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. 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/
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.
Key Intended Learning Outcomes:
Assess, analyze, and criticize the various strategies for storing data in a computer program
Compare and evaluate the different methodologies recommended in scholarly sources about solving problems with 2D lists
Propose appropriate solutions to complex and changing problems of data storage, programming functions, and algorithms
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.
Mathematics and computer science are closely related fields. Problems in computer science are often formalized and solved with mathematical methods. It is likely that many important problems currently facing computer scientists will be solved by researchers skilled in algebra, analysis, combinatorics, logic and/or probability theory, as well as computer science.
This course covers discrete mathematics for computer science and engineering. Topics may include asymptotic notation and growth of functions; permutations and combinations; counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions.
Students will be able to explain and apply the basic methods of discrete (noncontinuous) mathematics in computer science. They will be able to use these methods in subsequent courses in the design and analysis of algorithms, computability theory, software engineering, and computer systems. The focus of the course is real-world problems and applications often found in business and industry.
Besides, students will learn 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).
Key Intended Learning Outcomes:
Assess, analyse, and criticise the various strategies for evaluating algorithmic cost arising in the context of computational problem-solving and handling matters arising in the context of structured data
Compare and evaluate the different methodologies recommended in scholarly sources pertaining to how managers should handle evaluating algorithmic performance and solving problems with structured data
Propose appropriate solutions to complex and changing problems pertaining to problem-solving in software development
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 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 open source MySQL database and cloud-hosted relational databases (like Amazon RDS) to help students apply the concepts learned on real databases via assignments.
Key Intended Learning Outcomes:
Assess, analyse, and criticise the various strategies for handling matters arising in the context of Relational Databases
Compare and evaluate the different methodologies recommended in scholarly sources pertaining to how managers should handle Relational Databases
Propose appropriate solutions to complex and changing problems pertaining to Relational Databases
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.
Data is the fuel driving all major organisations. This course helps you understand how to process data at scale. From understanding the fundamentals of distributed processing to designing data warehousing and writing ETL (Extract Transform Load) pipelines to process batch and streaming data. Students will learn a comprehensive view of the complete Data Engineering lifecycle.
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 valuabe for all students specializing in mathematical sub-areas of CS like ML, Data Science, Scientific Computing etc.
Key Intended Learning Outcomes:
Assess, analyze, and criticize the various strategies for handling matters arising in the context of numerical programming in Python
Compare and evaluate the different methodologies recommended in scholarly sources pertaining to how managers should handle numerical programming in Python
Propose appropriate solutions to complex and changing problems pertaining to numerical programming in Python
This course is aimed to build a strong foundational knowledge of Data Analytics tools used extensively in the Data Science field. There now are powerful data visualisation tools used in the business analytics industry to process and visualise raw business data in a very presentable and understandable format. A good example is Tableau, used by all data analytics departments of companies and in data analytics companies in various fields for its ease of use and efficiency. Tableau uses relational databases, Online Analytical Processing Cubes, Spreadsheets, cloud databases to generate graphical type visualisations. Course starts with visualisations and moves to an in-depth look at the different chart and graph functions, calculations, mapping and other functionality. Students will be taught quick table calculations, reference lines, different types of visualisations, bands and distributions, parameters, motion chart, trends and forecasting, formatting, stories, performance recording and advanced mapping.
This course focuses on modelling sequences (text, music, time-series, genes) using deep-learning models. We start with a simple Recurrent Neural Network and its limitations with long-sequences. Students learn LSTMs and GRUs which can handle significantly longer sequences to model sequence data like text, music, gene-sequences and time-series data. We study variations of LSTM like bi-directional LSTMs and encoder-decoder architectures. This is followed by a detailed study of attention mechanism and Transformer based models which are currently the state-of-the-art for NLP and sequence modelling. The module teaches encoder-decoder Transformers, BERT, BERT-variations, GPT-1,2 &3 models from both the architectural and mathematical viewpoints and also a practical viewpoint. Studnets learn to implement many of these complex models from scratch (using TensorFlow 2 and Keras) to gain a deeper understanding of how they work internally. Students will study popular applications of deep-learning in NLP like parts-of-speech tagging, question-answering systems, conversational engines (chatbots), Semantic search with low-latency etc. For each of these problems, Students will study cutting edge deep-learning models along with code implementations.
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, Naive 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.
This course teaches students how to analyse the ways users engage with a service. This method, called product analytics, helps businesses track and analyse user data. Students will learn more deeply what is required to move a product from idea to implementation, through to launch, and then on to iterative improvements. The course teaches how to measure progress, validate or update product hypotheses, and present product learnings.
Also, students will gain experience in making informed decisions, as well as how to present findings and make an analytics-informed business case to win support for a product.
This is a course that focuses both on architectural design and practical hands-on learning of advanced cloud-based services. We begin with the serverless computing model and how it is achieved by most cloud providers. We learn to use it for building web applications, data and file processing and analytics applications. We then learn of the architecture of distributed messaging queues and how they can be used for plumbing complex cloud systems with many components and services. Monitoring the resources in your cloud setup is a key to ensure low costs and high availability and the smooth functioning of your overall setup. We learn to use AWS CloudWatch to track various key metrics, trigger alarms, detect anomalous behaviour and act upon them in near real-time. We learn the architecture and design of load balancers and how they play a key role in most horizontally scalable web-applications. Students also learn of the architecture and design of Content delivery networks (CDNs) from Akamai and Amazon. We learn how CDNs can be used to deliver live streaming and website content fast using globally distributed servers and caching. Most of this course involves learning the internal architecture of various cloud systems and using them to solve real world engineering problems.
Every organization is building products to solve the pain points of its customers. Product managers are a critical part of an organization, 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.
Key Intended Learning Outcomes:
Assess, analyze, and criticize the various strategies for improving a product after launch
Compare and evaluate the different methodologies recommended in scholarly sources pertaining to measuring user engagement
Propose appropriate solutions to complex and changing problems of product success or failure in real-world engineering and science contexts
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.
Key Intended Learning Outcomes:
Develop and demonstrate effective communication skills.
Collaborate effectively in a team environment.
Develop and demonstrate leadership skills.
Build and maintain professional relationships.
Develop and execute a career plan.
This course aims to build the core competency of building real world end-to-end ML systems and deploy them into production for a variety of problems and scenarios. Students would learn a variety of ML systems ranging from high throughput and low latency internet scale systems to low compute power and energy constrained IoT devices like smart watches. Students will study the ML lifecycle and various components in detail. We also use real world ML platforms like Google’s KubeFlow, TensorFlow Lite, and Amazon’s SageMaker to implement real world systems and understand the engineering trade-offs and challenges. Students also learn relevant technologies and tools like Containerization (Docker) and Container Orchestration (Kubernetes) and Git which are often used extensively in real world scalable ML systems. This course is a hands-on course where we solve multiple real world cases and discuss solutions built by various companies and organizations to provide the students a comprehensive understanding of varied systems and design choices.
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.
