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. Most of this program is the 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 problem 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 and Full Stack Development, etc.
Target Audience
- Ages 19-30, 31-65, 65+
Target Group
This course is designed for individuals who wish to enhance their knowledge of computer science and its various applications used in different fields of employment. It is designed for those that will have responsibility for planning, organizing, and directing technological operations. In all cases, the target group should be prepared to pursue substantial academic studies. Students must qualify for the course of study by entrance application. A prior computer science degree is not required; however the course does assume technical aptitude; and it targets students with finance, engineering, or STEM training or professional experience.
Mode of attendance
Online/Blended Learning
Structure of the programme - Please note that this structure may be subject to change based on faculty expertise and evolving academic best practices. This flexibility ensures we can provide the most up-to-date and effective learning experience for our students.The Master of Science in Computer Science combines asynchronous components (lecture videos, readings, and assignments) and synchronous meetings attended by students and a teacher during a video call. Asynchronous components support the schedule of students from diverse work-life situations, and synchronous meetings provide accountability and motivation for students. Students have direct access to their teacher and their peers at all times through the use of direct message and group chat; teachers are also able to initiate voice and video calls with students outside the regularly scheduled synchronous sessions. Modules are offered continuously on a publicly advertised schedule consisting of cohort sequences designed to accommodate adult students at different paces. Although there are few formal prerequisites identified throughout the programme, enrollment in courses depends on advisement from Woolf faculty and staff.The degree has 3 tiers: The first tier is required for all students, who must take 15 ECTS. In the second tier, students must select 45 ECTS from elective tiers. Under the guidance of the Academic Staff at Woolf, students may either select exclusively from one specialization track (in which case they will earn that specialization), or they may mix tracks (in which case they will finish without a specialization). Tier Three may be completed in two different ways: a) by completing a 30ECTS Advanced Applied Computer Science capstone project, or b) by completing a 10 ECTS Applied Computer Science project and 20 ECTS of electives from the program.
Grading System
Scale: 0-100 points
Components: 60% of the mark derives from the average of the assignments, and 40% of the mark derives from the cumulative examination
Passing requirement: minimum of 60% overall
Dates of Next Intake
Rolling admission
Pass rates
2023 pass rates will be publicised in the next cycle, contingent upon ensuring sufficient student data for anonymization.
Identity Malta’s VISA requirement for third country nationals: https://www.identitymalta.com/unit/central-visa-unit/
Passing requirement: minimum of 60% overall
Dates of Next Intake
Rolling admission
Pass rates
2023 pass rates will be publicised in the next cycle, contingent upon ensuring sufficient student data for anonymization. Identity Malta’s VISA requirement for third country nationals: https://www.identitymalta.com/unit/central-visa-unit/
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 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 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.
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.
Every organisation is building products to solve the pain points of its customers. Product managers are a critical part of an organisation, who make sure that evolving customer needs, and market trends are observed and converted into delightful solutions which help businesses get its outcomes.
In this course, students will get a fundamental understanding of product management practices.
This will give them a comprehensive view of the complete product management life cycle.
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.
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 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.
In this module we will discuss general approaches to the construction of efficient solutions to problems. Such methods are of interest because:
They provide templates suited to solving a broad range of diverse problems.
They can be translated into common control and data structures provided by most high-level languages.
The temporal and spatial requirements of the algorithms which result can be precisely analyzed. This course will provide a solid foundation and background to design and analysis of algorithms. In particular, upon successful completion of this course, students will be able to understand, explain and apply key algorithmic concepts and principles, which might include:
Greedy algorithms (Activity Selection, 0-1 Knapsack Problem, Fractional Knapsack Problem)
Dynamic programming (Longest Common Subsequence, 0-1 Knapsack Problem)
Minimum Spanning Trees (Prim’s Algorithm, Kruskal’s Algorithm)
Graph Algorithms (Dijkstra’s Shortest Path Algorithm, Bipartite Graphs, Minimum Vertex Cover) Although more than one technique may be applicable to a specific problem, it is often the case that an algorithm constructed by one approach is clearly superior to equivalent solutions built using alternative techniques. This module will help students assess these choices.
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 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 light-weight UI for web, mobile and desktop applications using HTML5, CSS and Frameworks like Bootstrap 4. This course starts with an introduction on 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 on 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 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 Fetch 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.
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 comprehensive overview and practical knowledge of various NoSQL data stores and how they can be used on the Cloud (AWS). We focus on three NoSQL datastores in this course: MongoDB, DynamoDB and Redis. For each of them, we first understand the design and architecture in depth. We compare and contrast each of them with traditional relational databases and other NoSQL databases so that students understand the engineering trade-offs when using them. We take multiple real-world case-studies from various companies and organizations to discuss which datastore is more apt in each situation to help students better appreciate the differences and use-cases. We dive into the technical details from setting up and deploying each of these datastores on the cloud (AWS) with latency and scalability in mind. We also discuss various datastore specific optimizations and good practices to follow. The module also teaches students how to stress test each of these datastores under differing loads to compare and contrast which would be a better fit in a real world scenario. At the end of this course, students would be able to choose an optimal data store for their engineering needs to build websites or build data pipelines or deploy machine learning applications.
This course provides a practical understanding of popular object-oriented design patterns so that students can reuse design strategies developed for commonly occurring problems in software development. We begin the course with a revision of object-oriented programming and an overview of UML (unified modelling language) diagrams to represent software design diagrammatically. We then dive into 10-12 most popular design patterns motivating each of them from real world scenarios. We would also showcase multiple opensource code bases which use the specific design pattern to solve a real-world design problem. This would help students gain an appreciation of how each of the theoretical patterns they learn actually translate to code. We also take up real world cases and dive into various design patterns that can be used to solve the problem. Sometimes, there could be multiple valid designs. We would five into the pros and cons of each design decision and trade-offs involved. Our objective is to build the problem-solving ability amongst students to recognize the appropriate design pattern to tackle a real-world problem. The module briefly discusses domain specific design patterns in their respective contexts.
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.
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.
This course is designed to equip IT professionals with the soft skills and career strategies required for success in the technology industry. The course is project-based and covers a range of topics such as communication skills, teamwork, time management, leadership, networking, and career development. The course covers the entire lifecycle of a technology project, from requirement gathering to delivery and maintenance. Students will learn how to communicate effectively with stakeholders, manage their time efficiently, lead a team, and collaborate effectively in a team environment. The course also covers aspects of career development, such as networking and building professional relationships, creating a personal brand, and developing a career plan. Students will learn how to identify their strengths and weaknesses, and how to leverage their skills and experience to advance their careers in the technology industry. Throughout the course, students will engage in practical exercises, case studies,
and interactive discussions to enhance their soft skills and career strategies. By the
end of the course, students will have developed a strong foundation in essential
soft skills, self-management, effective communication, leadership, ethical conduct,
adaptability, and job search strategies necessary for successful IT careers.
Every organisation is building products to solve the pain points of its customers. Product managers are a critical part of an organisation, who make sure that evolving customer needs, and market trends are observed and converted into delightful solutions which help businesses get its outcomes.
In this course, students will get a fundamental understanding of product management practices.
This will give them a comprehensive view of the complete product management life cycle.
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.
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 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.
In this module we will discuss general approaches to the construction of efficient solutions to problems. Such methods are of interest because:
They provide templates suited to solving a broad range of diverse problems.
They can be translated into common control and data structures provided by most high-level languages.
The temporal and spatial requirements of the algorithms which result can be precisely analyzed. This course will provide a solid foundation and background to design and analysis of algorithms. In particular, upon successful completion of this course, students will be able to understand, explain and apply key algorithmic concepts and principles, which might include:
Greedy algorithms (Activity Selection, 0-1 Knapsack Problem, Fractional Knapsack Problem)
Dynamic programming (Longest Common Subsequence, 0-1 Knapsack Problem)
Minimum Spanning Trees (Prim’s Algorithm, Kruskal’s Algorithm)
Graph Algorithms (Dijkstra’s Shortest Path Algorithm, Bipartite Graphs, Minimum Vertex Cover) Although more than one technique may be applicable to a specific problem, it is often the case that an algorithm constructed by one approach is clearly superior to equivalent solutions built using alternative techniques. This module will help students assess these choices.
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 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.