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. 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 Computing, Software Engineering, or Data Science, 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
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, enrolment 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 complete all courses in module two, earning a total of 45 ECTS. Tier Three will be completed by a) completing 20 ECTS of compulsory modules and b)completing a 10 ECTS Applied Computer Science capstone project.
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
Cohort 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/
375 hours | 15 ECTS
125 hours | 5 ECTS
Introduction to Problem-Solving Techniques: Part 1
125 hours | 5 ECTS
Relational Databases
125 hours | 5 ECTS
Introduction to Computer Programming: Part 1
1625 hours | 65 ECTS
125 hours | 5 ECTS
User Interface Design: Part 2
125 hours | 5 ECTS
User Interface Design: Part 1
125 hours | 5 ECTS
UX Psychology
125 hours | 5 ECTS
Additional Studies in Software Testing
125 hours | 5 ECTS
System Design
125 hours | 5 ECTS
Introduction to Computer Programming: Part 2
125 hours | 5 ECTS
Applied Statistics
125 hours | 5 ECTS
Foundations of Cloud Computing
125 hours | 5 ECTS
Introduction to Deep Learning
125 hours | 5 ECTS
Introduction to Machine Learning
125 hours | 5 ECTS
Numerical Programming in Python
125 hours | 5 ECTS
Front End UI/UX Development
125 hours | 5 ECTS
Spreadsheets for Data Understanding
125 hours | 5 ECTS
Back End Development
125 hours | 5 ECTS
Front End Development
125 hours | 5 ECTS
DevOps
125 hours | 5 ECTS
JavaScript
125 hours | 5 ECTS
Practical Software Engineering
125 hours | 5 ECTS
Data Visualisation Tools
125 hours | 5 ECTS
Gamification
125 hours | 5 ECTS
Data Privacy and Ethics in Technology
125 hours | 5 ECTS
Project Management
125 hours | 5 ECTS
Data Engineering
125 hours | 5 ECTS
Product Analytics
250 hours | 10 ECTS
250 hours | 10 ECTS
Applied Computer Science Project
1125 hours | 45 ECTS
125 hours | 5 ECTS
Introduction to Computer Programming: Part 2
125 hours | 5 ECTS
Foundations of Cloud Computing
125 hours | 5 ECTS
Introduction to Machine Learning
125 hours | 5 ECTS
Front End UI/UX Development
125 hours | 5 ECTS
Back End Development
125 hours | 5 ECTS
Front End Development
125 hours | 5 ECTS
DevOps
125 hours | 5 ECTS
JavaScript
125 hours | 5 ECTS
Practical Software Engineering
1125 hours | 45 ECTS
125 hours | 5 ECTS
User Interface Design: Part 2
125 hours | 5 ECTS
User Interface Design: Part 1
125 hours | 5 ECTS
UX Psychology
125 hours | 5 ECTS
System Design
125 hours | 5 ECTS
Front End UI/UX Development
125 hours | 5 ECTS
Spreadsheets for Data Understanding
125 hours | 5 ECTS
Front End Development
125 hours | 5 ECTS
JavaScript
125 hours | 5 ECTS
Practical Software Engineering
1125 hours | 45 ECTS
125 hours | 5 ECTS
Numerical Programming in Python
125 hours | 5 ECTS
Data Visualisation Tools
125 hours | 5 ECTS
Spreadsheets for Data Understanding
125 hours | 5 ECTS
Practical Software Engineering
125 hours | 5 ECTS
Applied Statistics
125 hours | 5 ECTS
Foundations of Cloud Computing
125 hours | 5 ECTS
Introduction to Deep Learning
125 hours | 5 ECTS
Introduction to Machine Learning
125 hours | 5 ECTS
Introduction to Computer Programming: Part 1
1125 hours | 45 ECTS
125 hours | 5 ECTS
Front End Development
125 hours | 5 ECTS
DevOps
125 hours | 5 ECTS
JavaScript
125 hours | 5 ECTS
Practical Software Engineering
125 hours | 5 ECTS
Additional Studies in Software Testing
125 hours | 5 ECTS
Introduction to Computer Programming: Part 2
125 hours | 5 ECTS
Foundations of Cloud Computing
125 hours | 5 ECTS
Spreadsheets for Data Understanding
125 hours | 5 ECTS
Back End Development