Master of Science in Computer Science

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.

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

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.

The course prepares students to handle advanced data structures and algorithm

design methods in the separate module, ‘Data Structures’

This course gives the detailed overview on how to approach Low Level Design problems with real-world case studies discussed such as Designing a Pen (Mac/Windows), TicTacToe, BookMyShow (most used event booking app, manages millions of users), Email campaign Management System and detailed design of Splitwise.

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.

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

This is a course that focuses both on architectural design and practical hands-on

learning of the most used cloud services. The module extensively uses Amazon

Web services (AWS) to show real world code examples of various cloud services. It

also covers the core concepts and architectures in a platform agnostic manner so

that students can easily translate these learnings to other cloud platforms (like

Azure, GCP etc.). The module starts with virtualization and how virtualized compute instances are created and configured. Students also learn how to auto-scale applications using load balancers and build fault tolerant applications across a geographically distributed cloud. As relational databases are widely used in most enterprises, students learn how to migrate and scale (both vertically and

horizontally) these databases on the cloud while ensuring enterprise grade security. Virtual private clouds enable us to create a logically isolated virtual network of computer resources. Students learn to set up a VPC using virtualized-compute-servers on AWS. The course also covers the basics of networking while setting up a VPC. Students learn of the architecture and practical aspects of distributed object storage and how it enables low latency and high availability data storage on the cloud

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

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.

This course provides a dive deep into more advanced concepts in server-side

programming using Node.js to enable initiative, real-time and scalable web

applications. We dive into threading and thread pools in Node.js and how they can

be leveraged to build more responsive web apps. We learn socket programming

using socket.io and Node.js for instant messaging, document collaboration, real

time analytics and streaming applications. Students also learn to use Caching using distributed in-memory key-value stores (like Redis) to rescue latency while serving web-apps. Students also learn how to use Node.js with popular NoSQL data stores like MongoDB for storing unstructured data. We also cover GraphQL which is an open source data query and manipulation language for APIs, which is gaining popularity more recently. We learn popular protocols like OAuth to enable cross platform logins. Students also learn the architecture and practical aspects of Web-RTC to enable multimedia applications like video-chat, live-streaming, music-streaming etc.

This course provides a practical and detailed understanding of popular

programming paradigms and data storage types. Students learning this will be able to write and solve programming problems. The course starts from the basics about functions, various built in functions and how to code user defined functions. Then students will learn about various data type storages and learn about lists and how various manipulations can be done lists like list slicing and also go through examples of 2D Lists.

While learning how to create functions students have to learn how various results

and inputs can be stored using different data types after the introduction and

discussion on Lists, students will go through sets, tuples, Dictionaries and Strings.

The student should be well prepared to apply these concepts and build algorithms

and software using what they learnt in this course.

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

Gain a seamless transition into professional roles by refining interview skills and providing structured job search guidance. The program focuses on building career assets, mastering behavioral and technical interviews, and initiating a structured job search process. Progressing through program stages empowers individuals to confidently pursue and secure desired roles in the dynamic tech industry.

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.

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 would also study popular frameworks for front end development like Bootstrap 4, which can speed up UI development significantly.

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.

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.

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.

Embark on a journey into the vibrant world of open source software with our course, "Open Source Ecosystem." Participants will explore foundational principles and community-driven development models through interactive lectures and projects. Gain practical skills in contributing to existing projects and initiating new ones, and gain a deep understanding of open source's impact on technology and innovation.

Dive into theoretical frameworks and practical methodologies independently, tackling complex computational challenges. Engage in hands-on research projects and experimental design to advance your understanding of modern computer science methodologies.

Low-Level Design & Design Patterns focuses on modularity and reusability in

software design, common design vocabularies, refactoring and how to reduce it,

and how to incorporate design patterns into iterative development processes. The course pays significant attention to the interaction between system architecture and components, including data organisation.

The course begins with Object-Oriented Analysis (OOA), which is a problems-solving technique that includes: modelling an information design; representing behaviour; describing functions; dividing data, functional, and behavioural models to uncover detail; moving from abstraction to implementation details. The course then turns to Object-Oriented Design (OOD), which reduces the analysis model into a modular design for software creation, with subsystems, components, and objects.

The iteration of analysis and implementation will be covered in detail with real-

world industry examples..

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.

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.

Engage in hands-on exploration of fundamental algorithms, data structures, and advanced algorithmic techniques. Through real-world projects and collaborative problem-solving, students develop expertise in designing, analyzing, and optimizing algorithms. Get equipped with the practical skills and theoretical understanding to tackle complex computational challenges in modern technology environments.

Advanced Applied Computer Science is a capstone project, an end-to-end

deployable solution to a real-world computational problem that students build in

the last phase of the programme. Its objective is to help students rigorously solve a technically challenging problem where they would apply all of the concepts,

techniques and tools learned in the programme. Students typically pick a problem

from their specialisation after discussing it with the course instructor(s). Students also have the option of working on a real-world problem in their

company/organization/institution. They can be mentored by an expert supervisor

from their organization along with an academic supervisor from Woolf. All external expert-supervisors and projects need to be approved by the instructor(s) to ensure that the project is technically challenging and the solution being built is rigorous and of high quality. Students start with identifying a technically challenging problem. Once approved by the instructor(s), they start the literature survey to read research papers and technical reports of prior related work. Then, they build the system design and write a design document to solve the problem. This would be followed by designing and implementing individual modules and testing them. This would be followed by deploying the solution and making it available to end users while satisfying the problem’s real-world constraints and objectives. Students then document their work into a detailed technical report.

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.

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 would also study popular frameworks for front end development like Bootstrap 4, which can speed up UI development significantly.

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.

This course gives the detailed overview on how to approach Low Level Design problems with real-world case studies discussed such as Designing a Pen (Mac/Windows), TicTacToe, BookMyShow (most used event booking app, manages millions of users), Email campaign Management System and detailed design of Splitwise.

This is a course that focuses both on architectural design and practical hands-on

learning of the most used cloud services. The module extensively uses Amazon

Web services (AWS) to show real world code examples of various cloud services. It

also covers the core concepts and architectures in a platform agnostic manner so

that students can easily translate these learnings to other cloud platforms (like

Azure, GCP etc.). The module starts with virtualization and how virtualized compute instances are created and configured. Students also learn how to auto-scale applications using load balancers and build fault tolerant applications across a geographically distributed cloud. As relational databases are widely used in most enterprises, students learn how to migrate and scale (both vertically and

horizontally) these databases on the cloud while ensuring enterprise grade security. Virtual private clouds enable us to create a logically isolated virtual network of computer resources. Students learn to set up a VPC using virtualized-compute-servers on AWS. The course also covers the basics of networking while setting up a VPC. Students learn of the architecture and practical aspects of distributed object storage and how it enables low latency and high availability data storage on the cloud

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.

This course provides a dive deep into more advanced concepts in server-side

programming using Node.js to enable initiative, real-time and scalable web

applications. We dive into threading and thread pools in Node.js and how they can

be leveraged to build more responsive web apps. We learn socket programming

using socket.io and Node.js for instant messaging, document collaboration, real

time analytics and streaming applications. Students also learn to use Caching using distributed in-memory key-value stores (like Redis) to rescue latency while serving web-apps. Students also learn how to use Node.js with popular NoSQL data stores like MongoDB for storing unstructured data. We also cover GraphQL which is an open source data query and manipulation language for APIs, which is gaining popularity more recently. We learn popular protocols like OAuth to enable cross platform logins. Students also learn the architecture and practical aspects of Web-RTC to enable multimedia applications like video-chat, live-streaming, music-streaming etc.

Engage in hands-on exploration of fundamental algorithms, data structures, and advanced algorithmic techniques. Through real-world projects and collaborative problem-solving, students develop expertise in designing, analyzing, and optimizing algorithms. Get equipped with the practical skills and theoretical understanding to tackle complex computational challenges in modern technology environments.

Dive into theoretical frameworks and practical methodologies independently, tackling complex computational challenges. Engage in hands-on research projects and experimental design to advance your understanding of modern computer science methodologies.

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.

Embark on a journey into the vibrant world of open source software with our course, "Open Source Ecosystem." Participants will explore foundational principles and community-driven development models through interactive lectures and projects. Gain practical skills in contributing to existing projects and initiating new ones, and gain a deep understanding of open source's impact on technology and innovation.

Gain a seamless transition into professional roles by refining interview skills and providing structured job search guidance. The program focuses on building career assets, mastering behavioral and technical interviews, and initiating a structured job search process. Progressing through program stages empowers individuals to confidently pursue and secure desired roles in the dynamic tech industry.

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.

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.

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.

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

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

Engage in hands-on exploration of fundamental algorithms, data structures, and advanced algorithmic techniques. Through real-world projects and collaborative problem-solving, students develop expertise in designing, analyzing, and optimizing algorithms. Get equipped with the practical skills and theoretical understanding to tackle complex computational challenges in modern technology environments.

Dive into theoretical frameworks and practical methodologies independently, tackling complex computational challenges. Engage in hands-on research projects and experimental design to advance your understanding of modern computer science methodologies.

Embark on a journey into the vibrant world of open source software with our course, "Open Source Ecosystem." Participants will explore foundational principles and community-driven development models through interactive lectures and projects. Gain practical skills in contributing to existing projects and initiating new ones, and gain a deep understanding of open source's impact on technology and innovation.

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.

This course provides a practical and detailed understanding of popular

programming paradigms and data storage types. Students learning this will be able to write and solve programming problems. The course starts from the basics about functions, various built in functions and how to code user defined functions. Then students will learn about various data type storages and learn about lists and how various manipulations can be done lists like list slicing and also go through examples of 2D Lists.

While learning how to create functions students have to learn how various results

and inputs can be stored using different data types after the introduction and

discussion on Lists, students will go through sets, tuples, Dictionaries and Strings.

The student should be well prepared to apply these concepts and build algorithms

and software using what they learnt in this course.

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.

Engage in hands-on exploration of fundamental algorithms, data structures, and advanced algorithmic techniques. Through real-world projects and collaborative problem-solving, students develop expertise in designing, analyzing, and optimizing algorithms. Get equipped with the practical skills and theoretical understanding to tackle complex computational challenges in modern technology environments.

Low-Level Design & Design Patterns focuses on modularity and reusability in

software design, common design vocabularies, refactoring and how to reduce it,

and how to incorporate design patterns into iterative development processes. The course pays significant attention to the interaction between system architecture and components, including data organisation.

The course begins with Object-Oriented Analysis (OOA), which is a problems-solving technique that includes: modelling an information design; representing behaviour; describing functions; dividing data, functional, and behavioural models to uncover detail; moving from abstraction to implementation details. The course then turns to Object-Oriented Design (OOD), which reduces the analysis model into a modular design for software creation, with subsystems, components, and objects.

The iteration of analysis and implementation will be covered in detail with real-

world industry examples..

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.

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

This course gives the detailed overview on how to approach Low Level Design problems with real-world case studies discussed such as Designing a Pen (Mac/Windows), TicTacToe, BookMyShow (most used event booking app, manages millions of users), Email campaign Management System and detailed design of Splitwise.

Dive into theoretical frameworks and practical methodologies independently, tackling complex computational challenges. Engage in hands-on research projects and experimental design to advance your understanding of modern computer science methodologies.

Embark on a journey into the vibrant world of open source software with our course, "Open Source Ecosystem." Participants will explore foundational principles and community-driven development models through interactive lectures and projects. Gain practical skills in contributing to existing projects and initiating new ones, and gain a deep understanding of open source's impact on technology and innovation.

Gain a seamless transition into professional roles by refining interview skills and providing structured job search guidance. The program focuses on building career assets, mastering behavioral and technical interviews, and initiating a structured job search process. Progressing through program stages empowers individuals to confidently pursue and secure desired roles in the dynamic tech industry.