications
Competencies
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Back End Development
  • Create synthetic contextualised discussions of key issues related to Back End Development
  • Efficiently manage interdisciplinary issues that arise in connection to Back End Development
  • Act autonomously in identifying research problems and solutions related to Back End Development
  • Apply a professional and scholarly approach to research problems pertaining to Back End Development
  • Demonstrate self-direction in research and originality in solutions developed for Back End Development
Front End UI/UX Development
125 hours | 5 ECTS

About

This is a hands-on course on designing responsive, modern, and lightweight UI for

web, mobile, and desktop applications using HTML5, CSS, and Frameworks like

Bootstrap 4. This course starts with an introduction to how web browsers, mobile

apps, and web servers work. We then dive into each of the nitty-gritty details of

HTML5 to build webpages. We would start with simple web pages and then

graduate to more complex layouts and features in HTML like forms, iFrames,

multimedia playback, and using web APIs. We then go on to learn stylesheets based on CSS 4 and how browsers interpret CSS files to render web pages. Once again, we use multiple real-world example web pages to learn the internals of CSS4. We learn popular good practices for writing responsive HTML and CSS code, which is also interoperable on mobile browsers, apps, and desktop apps. We would introduce students to building desktop apps using HTML and CSS using toolkits like Electron. We also study popular frameworks for front end development like Bootstrap 4, which can speed up UI development significantly.

Teachers

Devaguptapu Venkata Abhinav
Devaguptapu Venkata Abhinav
Mahesh Jagadeesha
Mahesh Jagadeesha

Intended learning outcomes

Knowledge
  • Develop a critical knowledge of front end development
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Acquire knowledge of popular frameworks/libraries in use: React.js, jQuery and AngularJS
  • Develop a specialised knowledge of key strategies related to front end development
  • Critically evaluate diverse scholarly views on front end development
Skills
  • Autonomously gather material and organise it into coherent problem sets or presentations
  • Creatively apply front end development applications to develop critical and original solutions for computational problems
  • Apply an in-depth domain-specific knowledge and understanding to front end development solutions
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
Competencies
  • Apply a professional and scholarly approach to research problems pertaining to front end development
  • Act autonomously in identifying research problems and solutions related to front end development
  • Create synthetic contextualised discussions of key issues related to front end development
  • Efficiently manage interdisciplinary issues that arise in connection to front end development
  • Demonstrate self-direction in research and originality in solutions developed for front end development
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of front end developmen
Deep Learning for Natural Language Processing
125 hours | 5 ECTS

About

This course focuses on modelling sequences (text, music, time-series, genes) using deep-learning models. We start with a simple Recurrent Neural Network and its limitations with long-sequences. Students learn LSTMs and GRUs which can handle significantly longer sequences to model sequence data like text, music, gene-sequences and time-series data. We study variations of LSTM like bi-directional LSTMs and encoder-decoder architectures. This is followed by a detailed study of attention mechanism and Transformer based models which are currently the state-of-the-art for NLP and sequence modelling. The module teaches encoder-decoder Transformers, BERT, BERT-variations, GPT-1,2 &3 models from both the architectural and mathematical viewpoints and also a practical viewpoint. Students learn to implement many of these complex models from scratch (using TensorFlow 2 and Keras) to gain a deeper understanding of how they work internally. Students will study popular applications of deep-learning in NLP like parts-of-speech tagging, question-answering systems, conversational engines (chatbots), Semantic search with low-latency etc. For each of these problems, Students will study cutting edge deep-learning models along with code implementations.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Acquire knowledge of popular applications of deep-learning in NLP like parts-of- speech tagging, question-answering systems, conversational engines (chatbots), etc
  • Develop a specialised knowledge of key strategies related to Deep Learning for NLP
  • Critically evaluate diverse scholarly views on Deep Learning for NLP
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Develop a critical knowledge of Deep Learning for NLP
Skills
  • Creatively apply Deep Learning for NLP techniques to develop critical and original solutions for computational problems
  • Apply an in-depth domain-specific knowledge and understanding to NLP solutions
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Autonomously gather material and organise it into coherent problem sets or presentation
Competencies
  • Act autonomously in identifying research problems and solutions related to Deep Learning for NLP
  • Create synthetic contextualised discussions of key issues related to Deep Learning for NLP
  • Apply a professional and scholarly approach to research problems pertaining to Deep Learning for NLP
  • Efficiently manage interdisciplinary issues that arise in connection to Deep Learning for NLP
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Deep Learning for NLP
  • Demonstrate self-direction in research and originality in solutions developed for Deep Learning for NLP
Deep Learning for Computer Vision
125 hours | 5 ECTS

About

This course provides a comprehensive overview of Computer vision problems and how they can be tackled using various Convolutional Neural networks (CNNs). Students start with classical image processing operations like edge detection, convolution, shape detectors and colour space conversions. This is followed by a foundational understanding of Deep-Convolutional Neural networks and how their training and evaluation works. We introduce various CNN specific layers like pooling-layers and upsampling layers. We also introduce various Data Augmentation techniques that are very helpful for image-related problems. This is followed by a dive deep into the internals of popular CNN architectures like: AlexNet, VGGNet, ResNet etc. Students also learn how to use these methods practically for transfer learning. Students will study how various computer-vision related tasks like image segmentation, image-generation, object detection and localization, contrastive learning etc., can be performed using state of the art algorithms for each of these tasks. Most of these techniques would be studied directly from the original research papers and open-source code provided by the authors. Students would also implement some of these algorithms from scratch in this course.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a specialised knowledge of key strategies related to Deep Learning for Computer Vision
  • Develop a critical knowledge of Deep Learning for Computer Vision
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Critically evaluate diverse scholarly views on Deep Learning for Computer Vision
  • Acquire knowledge of popular CNN architectures like: AlexNet, VGGNet, ResNet
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Creatively apply computer vision techniques to develop critical and original solutions for computational problems
  • Autonomously gather material and organise it into coherent problem sets or presentation
  • Apply an in-depth domain-specific knowledge and understanding to Deep Learning for Computer Vision techniques
Competencies
  • Act autonomously in identifying research problems and solutions related to Deep Learning for Computer Vision
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Deep Learning for Computer Vision
  • Create synthetic contextualised discussions of key issues related to Deep Learning for Computer Vision
  • Demonstrate self-direction in research and originality in solutions developed for Deep Learning for Computer Vision
  • Efficiently manage interdisciplinary issues that arise in connection to Deep Learning for Computer Vision
  • Apply a professional and scholarly approach to research problems pertaining to Deep Learning for Computer Vision
Introduction to Deep Learning
125 hours | 5 ECTS

About

This course provides a strong mathematical and applicative introduction to Deep Learning. The module starts with the perceptron model as an over simplified approximation to a biological neuron. We motivate the need for a network of neurons and how they can be connected to form a Multi Layered Perceptron (MLPs). This is followed by a rigorous understanding of back-propagation algorithms and its limitations from the 1980s. Students study how modern deep learning took off with improved computational tools and data sets. We teach more modern activation units (like ReLU and SeLU) and how they overcome problems with the more classical Sigmoid and Tanh units. Students learn weight initialization methods, regularization by dropouts, batch normalization etc., to ensure that deep MLPs can be successfully trained. The module teaches variants of Gradient Descent that have been specifically designed to work well for deep learning systems like ADAM, AdaGrad, RMSProp etc. Students also learn AutoEncoders, VAEs and Word2Vec as unsupervised, encoding deep-learning architectures. We apply all of the foundational theory learned to various real world problems using TensorFlow 2 and Keras. Students also understand how TensorFlow 2 works internally with specific focus on computational graph processing

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Acquire knowledge of deep learning systems like ADAM, AdaGrad, RMSProp etc. Students also learn AutoEncoders, VAEs and Word2Vec
  • Develop a specialised knowledge of key strategies related to Deep Learning
  • Develop a critical knowledge of Deep Learning
  • Critically evaluate diverse scholarly views on Deep Learning
Skills
  • Apply an in-depth domain-specific knowledge and understanding to Deep Learning
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Autonomously gather material and organise it into coherent problem sets or presentation
  • Creatively apply Deep Learning techniques to develop critical and original solutions for computational problems
Competencies
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Deep Learning
  • Act autonomously in identifying research problems and solutions related to Deep Learning
  • Apply a professional and scholarly approach to research problems pertaining to Deep Learning
  • Create synthetic contextualized discussions of key issues related to Deep Learning
  • Demonstrate self-direction in research and originality in solutions developed for Deep Learning
  • Efficiently manage interdisciplinary issues that arise in connection to Deep Learning
High Dimensional Data Analysis
125 hours | 5 ECTS

About

This course is aimed to help learners understand various techniques and algorithms to visualize, analyse and understand high dimensional data which is very common in Data Science and ML. The module starts with linear algebraic methods like Principal Component Analysis (PCA) and SVD (Singular Value Decomposition) for obtaining linear projection of high dimensional data. This is followed by more advanced nonlinear and state of the art techniques like t-SNE and UMAP for visualizing high dimensional data. Each of these techniques would be covered in full mathematical detail from first principles along with applying them to real world datasets in NLP, Genomics and internet-datasets. Students will also study how PCA and SVD are related to general Matrix Factorization techniques. To analyse and understand high dimensional un-labelled data, students learn clustering techniques like K-Means, Gaussian Mixture models, Hierarchical Clustering and DBSCAN. The modules show how some of the techniques are mathematically related to Matrix Factorization. Students study various outlier detection techniques based on density, proximity, factorization and cluster analysis.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a specialised knowledge of key strategies related to Data Analysis
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Develop a critical knowledge of Data Analysis
  • Critically evaluate diverse scholarly views on Data Analysis
  • Acquire knowledge of clustering techniques like K-Means, Gaussian Mixture models, Hierarchical Clustering and DBSCAN
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding to Data Analysis techniques
  • Creatively apply various techniques to develop critical and original solutions for computational problems
  • Autonomously gather material and organise it into coherent problems sets or presentation
Competencies
  • Act autonomously in identifying research problems and solutions related to Data Analysis
  • Create synthetic contextualised discussions of key issues related to Data Analysis
  • Apply a professional and scholarly approach to research problems pertaining to Data Analysis
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Data Analysis
  • Efficiently manage interdisciplinary issues that arise in connection to Data Analysis
  • Demonstrate self-direction in research and originality in solutions developed for data analysis
Introduction to Machine Learning
125 hours | 5 ECTS

About

This course focuses on building basic classification and regression models and understanding these models rigorously both with a mathematical and an applicative focus. The module starts with a basic introduction to high dimensional geometry of points, distance-metrics, hyperplanes and hyperspheres. We build on top this to introduce the mathematical formulation of logistic regression to find a separating hyperplane. Students learn to solve the optimization problem using vector calculus and gradient descent (GD) based algorithms. The module introduces computational variations of GD like mini-batch and stochastic gradient descent. Students also learn other popular classification and regression methods like k-Nearest Neighbours, NaiveI Bayes, Decision Trees, Linear Regression etc. Students also learn how each of these techniques under various real world situations like the presence of outliers, imbalanced data, multi class classification etc. Students learn bias and variance trade-off and various techniques to avoid overfitting and underfitting. Students also study these algorithms from a Bayesian viewpoint along with geometric intuition. This module is hands-on and students apply all these classical techniques to real world problems.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically evaluate diverse scholarly views on machine learning
  • Acquire knowledge of bias and variance trade-off, and various techniques to avoid overfitting and underfitting
  • Develop a specialised knowledge of key strategies related to machine learning
  • Develop a critical knowledge of machine learning
  • Critically assess the relevance of theories for business applications in the domain of technology
Skills
  • Autonomously gather material and organise it into coherent problem sets and presentation
  • Creatively apply regression models to develop critical and original solutions for computational issues
  • Apply an in-depth domain-specific knowledge and understanding to machine learning solutions
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
Competencies
  • Apply a professional and scholarly approach to research problems pertaining to machine learning
  • Demonstrate self-direction in research and originality in solutions developed for machine learning
  • Act autonomously in identifying research problems and solutions related to machine learning
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of machine learning
  • Create synthetic contextualised discussions of key issues related to machine learning
  • Efficiently manage interdisciplinary issues that arise in connection to machine learning
Numerical Programming in Python
125 hours | 5 ECTS

About

This course helps students translate mathematical/statistical/scientific concepts into code. This is a foundational course for writing code to solve Data Science ML &AI problems. It introduces basic programming concepts (like control structures, recursion, classes and objects) from scratch, assuming no prerequisites, to make this course accessible to students from non-computational scientific fields like Biology, Physics, Medicine, Chemistry, Civil & Mechanical Engineering etc. After building a strong foundation, the course advances to dive deep into core Mathematical libraries like NumPy, Scipy and Pandas. Students also learn when and how to use inbuilt-data structures like Lists, Dicts, Sets and Tuples. The module introduces the concepts of computational complexity to help students write optimized code using appropriate data structures and algorithmic design methods. The module does not dive deep into the data structures and algorithm design methods in this course – that is available in the ‘Data Structures and Algorithms’ module. This course is valuable for all students specializing in mathematical sub-areas of CS like ML, Data Science, Scientific Computing etc.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a specialised knowledge of key strategies related to Numerical programming in Python
  • Develop a critical knowledge of Numerical programming in Python
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Acquire knowledge of core Mathematical libraries like NumPy, Scipy and Pandas
  • Critically evaluate diverse scholarly views on Numerical programming in Python
Skills
  • Autonomously gather material and organize it into a coherent problem sets or presentation
  • Apply an in-depth domain-specific knowledge and understanding to numerical programming in Python
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Create new solutions that are critical to solving computational problems through creatively applying code writing.
Competencies
  • Create synthetic contextualised discussions of key issues related to Numerical programming in Python
  • Efficiently manage interdisciplinary issues that arise in connection to Numerical programming in Python
  • Apply a professional and scholarly approach to research problems pertaining to Numerical programming in Python
  • Demonstrate self-direction in research and originality in solutions developed for Numerical programming in Python
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Numerical programming in Python
  • Act autonomously in identifying research problems and solutions related to Numerical programming in Python
Introduction to Problem-Solving Techniques: Part 2
125 hours | 5 ECTS

About

This course is a follow-up to Introduction to Problem-Solving Techniques: Part 1, and as part of their academic planning process with Woolf staff, students will ordinarily take that course first. Part 2 deepens the approach to data structures by including such topics as stacks, queues, linked lists, and trees, and discussing in detail real world applications of each approach and their comparative strengths and limitations (i.e. when to use a data structure and when not to use a data structure). This course will also include hashing techniques along with recursion and subset problems. This course will have rigorous homework and assignments to support the introduction of more than 4 data structures. By the end of this course a student can come up with the best strategy which can optimize both time and space complexities by choosing the best data structure suitable for a given problem.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a specialised knowledge of key strategies related to optimising algorithms
  • Acquire knowledge of various methods for hashing and recursion
  • Develop a critical understanding of problem-solving strategies in computing
  • Critically assess the relevance of theories of problem-solving for business applications in the domain of software development
  • Critically evaluate diverse scholarly views on the appropriateness of various problem-solving strategies
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding to problem solving
  • Creatively apply various programming methods to develop critical and original solutions to computational problems
  • Autonomously gather material and organise it into a coherent presentation or essay
Competencies
  • Act autonomously in identifying research problems and solutions related to the real-world applications of optimising strategies
  • Demonstrate self-direction in research and originality in solutions developed for solving problems related to data structures
  • Apply a professional and scholarly approach to research problems pertaining to hashing techniques, recursion, and subset problems
  • Create synthetic contextualised discussions of key issues related to problem-solving, especially around optimising time and space complexities
  • Efficiently manage interdisciplinary issues that arise in connection to problem solving
  • Solve problems and be prepared to take leadership decisions related to applying problem-solving heuristics
JavaScript
125 hours | 5 ECTS

About

This course is a hands-on course covering JavaScript from basics to advanced concepts in detail using multiple examples. We start with basic programming concepts like variables, control statements, loops, classes and objects. Students also learn basic data-structures like Strings, Arrays and dates. Students also learn to debug our code and handle errors gracefully in code. We learn popular style guides and good coding practices to build readable and reusable code which is also highly performant. We then learn how web browsers execute JavaScript code using V8 engine as an example. We also cover concepts like JIT-compiling which helps JS code to run faster. This is followed by slightly advanced concepts like DOM, Async- functions, Web APIs and AJAX which are very popularly used in modern front end development. We learn how to optimize JavaScript code to run on both mobile apps and mobile browsers along with Desktop browsers and as desktop apps via ElectronJS. Most of this course would be covered via real world examples and by learning from JS code of popular open-source websites and libraries.

Teachers

Devaguptapu Venkata Abhinav
Devaguptapu Venkata Abhinav
Mahesh Jagadeesha
Mahesh Jagadeesha

Intended learning outcomes

Knowledge
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Acquire knowledge of popular style guides and good coding practices to build readable and reusable code which is also highly performant
  • Develop a critical knowledge of JavaScript
  • Critically evaluate diverse scholarly views on JavaScript
  • Develop a specialised knowledge of key strategies related to JavaScript
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Autonomously gather material and organise into a coherent problem sets or presentations
  • Creatively apply JavaScript concepts to develop critical and original solutions for computational problems.
  • Apply an in-depth domain-specific knowledge and understanding to JavaScript tools
Competencies
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of JavaScript
  • Act autonomously in identifying research problems and solutions related to JavaScript
  • Demonstrate self-direction in research and originality in solutions developed for JavaScript
  • Apply a professional and scholarly approach to research problems pertaining to JavaScript
  • Create synthetic contextualised discussions of key issues related to JavaScript
  • Efficiently manage interdisciplinary issues that arise in connection to JavaScript
Front End Development
125 hours | 5 ECTS

About

This course builds upon the introductory JavaScript course to acquaint students of popular and modern frameworks to build the front end. We focus on three very popular frameworks/libraries in use: React.js, jQuery and AngularJS. We start with React.js, one of the most popular and advanced ones amongst the three. students learn various components and data flow to learn to architect real world front end using React.js. This would be achieved via multiple code examples and code-walkthroughs from scratch. We would also dive into React Native which is a cross platform Framework to build native mobile and smart-TV apps using JavaScript. This helps students to build applications for various platforms using only JavaScript. jQuery is one of the oldest and most widely used JavaScript libraries, which students cover in detail. Students specifically focus on how jQuery can simplify event handling, AJAX, HTML DOM tree manipulation and create CSS animations. We also provide a hands-on introduction to AngularJS to architect model-view-controller (MVC) based dynamic web pages.

Teachers

Devaguptapu Venkata Abhinav
Devaguptapu Venkata Abhinav
Mahesh Jagadeesha
Mahesh Jagadeesha

Intended learning outcomes

Knowledge
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Critically evaluate diverse scholarly views on Front end UI/UX development
  • Acquire knowledge of HTML5, CSS and Frameworks like Bootstrap 4
  • Develop a specialised knowledge of key strategies related to Front end UI/UX development
  • Develop a critical knowledge of Front end UI/UX development
Skills
  • Autonomously gather material and organise into a coherent problem sets or presentation
  • Creatively apply Front end UI/UX development applications to develop critical and original solutions for computational problems.
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding to technology
Competencies
  • Apply a professional and scholarly approach to research problems pertaining to Front end UI/UX development
  • Act autonomously in identifying research problems and solutions related to Front end UI/UX developmen
  • Demonstrate self-direction in research and originality in solutions developed for Front end UI/UX development
  • Efficiently manage interdisciplinary issues that arise in connection to Front end UI/UX development
  • Create synthetic contextualised discussions of key issues related to Front end UI/UX development
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Front end UI/UX development
Advanced Machine Learning
125 hours | 5 ECTS

About

This course introduces more advanced ML techniques like ensembles: bagging, boosting, cascading and stacking classifiers and regressors. It covers both the theoretical foundations and applicative details of these techniques along with popular implementations of boosting like LightGBM, CatBoost and XGBoost. Students also delve into kernel methods with specific focus on SVMs for classification and regression. Students will study state of the art model agnostic feature importance and model-interpretability techniques like LIME and SHAP. Students also study classical NLP based text encoding methods like Bag-of-words, TF-IDF etc. The module teaches various classical methods in time series analysis and forecasting like ARMA, ARIMA etc. Students also learn how to pose time series forecasting problems as regression and classification problems to leverage well studied ML techniques. This is followed by various domain and problem specific Feature engineering techniques that are often helpful in real world problem solving. Students will study methods like error analysis, ablative analysis etc., to debug and understand why and where a model is performing well and where it is not performing well. This will further help us in designing appropriate features. Students study model calibration techniques like Platt Scaling, Isotonic Regression etc. Later in this course, we cover how to build recommender systems using content-based and collaborative filtering methods. The module also teaches the detailed solution of the Netflix prize (2009) and various recent advances in RecSys

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a specialised knowledge of key strategies related to Advanced Machine Learning
  • Acquire knowledge of model calibration techniques like Platt Scaling, Isotonic Regression
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Develop a critical knowledge of Advanced Machine Learning
  • Critically evaluate diverse scholarly views on Advanced Machine Learning
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Autonomously gather material and organise it into coherent problem sets or presentations
  • Apply an in-depth domain-specific knowledge and understanding to Advanced Machine Learning
  • Creatively apply Advanced Machine Learning techniques to develop critical and original solutions for computation problems
Competencies
  • Efficiently manage interdisciplinary issues that arise in connection to Advanced Machine Learning
  • Demonstrate self-direction in research and originality in solutions developed for Advanced Machine Learning
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Advanced Machine Learning
  • Apply a professional and scholarly approach to research problems pertaining to Advanced Machine Learning
  • Act autonomously in identifying research problems and solutions related to Advanced Machine Learning
  • Create synthetic contextualised discussions of key issues related to Advanced Machine Learning
Productionization of ML Systems
125 hours | 5 ECTS

About

This course aims to build the core competency of building real world end-to-end ML systems and deploy them into production for a variety of problems and scenarios. Students would learn a variety of ML systems ranging from high throughput and low latency internet scale systems to low compute power and energy constrained IoT devices like smart watches. Students will study the ML lifecycle and various components in detail. We also use real world ML platforms like Google’s KubeFlow, TensorFlow Lite, and Amazon’s SageMaker to implement real world systems and understand the engineering trade-offs and challenges. Students also learn relevant technologies and tools like Containerization (Docker) and Container Orchestration (Kubernetes) and Git which are often used extensively in real world scalable ML systems. This course is a hands-on course where we solve

multiple real world cases and discuss solutions built by various companies and

organizations to provide the students a comprehensive understanding of varied

systems and design choices.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Acquire knowledge of tools like Containerization (Docker) and Container Orchestration (Kubernetes) and Git
  • Critically evaluate diverse scholarly views on Productionization of Machine Learning
  • Critically assess the relevance of theories for business applications in the domain of Productionization of Machine Learning
  • Develop a specialised knowledge of key strategies related to Productionization of Machine Learning
  • Develop a critical knowledge of Productionization of Machine Learning Systems
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Autonomously gather material and organise it into coherent problem sets or presentation
  • Creatively apply ML systems to develop critical and original solutions for computational problems
  • Apply an in-depth domain-specific knowledge and understanding to technolog
Competencies
  • Apply a professional and scholarly approach to research problems pertaining to Productionization of ML Systems
  • Create synthetic contextualised discussions of key issues related to Productionization of ML Systems
  • Demonstrate self-direction in research and originality in solutions developed for Productionization of ML Systems
  • Act autonomously in identifying research problems and solutions related to Productionization of ML Systems
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of ML Productionization
  • Efficiently manage interdisciplinary issues that arise in connection to Productionization of ML Systems
System Design
125 hours | 5 ECTS

About

This course is aimed at equipping students with skills to architect the high level design (a.k.a. system design) of software and data systems. We start with some of the good to have properties of large complex software systems like scalability, reliability, availability, consistency etc. The module teaches various patterns and design choices we have to satisfy each of these good to have properties. We then go on to understand key components of system design like load-balancers, microservices, reverse-proxies, content-delivery networks etc. Students learn how each of them work internally along with real world implementations of each. We study various NoSQL data stores, their internal architectures and where to use which one with real-world examples. Students also learn popular data encoding schemes like XML and JSON. We learn how to build data pipelines using batch and stream processing systems. We also work on multiple real world cases on architecting on the cloud using popular open-source libraries and tools. Students will study design documents and high-level-design of popular internet applications and services like video-conferencing, recommender-systems, peer-to-peer chat, voice-assistants etc.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a critical knowledge of System Design
  • Critically evaluate diverse scholarly views on System Design
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Acquire knowledge of popular data encoding schemes like XML and JSON
  • Develop a specialised knowledge of key strategies related to System Design
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Creatively apply system design components to develop critical and original solutions for computational problems
  • Apply an in-depth domain-specific knowledge and understanding to System Design solutions
  • Autonomously gather material and organise it into coherent problem sets or presentations
Competencies
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of System Design
  • Apply a professional and scholarly approach to research problems pertaining to System Design
  • Create synthetic contextualised discussions of key issues related to System Design
  • Efficiently manage interdisciplinary issues that arise in connection to System Design
  • Demonstrate self-direction in research and originality in solutions developed for System Design
  • Act autonomously in identifying research problems and solutions related to System Design
Data Visualisation Tools
125 hours | 5 ECTS

About

This course is aimed to build a strong foundational knowledge of Data Analytics tools used extensively in the Data Science field. There now are powerful data visualisation tools used in the business analytics industry to process and visualise raw business data in a very presentable and understandable format. A good example is Tableau, used by all data analytics departments of companies and in data analytics companies in various fields for its ease of use and efficiency. Tableau uses relational databases, Online Analytical Processing Cubes, Spreadsheets, cloud databases to generate graphical type visualisations. Course starts with visualisations and moves to an in-depth look at the different chart and graph functions, calculations, mapping and other functionality. Students will be taught quick table calculations, reference lines, different types of visualisations, bands and distributions, parameters, motion chart, trends and forecasting, formatting, stories, performance recording and advanced mapping. At the end of this course, students will be prepared, if they desire, to earn such industry desktop certifications as a Tableau Desktop Specialist, a Tableau Certified Associate, or a Tableau Certified Professional.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically assess the relevance of theories of data visualisation for business applications in the realm of software engineering
  • Develop a specialised knowledge of such concepts as bands and distributions, parameters, motion chart, trends and forecasting, formatting, stories, performance recording and advanced mapping
  • Acquire knowledge of various methods for telling stories with data across different formats
  • Develop a critical understanding of key data science concepts as implemented in common software packages
  • Critically evaluate diverse scholarly views on advanced visualisation strategies
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Creatively apply various visual and written methods for developing data visualisations
  • Autonomously gather material and organise it into a coherent presentation or essay
  • Apply an in-depth domain-specific knowledge and understanding of the importance of data storytelling in software engineering
Competencies
  • Efficiently manage interdisciplinary issues that arise in connection to advanced visual analytics
  • Act autonomously in identifying research problems and solutions related to implementing data science visualisations from scratch
  • Demonstrate self-direction in research and originality in solutions developed for data visualisation
  • Apply a professional and scholarly approach to research problems pertaining to data visualisations, including dashboards and storytelling
  • Create synthetic contextualised discussions of key issues related to time and space complexity in data science
  • Solve problems and be prepared to take leadership decisions related to data visualisation strategies
Applied Statistics
125 hours | 5 ECTS

About

This course introduces basic probability theory , statistical methods and computational algorithms to perform mathematically rigorous data analysis. The course starts with basic foundational concepts of random variables, histograms, and various plots (PMF, PDF and CDF). Students learn various popular discrete and continuous distributions like Bernoulli, Binomial, Poisson, Gaussian, Exponential, Pareto, log-normal etc., both mathematically and from an applicative perspective. Students learn various measures like mean, median, percentiles, quantiles, variance and interquartile-range. Students learn the pros and cons of each metric and understand when and how to use them in practice. Students will learn conditional probability and Bayes theorem in the applied context of real-world problems in medicine and healthcare. The module teaches the foundations of non-parametric statistics and applies them to solve problems using computational tools. Students learn various methods to determine correlations rigorously in data. This is followed by applied and mathematical understanding of the statistics underlying control- treatment (A/B) experiments and hypothesis testing. The module engages computation tools in modern statics like Bootstrapping, Monte-Carlo methods, RANSAC etc.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Acquire knowledge of popular discrete and continuous distributions (like Bernoulli, Binomial, Poisson, Gaussian, Exponential, Pareto, and log-normal)
  • Develop a specialised knowledge of key strategies related to Applied Statistics
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Develop a critical knowledge of Applied Statistics
  • Critically evaluate diverse scholarly views on Applied Statistics
Skills
  • Apply an in-depth domain-specific knowledge and understanding of applied statistics
  • Autonomously gather material and organise it into a coherent problem set or presentation
  • Creatively apply basic probability theory to develop critical and original solutions for computational problems
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
Competencies
  • Demonstrate self-direction in research and originality in solutions developed for Applied Statistics
  • Create synthetic contextualised discussions of key issues related to Applied Statistics
  • Efficiently manage interdisciplinary issues that arise in connection to Applied Statistics
  • Apply a professional and scholarly approach to research problems pertaining to probability theory to perform mathematically rigorous data analysis
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Applied Statistics
  • Act autonomously in identifying research problems and solutions related to Applied Statistics
Applied Computer Science Project
250 hours | 10 ECTS

About

This is a project-based course, with the aim of building the required skills for creating web-based software systems. The course covers the entire lifecycle of building software projects, from requirement gathering and scope definition from a product document, to designing the architecture of the system, and all the way to delivery and maintenance of the software system. The course covers both frontend, which is, building browser-based interfaces for users, using frontend web frameworks, and also building the backend, which is the server running an API to serve the information to the frontend, and running on an

SQL or similar database management system for storage.

All aspects of delivering a software project, including security, user authentication and authorisation, monitoring and analytics, and maintaining the project are covered. The course also covers the aspects of project maintenance, like using a version control system, setting up continuous integration and deployment pipelines

and bug trackers. The Applied Computer Science Project will focus on the outcome of how computer science and its associated tools as a field of study can be beneficial to other fields of study or how such tools can be applied to modify existing processes for better outcomes.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Acquire knowledge of various methods for version control
  • Develop a critical understanding of modern computational applications
  • Critically evaluate diverse scholarly views on database management
  • Critically assess the relevance of theories of web security for cloud deployment
  • Develop a specialised knowledge of key strategies for designing well-architected information management systems
Skills
  • Creatively apply various visual and written methods for proposing a technical solution to a real-world problem to other technical and managerial-level audiences, and for documenting that solution
  • Apply an in-depth domain-specific knowledge and understanding of system design and implementation in business
  • Autonomously gather material and organise it into a coherent presentation or essay
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
Competencies
  • Efficiently manage interdisciplinary issues that arise in connection to deploying a modern, web-based system.
  • Apply a professional and scholarly approach to research problems pertaining to real-world computational complexities.
  • Act autonomously in identifying research problems and solutions related to modern computational tools and methods.
  • Create synthetic contextualised discussions of key issues related to real-world software design, implementation, and deployment situations
  • Demonstrate self-direction in research and originality in solutions developed for robust and reliable cloud deployments.
  • Solve problems and be prepared to take leadership decisions related to developing and deploying cloud-oriented software solutions.
Deep Learning for Natural Language Processing
125 hours | 5 ECTS

About

This course focuses on modelling sequences (text, music, time-series, genes) using deep-learning models. We start with a simple Recurrent Neural Network and its limitations with long-sequences. Students learn LSTMs and GRUs which can handle significantly longer sequences to model sequence data like text, music, gene-sequences and time-series data. We study variations of LSTM like bi-directional LSTMs and encoder-decoder architectures. This is followed by a detailed study of attention mechanism and Transformer based models which are currently the state-of-the-art for NLP and sequence modelling. The module teaches encoder-decoder Transformers, BERT, BERT-variations, GPT-1,2 &3 models from both the architectural and mathematical viewpoints and also a practical viewpoint. Students learn to implement many of these complex models from scratch (using TensorFlow 2 and Keras) to gain a deeper understanding of how they work internally. Students will study popular applications of deep-learning in NLP like parts-of-speech tagging, question-answering systems, conversational engines (chatbots), Semantic search with low-latency etc. For each of these problems, Students will study cutting edge deep-learning models along with code implementations.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Acquire knowledge of popular applications of deep-learning in NLP like parts-of- speech tagging, question-answering systems, conversational engines (chatbots), etc
  • Develop a specialised knowledge of key strategies related to Deep Learning for NLP
  • Critically evaluate diverse scholarly views on Deep Learning for NLP
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Develop a critical knowledge of Deep Learning for NLP
Skills
  • Creatively apply Deep Learning for NLP techniques to develop critical and original solutions for computational problems
  • Apply an in-depth domain-specific knowledge and understanding to NLP solutions
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Autonomously gather material and organise it into coherent problem sets or presentation
Competencies
  • Act autonomously in identifying research problems and solutions related to Deep Learning for NLP
  • Create synthetic contextualised discussions of key issues related to Deep Learning for NLP
  • Apply a professional and scholarly approach to research problems pertaining to Deep Learning for NLP
  • Efficiently manage interdisciplinary issues that arise in connection to Deep Learning for NLP
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Deep Learning for NLP
  • Demonstrate self-direction in research and originality in solutions developed for Deep Learning for NLP
Deep Learning for Computer Vision
125 hours | 5 ECTS

About

This course provides a comprehensive overview of Computer vision problems and how they can be tackled using various Convolutional Neural networks (CNNs). Students start with classical image processing operations like edge detection, convolution, shape detectors and colour space conversions. This is followed by a foundational understanding of Deep-Convolutional Neural networks and how their training and evaluation works. We introduce various CNN specific layers like pooling-layers and upsampling layers. We also introduce various Data Augmentation techniques that are very helpful for image-related problems. This is followed by a dive deep into the internals of popular CNN architectures like: AlexNet, VGGNet, ResNet etc. Students also learn how to use these methods practically for transfer learning. Students will study how various computer-vision related tasks like image segmentation, image-generation, object detection and localization, contrastive learning etc., can be performed using state of the art algorithms for each of these tasks. Most of these techniques would be studied directly from the original research papers and open-source code provided by the authors. Students would also implement some of these algorithms from scratch in this course.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a specialised knowledge of key strategies related to Deep Learning for Computer Vision
  • Develop a critical knowledge of Deep Learning for Computer Vision
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Critically evaluate diverse scholarly views on Deep Learning for Computer Vision
  • Acquire knowledge of popular CNN architectures like: AlexNet, VGGNet, ResNet
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Creatively apply computer vision techniques to develop critical and original solutions for computational problems
  • Autonomously gather material and organise it into coherent problem sets or presentation
  • Apply an in-depth domain-specific knowledge and understanding to Deep Learning for Computer Vision techniques
Competencies
  • Act autonomously in identifying research problems and solutions related to Deep Learning for Computer Vision
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Deep Learning for Computer Vision
  • Create synthetic contextualised discussions of key issues related to Deep Learning for Computer Vision
  • Demonstrate self-direction in research and originality in solutions developed for Deep Learning for Computer Vision
  • Efficiently manage interdisciplinary issues that arise in connection to Deep Learning for Computer Vision
  • Apply a professional and scholarly approach to research problems pertaining to Deep Learning for Computer Vision
Introduction to Deep Learning
125 hours | 5 ECTS

About

This course provides a strong mathematical and applicative introduction to Deep Learning. The module starts with the perceptron model as an over simplified approximation to a biological neuron. We motivate the need for a network of neurons and how they can be connected to form a Multi Layered Perceptron (MLPs). This is followed by a rigorous understanding of back-propagation algorithms and its limitations from the 1980s. Students study how modern deep learning took off with improved computational tools and data sets. We teach more modern activation units (like ReLU and SeLU) and how they overcome problems with the more classical Sigmoid and Tanh units. Students learn weight initialization methods, regularization by dropouts, batch normalization etc., to ensure that deep MLPs can be successfully trained. The module teaches variants of Gradient Descent that have been specifically designed to work well for deep learning systems like ADAM, AdaGrad, RMSProp etc. Students also learn AutoEncoders, VAEs and Word2Vec as unsupervised, encoding deep-learning architectures. We apply all of the foundational theory learned to various real world problems using TensorFlow 2 and Keras. Students also understand how TensorFlow 2 works internally with specific focus on computational graph processing

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Acquire knowledge of deep learning systems like ADAM, AdaGrad, RMSProp etc. Students also learn AutoEncoders, VAEs and Word2Vec
  • Develop a specialised knowledge of key strategies related to Deep Learning
  • Develop a critical knowledge of Deep Learning
  • Critically evaluate diverse scholarly views on Deep Learning
Skills
  • Apply an in-depth domain-specific knowledge and understanding to Deep Learning
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Autonomously gather material and organise it into coherent problem sets or presentation
  • Creatively apply Deep Learning techniques to develop critical and original solutions for computational problems
Competencies
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Deep Learning
  • Act autonomously in identifying research problems and solutions related to Deep Learning
  • Apply a professional and scholarly approach to research problems pertaining to Deep Learning
  • Create synthetic contextualized discussions of key issues related to Deep Learning
  • Demonstrate self-direction in research and originality in solutions developed for Deep Learning
  • Efficiently manage interdisciplinary issues that arise in connection to Deep Learning
High Dimensional Data Analysis
125 hours | 5 ECTS

About

This course is aimed to help learners understand various techniques and algorithms to visualize, analyse and understand high dimensional data which is very common in Data Science and ML. The module starts with linear algebraic methods like Principal Component Analysis (PCA) and SVD (Singular Value Decomposition) for obtaining linear projection of high dimensional data. This is followed by more advanced nonlinear and state of the art techniques like t-SNE and UMAP for visualizing high dimensional data. Each of these techniques would be covered in full mathematical detail from first principles along with applying them to real world datasets in NLP, Genomics and internet-datasets. Students will also study how PCA and SVD are related to general Matrix Factorization techniques. To analyse and understand high dimensional un-labelled data, students learn clustering techniques like K-Means, Gaussian Mixture models, Hierarchical Clustering and DBSCAN. The modules show how some of the techniques are mathematically related to Matrix Factorization. Students study various outlier detection techniques based on density, proximity, factorization and cluster analysis.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a specialised knowledge of key strategies related to Data Analysis
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Develop a critical knowledge of Data Analysis
  • Critically evaluate diverse scholarly views on Data Analysis
  • Acquire knowledge of clustering techniques like K-Means, Gaussian Mixture models, Hierarchical Clustering and DBSCAN
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding to Data Analysis techniques
  • Creatively apply various techniques to develop critical and original solutions for computational problems
  • Autonomously gather material and organise it into coherent problems sets or presentation
Competencies
  • Act autonomously in identifying research problems and solutions related to Data Analysis
  • Create synthetic contextualised discussions of key issues related to Data Analysis
  • Apply a professional and scholarly approach to research problems pertaining to Data Analysis
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Data Analysis
  • Efficiently manage interdisciplinary issues that arise in connection to Data Analysis
  • Demonstrate self-direction in research and originality in solutions developed for data analysis
Introduction to Machine Learning
125 hours | 5 ECTS

About

This course focuses on building basic classification and regression models and understanding these models rigorously both with a mathematical and an applicative focus. The module starts with a basic introduction to high dimensional geometry of points, distance-metrics, hyperplanes and hyperspheres. We build on top this to introduce the mathematical formulation of logistic regression to find a separating hyperplane. Students learn to solve the optimization problem using vector calculus and gradient descent (GD) based algorithms. The module introduces computational variations of GD like mini-batch and stochastic gradient descent. Students also learn other popular classification and regression methods like k-Nearest Neighbours, NaiveI Bayes, Decision Trees, Linear Regression etc. Students also learn how each of these techniques under various real world situations like the presence of outliers, imbalanced data, multi class classification etc. Students learn bias and variance trade-off and various techniques to avoid overfitting and underfitting. Students also study these algorithms from a Bayesian viewpoint along with geometric intuition. This module is hands-on and students apply all these classical techniques to real world problems.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically evaluate diverse scholarly views on machine learning
  • Acquire knowledge of bias and variance trade-off, and various techniques to avoid overfitting and underfitting
  • Develop a specialised knowledge of key strategies related to machine learning
  • Develop a critical knowledge of machine learning
  • Critically assess the relevance of theories for business applications in the domain of technology
Skills
  • Autonomously gather material and organise it into coherent problem sets and presentation
  • Creatively apply regression models to develop critical and original solutions for computational issues
  • Apply an in-depth domain-specific knowledge and understanding to machine learning solutions
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
Competencies
  • Apply a professional and scholarly approach to research problems pertaining to machine learning
  • Demonstrate self-direction in research and originality in solutions developed for machine learning
  • Act autonomously in identifying research problems and solutions related to machine learning
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of machine learning
  • Create synthetic contextualised discussions of key issues related to machine learning
  • Efficiently manage interdisciplinary issues that arise in connection to machine learning
Numerical Programming in Python
125 hours | 5 ECTS

About

This course helps students translate mathematical/statistical/scientific concepts into code. This is a foundational course for writing code to solve Data Science ML &AI problems. It introduces basic programming concepts (like control structures, recursion, classes and objects) from scratch, assuming no prerequisites, to make this course accessible to students from non-computational scientific fields like Biology, Physics, Medicine, Chemistry, Civil & Mechanical Engineering etc. After building a strong foundation, the course advances to dive deep into core Mathematical libraries like NumPy, Scipy and Pandas. Students also learn when and how to use inbuilt-data structures like Lists, Dicts, Sets and Tuples. The module introduces the concepts of computational complexity to help students write optimized code using appropriate data structures and algorithmic design methods. The module does not dive deep into the data structures and algorithm design methods in this course – that is available in the ‘Data Structures and Algorithms’ module. This course is valuable for all students specializing in mathematical sub-areas of CS like ML, Data Science, Scientific Computing etc.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a specialised knowledge of key strategies related to Numerical programming in Python
  • Develop a critical knowledge of Numerical programming in Python
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Acquire knowledge of core Mathematical libraries like NumPy, Scipy and Pandas
  • Critically evaluate diverse scholarly views on Numerical programming in Python
Skills
  • Autonomously gather material and organize it into a coherent problem sets or presentation
  • Apply an in-depth domain-specific knowledge and understanding to numerical programming in Python
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Create new solutions that are critical to solving computational problems through creatively applying code writing.
Competencies
  • Create synthetic contextualised discussions of key issues related to Numerical programming in Python
  • Efficiently manage interdisciplinary issues that arise in connection to Numerical programming in Python
  • Apply a professional and scholarly approach to research problems pertaining to Numerical programming in Python
  • Demonstrate self-direction in research and originality in solutions developed for Numerical programming in Python
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Numerical programming in Python
  • Act autonomously in identifying research problems and solutions related to Numerical programming in Python
Distributed Systems with High-Level System Design
125 hours | 5 ECTS

About

A distributed system is an application that executes a collection of protocols to coordinate the actions of multiple processes on a network, such that all components cooperate together to perform a single or small set of related tasks. Goals of a Distributed System: ● Transparency -> End user does not know what lies behind and how the system is working internally. ● Scalability - > Refers to the growth of the system. ● Availability -> Refers to the system's uptime. The module will carefully examine three case studies, with attention to such topics as: ● Basics of High Level System Design and consistent Hashing ● Caching ● CAP Theorem ● Replication and Master-Slave

● NoSQL

● Differences between SQL and NoSQL

● Multi Master

● Apache Zookeeper & Apache Kafka

● Case Study on ElasticSearch

● AWS S3 and Quad Trees

● Design Distributed Crawler

● Microservices and Containerisation

● Hotstar & IRCTC System design

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically evaluate diverse scholarly views on containerisation as a system architecture strategy
  • Critically assess the relevance of theories of distributed system design for business applications in the realm of software engineering
  • Acquire knowledge of various methods for optimising the tradeoffs between consistency and availability in the presence of partitions
  • Develop a specialised knowledge of hashing and caching strategies in distributed systems
  • Develop a critical understanding of software architecture design
Skills
  • Creatively apply various visual and written methods for developing high-level system architecture designs
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding of the importance of scalability in software engineering
  • Autonomously gather material and organise it into a coherent presentation or essay
Competencies
  • Act autonomously in identifying research problems and solutions related to implementing SQL and NoSQL designs
  • Apply a professional and scholarly approach to research problems pertaining to tradeoffs between consistency and availability when distributed systems are partitioned
  • Demonstrate self-direction in research and originality in solutions developed for search across distributed environments
  • Solve problems and be prepared to take leadership decisions related to designing distributed systems that can scale
  • Create synthetic contextualised discussions of key issues related to designing system architecture that is capable of scaling
  • Efficiently manage interdisciplinary issues that arise in connection to microservices and containerisation
Computer Systems and Their Fundamentals
125 hours | 5 ECTS

About

This core course equips the student with knowledge of database management systems, operating systems and computer networks. At the end of the course, students will have a critical understanding of the architecture of computers and networks, as well has how programs interact with these. Students begin with mapping data storage problems (as they had done in Relational Databases) to understand how data is stored in a distributed network, and related issues such as concurrency. Subsequently, students cover operating systems with an overview of process scheduling, process synchronisation and memory management techniques with disk scheduling. The module concludes with computer networks, where we will be discussing all of the computer network layers and their protocols in detail.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically evaluate diverse scholarly views on the appropriateness of various approaches to memory management in operating systems
  • Critically assess the relevance of theories of database design for business applications in the domain of software engineering
  • Develop a critical understanding of relational database strategies, process and memory management in operating systems, and computer network protocols
  • Develop a specialised knowledge of optimising relational database performance in low-latency environments
  • Acquire knowledge of various methods for troubleshooting computer network layers
Skills
  • Creatively apply various programming methods to most efficiently design databases that perform well under specified constraints
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding of the importance of relational databases in modern software engineering
  • Autonomously gather material and organise it into a coherent presentation or essay
Competencies
  • Act autonomously in identifying research problems and solutions related to the real-world application of relational databases
  • Solve problems and be prepared to take leadership decisions related to relational database design to solve computational and business problems
  • Create synthetic contextualised discussions of key issues related to the optimal design and use of databases, operating systems, and computer networks
  • Apply a professional and scholarly approach to research problems pertaining to the design of databases in low-latency environments
  • Efficiently manage interdisciplinary issues that arise in connection to process management in operating systems
  • Demonstrate self-direction in research and originality in solutions developed for optimising performance of computer networks
Data Structures
125 hours | 5 ECTS

About

This course is aimed to build a strong foundational knowledge of data structures (DS) used extensively in computing. The module starts with introducing time and space complexity notations and estimation for code snippets. This helps students be able to make trade-offs between various Data Structures while solving real world computational problems. The module introduces most widely used basic data structures like Dynamic arrays, multi-dimensional arrays, Lists, Strings, Hash Tables, Binary Trees, Balanced Binary Trees, Priority Queues and Graphs. The module discusses multiple implementation variations for each of the above data-structures along with trade-offs in space and time for each implementation. In this course, students implement these data-structures from scratch to gain a solid understanding of their inner workings. Students are also introduced to how to use the built-in data-structures available in various programming languages/libraries like Python/NumPy/C++ STL/Java/JavaScript. Students solve real-world problems where they must use an optimal DS to solve a computational problem at hand.

Teachers

Devaguptapu Venkata Abhinav
Devaguptapu Venkata Abhinav
Gujjula Sashank Reddy
Gujjula Sashank Reddy

Intended learning outcomes

Knowledge
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Acquire knowledge widely used basic data structures like Dynamic arrays, multi- dimensional arrays, Lists, Strings, Hash Tables, Binary Trees, Balanced Binary Trees, Priority Queues and Graphs
  • Critically evaluate diverse scholarly views on data structures
  • Develop a specialized knowledge of key strategies related to Data Structures and their usage in computer science
  • Develop a critical knowledge of Data Structures and their implementation
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding of Data Structures
  • Autonomously gather material and organize it into coherent data structures
  • Apply data structures in a creative way to develop original, critical solutions to real world problems.
Competencies
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Data Structures and their implementation
  • Act autonomously in identifying research problems and solutions related to Data Structures and their implementation
  • Apply a professional and scholarly approach to research problems pertaining to Data Structures and their implementation
  • Efficiently manage interdisciplinary issues that arise in connection to Data Structures and their implementation
  • Demonstrate self-direction in research and originality in solutions developed for Data Structures and their implementation
  • Create synthetic contextualized discussions of key issues related to Data Structures and the different approaches to their implementation
Design and Analysis of Algorithms
125 hours | 5 ECTS

About

This is a foundational and mandatory course which aims to build student’s ability to apply various algorithmic design methods to provide an optimal solution to computational problems. This course starts with time and space complexity analysis of divide and conquer algorithms using recursion-tree based methods and Master’s theorem. Students would also learn about amortized time and space complexity analysis for randomized/probabilistic algorithms. Various algorithmic design strategies would be introduced via real world examples and problems. Students would learn when, where and how to optimally use Divide and Conquer, Dynamic programming (top-down and button-up), Greedy, Backtracking and Randomization strategies with examples. The module uses various practical examples from Array manipulations, Sorting, Searching, String manipulations, Tree & Graphs traversals, Graph path-finding, Spanning Trees etc., to introduce the above algorithmic strategies in action. Students would implement many of the above algorithmic design methods from scratch as part of the assignments. The module also introduces how some of these popular algorithms are readily available via popular libraries in various programming languages

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a specialized knowledge of key strategies related to design and analysis of algorithms
  • Acquire knowledge of various algorithmic design methods
  • Develop a critical knowledge of design and analysis of algorithms
  • Critically evaluate diverse scholarly views on design and analysis of algorithms
  • Critically assess the relevance of theories for business applications in the domain of technology
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding to design and analysis of algorithms
  • Autonomously gather material and organize it into a coherent presentation or essay
  • Creatively apply various algorithmic design methods to develop critical and original solutions to computational problems
Competencies
  • Act autonomously in identifying research problems and solutions related to design and analysis of algorithms
  • Efficiently manage interdisciplinary issues that arise in connection to design and analysis of algorithms
  • Apply a professional and scholarly approach to research problems pertaining to design and analysis of algorithms
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of design and analysis of algorithms
  • Demonstrate self-direction in research and originality in solutions developed for design and analysis of algorithms
  • Create synthetic contextualized discussions of key issues related to design and analysis of algorithms to provide solutions to computational problems
Back End Development
125 hours | 5 ECTS

About

This is a foundational course on building server-side (or backend) applications using popular JavaScript runtime environments like Node.js. Students will learn event driven programming for building scalable backend for web applications. The module teaches various aspects of Node.js like setup, package manager, client- server programming and connecting to various databases and REST APIs. Most of these concepts would be covered in a hands-on manner with real world examples and applications built from scratch using Node.js on Linux servers. This course also provides an introduction to Linux server administration and scripting with special focus on web-development and networking. Students learn to use Linux monitoring tools (like Monit) to track the health of the servers. The module also provides an introduction to Express.js which is a popular light-weight framework for Node.js applications. Given the practical nature of this course, this would involve building actual website backends via assignments/projects for ecommerce, online learning and/or photo-sharing.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a critical knowledge of Back End Development
  • Acquire knowledge of key aspects of Node.js like setup, package manager, client-server programming and connecting to various databases and REST
  • Critically evaluate diverse scholarly views on Back End Development
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Develop a specialised knowledge of key strategies related to Back End Development
Skills
  • Autonomously gather material and organise it into coherent problem sets or presentations
  • Creatively apply Back End Development tools to develop critical and original solutions for computational problems
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding to Back End Development applications
Competencies
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Back End Development
  • Create synthetic contextualised discussions of key issues related to Back End Development
  • Efficiently manage interdisciplinary issues that arise in connection to Back End Development
  • Act autonomously in identifying research problems and solutions related to Back End Development
  • Apply a professional and scholarly approach to research problems pertaining to Back End Development
  • Demonstrate self-direction in research and originality in solutions developed for Back End Development
Front End UI/UX Development
125 hours | 5 ECTS

About

This is a hands-on course on designing responsive, modern, and lightweight UI for

web, mobile, and desktop applications using HTML5, CSS, and Frameworks like

Bootstrap 4. This course starts with an introduction to how web browsers, mobile

apps, and web servers work. We then dive into each of the nitty-gritty details of

HTML5 to build webpages. We would start with simple web pages and then

graduate to more complex layouts and features in HTML like forms, iFrames,

multimedia playback, and using web APIs. We then go on to learn stylesheets based on CSS 4 and how browsers interpret CSS files to render web pages. Once again, we use multiple real-world example web pages to learn the internals of CSS4. We learn popular good practices for writing responsive HTML and CSS code, which is also interoperable on mobile browsers, apps, and desktop apps. We would introduce students to building desktop apps using HTML and CSS using toolkits like Electron. We also study popular frameworks for front end development like Bootstrap 4, which can speed up UI development significantly.

Teachers

Devaguptapu Venkata Abhinav
Devaguptapu Venkata Abhinav
Mahesh Jagadeesha
Mahesh Jagadeesha

Intended learning outcomes

Knowledge
  • Develop a critical knowledge of front end development
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Acquire knowledge of popular frameworks/libraries in use: React.js, jQuery and AngularJS
  • Develop a specialised knowledge of key strategies related to front end development
  • Critically evaluate diverse scholarly views on front end development
Skills
  • Autonomously gather material and organise it into coherent problem sets or presentations
  • Creatively apply front end development applications to develop critical and original solutions for computational problems
  • Apply an in-depth domain-specific knowledge and understanding to front end development solutions
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
Competencies
  • Apply a professional and scholarly approach to research problems pertaining to front end development
  • Act autonomously in identifying research problems and solutions related to front end development
  • Create synthetic contextualised discussions of key issues related to front end development
  • Efficiently manage interdisciplinary issues that arise in connection to front end development
  • Demonstrate self-direction in research and originality in solutions developed for front end development
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of front end developmen
Product Analytics
125 hours | 5 ECTS

About

This course teaches students how to analyse the ways users engage with a service. This method, called product analytics, helps businesses track and analyse user data. Students will learn more deeply what is required to move a product from idea to implementation, through to launch, and then on to iterative improvements. The course teaches how to measure progress, validate or update product hypotheses, and present product learnings. Also, students will gain experience in making informed decisions, as well as how to present findings and make an analytics-informed business case to win support for a product

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically evaluate diverse scholarly views on assessing user behaviours
  • Develop a specialised knowledge of frameworks for measuring user engagement, such as diagnostics, key performance indicators (KPI), and other metrics
  • Critically assess the relevance of theories of user behaviour for product development
  • Acquire knowledge of various methods for testing hypotheses about the viability of a product and about how users engage with it
  • Develop a critical understanding of product design and development
Skills
  • Creatively apply various visual and written methods for proposing a technical solution to a real-world problem to other technical and managerial-level audiences, and for documenting that solution
  • Apply an in-depth domain-specific knowledge and understanding of system design and implementation in business
  • Autonomously gather material and organise it into a coherent presentation or essay
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
Competencies
  • Create synthetic contextualised discussions of key issues related to product sense, and how to tell whether a product is worth bringing to market.
  • Solve problems and be prepared to take leadership decisions related to developing data-informed business cases about bringing products to market and iterating upon them.
  • Apply a professional and scholarly approach to research problems pertaining to measuring user engagement.
  • Act autonomously in identifying research problems and solutions related to product analytics.
  • Efficiently manage interdisciplinary issues that arise in connection to designing a product and bringing it to market
  • Demonstrate self-direction in research and originality in testing and validating hypotheses about a product and its users.
Further Studies in Data Science and Data Analytics
125 hours | 5 ECTS

About

This advanced graduate class addresses a unique topic on a rotating basis in order to keep the program at the forefront of scholarly research and industry practice. Every year the academic staff member will approve of a new topic to be covered. The bibliography will contain not less than 8 peer-reviewed articles or scholarly publications reflecting the current topic. Though the exact topic will vary, the emphasis of this module is practical, domain- specific issues in data science. Topics might include data handling, big data management systems, optimization, sparse signal recovery, principal component analysis, or deeper explorations of text mining, natural language processing, computer vision, or other topics introduced in other modules. Often, Further Studies in Data Science and Data Analytics will extend, complicate, or otherwise deepen the topic taken on in its predecessor course, Studies in Data Science and Data Analytics, giving students who elect this sequence to develop genuine expertise in a specific domain

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically evaluate diverse scholarly views on the utility of data science in a given domain
  • Develop a critical understanding of how data science works in a specific domain
  • Develop a specialised knowledge of strategies for working with large data from varied sources
  • Acquire knowledge of various methods for deploying data science algorithms to solve domain-specific problems
  • Critically assess the relevance of theories of data science or data analytics in solving practical, domain-specific problems
Skills
  • Apply an in-depth domain-specific knowledge and understanding of data science and data analytics to a practical problem
  • Creatively apply various visual and written methods for proposing a technical solution to a real-world problem to other technical and managerial-level audiences, and for documenting that solution
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Autonomously gather material and organise it into a coherent presentation or essay
Competencies
  • Solve problems and be prepared to take leadership decisions related to developing a data-informed approach to a domain-specific problem
  • Efficiently manage interdisciplinary issues that arise in connection to the practice of data science at scale
  • Apply a professional and scholarly approach to research problems pertaining to data science or data analytics in a specific domain
  • Create synthetic contextualised discussions of key issues related to a practical data science or data analytics problem
  • Act autonomously in identifying research problems and solutions related to massive data sets
  • Demonstrate self-direction in research and originality in solutions developed to solve practical data problems
Studies in Data Science and Data Analytics
125 hours | 5 ECTS

About

This advanced graduate class addresses a unique topic on a rotating basis in order to keep the program at the forefront of scholarly research and industry practice. Every year the academic staff member will approve of a new topic to be covered. The bibliography will contain not less than 8 peer-reviewed articles or scholarly publications reflecting the current topic. Though the exact topic will vary, the emphasis of this module is practical, domain- specific issues in data science. Topics might include data handling, big data management systems, optimization, sparse signal recovery, principal component analysis, or deeper explorations of text mining, natural language processing, computer vision, or other topics introduced in other modules

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a critical understanding of how data science works in a specific domain
  • Develop a specialised knowledge of strategies for working with large data from varied sources
  • Critically evaluate diverse scholarly views on the utility of data science in a given domain
  • Acquire knowledge of various methods for deploying data science algorithms to solve domain-specific problems
  • Critically assess the relevance of theories of data science or data analytics in solving practical, domain-specific problems
Skills
  • Creatively apply various visual and written methods for proposing a technical solution to a real-world problem to other technical and managerial-level audiences, and for documenting that solution
  • Autonomously gather material and organise it into a coherent presentation or essay
  • Apply an in-depth domain-specific knowledge and understanding of data science and data analytics to a practical problem
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
Competencies
  • Solve problems and be prepared to take leadership decisions related to developing a data-informed approach to a domain-specific problem
  • Act autonomously in identifying research problems and solutions related to massive data sets
  • Demonstrate self-direction in research and originality in solutions developed to solve practical data problems
  • Apply a professional and scholarly approach to research problems pertaining to data science or data analytics in a specific domain
  • Create synthetic contextualised discussions of key issues related to a practical data science or data analytics problem.
  • Efficiently manage interdisciplinary issues that arise in connection to the practice of data science at scale
Foundations of Machine Learning
125 hours | 5 ECTS

About

This course focuses on building basic classification and regression models and understanding these models rigorously both with a mathematical and an applicative focus. It opens with a basic introduction to high dimensional geometryof points, distance-metrics, hyperplanes and hyperspheres. Then, it introduces the mathematical formulation of logistic regression to find a separating hyperplane. Vector calculus and gradient descent (GD)-based algorithms are explored to learn to solve the optimization problem, including computational variations of GD like mini-batch and stochastic gradient descent. The course also covers other popular classification and regression methods like k-Nearest Neighbours, Naive Bayes, Decision Trees, Linear Regression etc, to show how each of these techniques performs under various real-world situations like the presence of outliers, imbalanced data, multi class classification etc. Lectures on bias and variance tradeoff and various techniques to avoid overfitting and underfitting are incorporated. Algorithms are taught from a Bayesian viewpoint along with geometric intuition. This course would be heavily hands-on where students apply all these classical techniques to real world problems

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Develop a critical understanding of classification and regression machine learning problems
  • Acquire knowledge of various methods for solving both classification and regression problems, such as k-nearest neighbours, naïve Bayes, decision trees, and linear regression
  • Critically evaluate diverse scholarly views on Bayesian and geometric methods for evaluating algorithms
  • Critically assess the relevance of theories of machine learning in the realm of software engineering
  • Develop a specialised knowledge of the use of gradient descent and related algorithms for optimising solutions
Skills
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
  • Apply an in-depth domain-specific knowledge and understanding of the use cases of machine learning algorithms in business
  • Autonomously gather material and organise it into a coherent presentation or essay
  • Creatively apply various visual and written methods for explaining machine learning solutions to expert and, where possible, nontechnical audiences
Competencies
  • Efficiently manage interdisciplinary issues that arise in connection to over- and underfitting of data to models
  • Demonstrate self-direction in research and originality in solutions developed to account for imbalanced data
  • Create synthetic contextualised discussions of key issues related to classification and regression problems
  • Solve problems and be prepared to take leadership decisions related to selecting algorithms for machine learning problems
  • Apply a professional and scholarly approach to research problems pertaining to gradient descent-based algorithms
  • Act autonomously in identifying research problems and solutions related to the classification problems in machine learning
Advanced Python Programming
125 hours | 5 ECTS

About

builds on introductory programming courses to illustrate object-oriented programming concepts, database design in Python, and the basics of Machine Learning with Python libraries. Students will learn how to solve problems in Python, develop design patterns in Python code, develop internet applications with Python, and collaborate with other students to implement projects. The course introduces advanced features such as decorators and generators, as well as a thorough exploration of the Python development environment. This course is designed to prepare students for an entry-level developer position.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically evaluate diverse scholarly views on developing design patterns in Python
  • Critically assess the relevance of theories of statistical analysis in the realm of software engineering
  • Develop a critical understanding of programming in Python for object-oriented design
  • Acquire knowledge of various methods for using Python libraries for machine learning
  • Develop a specialized knowledge of mathematically-oriented Python libraries such as NumPy, SciPy, and Pandas beyond an introductory level
Skills
  • Creatively apply various visual and written methods for developing meaningful visualisations of mathematical data sets
  • Autonomously gather material and organise it into a coherent presentation or essay
  • Apply an in-depth domain-specific knowledge and understanding of the importance of data analysis in business
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
Competencies
  • Apply a professional and scholarly approach to research problems pertaining to object-oriented programming in Python
  • Demonstrate self-direction in research and originality in solutions developed for real-world problems using Python libraries and algorithms
  • Solve problems and be prepared to take leadership decisions related to the implementation of web applications in Python
  • Act autonomously in identifying research problems and solutions related to the developing in Python
  • Create synthetic contextualised discussions of key issues related to problem-solving in Python
  • Efficiently manage interdisciplinary issues that arise in connection to translating mathematical ideas and solutions into code
Statistical Programming
125 hours | 5 ECTS

About

This module focuses on representing statistical techniques in code, and may be

conducted in Python, R, or another relevant language. Such languages provide

libraries that can handle a wide variety of statistical techniques like linear and

nonlinear modeling, classical statistical tests, time-series analysis, classification,

clustering and graphical techniques, and is highly extensible.

Learning to work in statistically-oriented programming language environments can

equip you with the following skills among many others:

  1. An effective way of data handling (using arrays for example) and storing

data in a structured manner.

  1. Expertise in diverse tools and libraries for Data Analysis

  2. Ability to present complex data in a graphical and visual format for easy

understanding of the data and further solutions.

Teachers

No items found.

Intended learning outcomes

Knowledge
  • Critically assess the relevance of theories of statistical analysis for business applications in the realm of software engineering
  • Critically evaluate diverse scholarly views on the graphical presentation of complex data
  • Acquire knowledge of various methods for structuring data in arrays
  • Develop a critical understanding of a statistical programming language and its use in cleaning and analysing data
  • Develop a specialised knowledge of statistical techniques such as linear and nonlinear modelling, time-series analysis, and clustering
Skills
  • Autonomously gather material and organise it into a coherent presentation or essay
  • Apply an in-depth domain-specific knowledge and understanding of the importance of data analysis in business
  • Creatively apply various visual and written methods for developing meaningful visualisations of complex data sets
  • Employ the standard modern conventions for the presentation of scholarly work and scholarly referencing
Competencies
  • Act autonomously in identifying research problems and solutions related to statistical methods in programming
  • Create synthetic contextualised discussions of key issues related to handling and storing data
  • Solve problems and be prepared to take leadership decisions related to selecting tools and libraries for data analysis
  • Efficiently manage interdisciplinary issues that arise in connection to structuring data
  • Apply a professional and scholarly approach to research problems pertaining to statistical tests, modelling, and visualisations
  • Demonstrate self-direction in research and originality in solutions developed for presenting complex data in graphical or visual formats

Entry Requirements

Tuition Cost
5,25,000 INR
Student education requirement
Undergraduate (Bachelor’s)

Application Process

1

Submit initial Application

Complete the online application form with your personal information

2

Documentation Review

Submit required transcripts, certificates, and supporting documents

3

Assessment

Your application will be evaluated against program requirements

4

Interview

Selected candidates may be invited for an interview

5

Decision

Receive an admission decision

6

Enrollment

Complete registration and prepare to begin your studies

Ready to advance your education with a globally recognised degree?

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