Master of Science in Computer Science

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.

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.

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 delves into current and emerging topics in cybersecurity, providing students with an understanding of the latest challenges and innovations in the field. Focus areas include Internet of Things (IoT) security, cloud security, and advanced threat detection techniques. The course is designed to keep students at the forefront of cybersecurity, equipping them with the knowledge and skills to address evolving threats and secure modern computing environments.

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 provides an in-depth exploration of methods for assessing and managing cybersecurity risks within organizations. Students will learn to identify potential threats, evaluate their impact, and develop strategies to mitigate risks while ensuring business continuity. The course covers risk assessment frameworks, compliance requirements, and the role of cybersecurity in corporate governance. Emphasis is placed on applying theoretical concepts to real-world scenarios, enabling students to effectively manage cybersecurity risks in various organizational settings.

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 offers students hands-on experience with penetration testing and ethical hacking techniques. It covers various tools and methodologies used to identify and exploit vulnerabilities in computer systems and networks. Students will learn to think like an attacker to better protect organizational assets. The course emphasizes ethical considerations, legal implications, and the importance of responsible disclosure in the context of cybersecurity.

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 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 geometry of 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.

This course explores the essential strategies and best practices for responding to and recovering from cybersecurity incidents. Students will learn how to develop and implement incident response plans, manage breaches effectively, and ensure rapid recovery to minimize damage. The course emphasizes the importance of preparation, quick response, and systematic recovery to protect organizational assets and maintain business continuity in the face of cybersecurity threats.

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.

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