Postgraduate Certificate in Computer Science: Artificial Intelligence & Machine Learning

Fully Online
6 months
750 hours | 30 ECTS
Certificate
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.

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Supporting your global mobility

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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

Course Structure

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
  • Develop a critical understanding of problem-solving strategies in computing.
  • Critically evaluate diverse scholarly views on the appropriateness of various problem-solving strategies.
  • Critically assess the relevance of theories of problem-solving for business applications in the domain of software development
  • Acquire knowledge of various methods for structuring data in arrays.
  • Develop a specialised knowledge of key strategies related to structuring data.
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
  • Apply an in-depth domain-specific knowledge and understanding to problem solving.
  • Autonomously gather material and organise it into a coherent presentation or essay.
Competencies
  • Create synthetic contextualised discussions of key issues related to problem-solving, and moving from algorithmic to heuristic problem-solving strategies.
  • Act autonomously in identifying research problems and solutions related to arrays and their real-world applications.
  • Solve problems and be prepared to take leadership decisions related to applying problem-solving heuristics.
  • 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 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 optimised 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’.

Teachers

Soumya Ranjan Mishra
Soumya Ranjan Mishra
Chirag Beniwal
Chirag Beniwal
Ravi Kumar Gupta
Ravi Kumar Gupta
Alok Anand
Alok Anand

Intended learning outcomes

Knowledge
  • Develop a critical understanding of a modern programming language such as Java or Python.
  • Acquire knowledge of various methods for structuring data.
  • Critically assess the relevance of theories for business applications in the domain of technology.
  • Critically evaluate diverse scholarly views on computational complexity.
  • Develop a specialised knowledge of key strategies related to Object-Oriented Programming.
Skills
  • 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.
  • 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.
Competencies
  • Act autonomously in identifying research problems and solutions related to Object-Oriented programming
  • Create synthetic contextualised discussions of key issues related to converting scientific knowledge into programming concepts, and how to instantiate these using Object-Oriented methods
  • Apply a professional and scholarly approach to research problems pertaining to computational complexity
  • Efficiently manage interdisciplinary issues that arise in connection to data structured in 1- and 2-dimensional arrays
  • Demonstrate self-direction in research and originality in solutions developed for modern programming languages
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of computer programming
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 toSQL’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 open source MySQL database and cloud-hosted relational databases (like Amazon RDS) to help students apply the concepts learned on real databases via assignments.

Teachers

Soumya Ranjan Mishra
Soumya Ranjan Mishra
Chirag Beniwal
Chirag Beniwal
Alok Anand
Alok Anand

Intended learning outcomes

Knowledge
  • 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.
  • Critically assess the relevance of theories for business applications in the domain of technology.
  • Develop a critical knowledge of relational databases.
Skills
  • Autonomously gather material and organise it into a coherent presentation or essay.
  • Apply an in-depth domain-specific knowledge and understanding to Relational Databases.
  • 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.
Competencies
  • 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.
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Relational Databases.
  • Create synthetic contextualised discussions of key issues related to Relational Databases.
  • 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.
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

Soumya Ranjan Mishra
Soumya Ranjan Mishra
Chirag Beniwal
Chirag Beniwal
Alok Anand
Alok Anand

Intended learning outcomes

Knowledge
  • Develop a critical knowledge of Numerical programming in Python.
  • Develop a specialised knowledge of key strategies related to Numerical programming in Python.
  • Acquire knowledge of core Mathematical libraries like NumPy, Scipy and Pandas.
  • Critically evaluate diverse scholarly views on Numerical programming in Python.
  • Critically assess the relevance of theories for business applications in the domain of technology.
Skills
  • Apply an in-depth domain-specific knowledge and understanding to numerical programming in Python.
  • Autonomously gather material and organize it into a coherent problem sets or presentation.
  • 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
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Numerical programming in Python.
  • Efficiently manage interdisciplinary issues that arise in connection to Numerical programming in Python.
  • Act autonomously in identifying research problems and solutions related to Numerical programming in Python.
  • Demonstrate self-direction in research and originality in solutions developed for Numerical programming in Python.
  • Apply a professional and scholarly approach to research problems pertaining to Numerical programming in Python.
  • Create synthetic contextualised discussions of key issues related to Numerical programming in Python.
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 usingvector 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

Soumya Ranjan Mishra
Soumya Ranjan Mishra
Chirag Beniwal
Chirag Beniwal
Ravi Kumar Gupta
Ravi Kumar Gupta
Alok Anand
Alok Anand

Intended learning outcomes

Knowledge
  • Develop a specialised knowledge of key strategies related to machine learning
  • Acquire knowledge of bias and variance trade-off, and various techniques to avoid overfitting and underfitting
  • Critically evaluate diverse scholarly views on machine learning
  • Develop a critical knowledge of machine learning
  • Critically assess the relevance of theories for business applications in the domain of technology
Skills
  • Apply an in-depth domain-specific knowledge and understanding to machine learning solutions
  • 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
  • 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 machine learning
  • Efficiently manage interdisciplinary issues that arise in connection to machine learning
  • Create synthetic contextualised discussions of key issues related to machine learning
  • Apply a professional and scholarly approach to research problems pertaining to machine learning
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of machine learning
  • Act autonomously in identifying research problems and solutions related to machine learning
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

Soumya Ranjan Mishra
Soumya Ranjan Mishra
Chirag Beniwal
Chirag Beniwal
Ravi Kumar Gupta
Ravi Kumar Gupta
Alok Anand
Alok Anand

Intended learning outcomes

Knowledge
  • Develop a critical knowledge of Applied Statistics
  • Develop a specialised knowledge of key strategies related to Applied Statistics
  • Acquire knowledge of popular discrete and continuous distributions (like Bernoulli, Binomial, Poisson, Gaussian, Exponential, Pareto, and log-normal)
  • Critically assess the relevance of theories for business applications in the domain of technology
  • Critically evaluate diverse scholarly views on Applied Statistics
Skills
  • 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
  • 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
Competencies
  • Apply a professional and scholarly approach to research problems pertaining to probability theory to perform mathematically rigorous data analysis
  • Efficiently manage interdisciplinary issues that arise in connection to Applied Statistics
  • Demonstrate self-direction in research and originality in solutions developed for Applied Statistics
  • Act autonomously in identifying research problems and solutions related to Applied Statistics
  • Create synthetic contextualised discussions of key issues related to Applied Statistics
  • Solve problems and be prepared to take leadership decisions related to the methods and principles of Applied Statistics

Entry Requirements

Tuition Cost
1,00,000 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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