Traditionally, Computer Science involved the study of computers and computational systems and dealt mostly with software and software systems, including their theory, design, development and application. However, over the years as the field of Computer Science matured and expanded, it has helped to solve hard problems in many areas including mathematics, physics, chemistry, biology, linguistics, economics, business, and the arts. Therefore, the problems that computer scientists encounter range from the abstract–determining what problems can be solved with computers and the complexity of the algorithms that solve them–to the tangible–designing applications that perform well on handheld devices, that are easy to use, and that uphold security measures.
The major in Computer Science offers both breadth and depth of knowledge in computing. Students undergo rigorous coursework coupled with exposure to a rich set of applications and tools through project-based courses, course projects, and the Undergraduate Capstone project and Summer Internships. The core courses under this Major provide a solid foundation to students in the field of Computer Science. In addition to core courses, students may pursue electives in one or more specialisation areas to gain a deeper understanding of these areas. The specialisation areas that we plan to offer are: artificial intelligence and machine learning, bio-computing, data science and its applications, quantum computing, systems and theoretical computer science.
The major in Computer Science seeks to provide students:
On completing this major, the student will be able to:
Offered by | School of Arts and Sciences |
Programmes | Bachelor of Science (Honours) |
Degree | Bachelor of Science (Honours) |
Minimum Programme Credits | 120 |
Minimum Major Credits | 60 |
Democracy and Justice
Environment and Climate Change
Neighbourhoods
Water
The studios deliver interdisciplinary learning around six domains:
Data Science, Communication, Behaviour, Constitution & Civilisation, Materials, and Biology & Life.
Humanities & Languages GER |
Social Sciences GER |
Biological and Life Sciences GER: Introduction to Bioinformatics OR Introductory Biology |
Performance & Visual Arts GER |
GER Elective I: Communication I |
GER Elective II: Communication II |
GER Elective III: Introductory Calculus |
GER Elective IV: Any course at the university outside the major |
GER Elective V: Any course at the university outside the major |
Major Requirements | Credits |
---|---|
Major Core | 33 |
Fundamentals of Computer Programming | |
Programming Lab | |
Discrete Mathematics | |
Design and Analysis of Data Structures and Algorithms | |
Computer Organisation | |
Computer Organisation Lab | |
Operating Systems | |
Theory of Computing | |
Probability and Stochastic Processes | |
Applied Linear Algebra | |
Introduction to Artificial Intelligence | |
Computer Networks | |
Internship | Required |
Undergraduate Thesis or Capstone Project | 6 |
Major Electives | 21 |
Free Electives provide flexibility to students to customise their education at the University.
All students will complete 30 hours of engagement with society to develop a sense of engagement, concern, build problem solving skills, and understand the role of an engaged member of a society. This will be done through a mandatory course, Engagement with Society, that would be a graduation requirement. This course can be taken anytime during the stay at the University but it is advised that the student engage with the courses during the first two years at the University. The 30 hours of volunteer work may be completed during one semester or during the Winter or Summer Break.
Biocomputing
Introduction to Bioinformatics
Computational Structural Biology
Theoretical Computer Science
Advanced Data Structures and Algorithms
Algorithms and Optimization for Big Data
Combinatorial Optimization
Information and Coding Theory
Models of Computation
Systems
Cloud Computing
Database Management Systems
High Performance Computing
Production and Operations Management
Software Engineering
Supply Chain and Logistics Management
Artificial Intelligence
Introduction to Machine Learning
Natural Language Processing
Computer Vision
Robotics
Data Science
Advanced Statistics
Big Data Analytics
Data Analytics and Visualisation
Introduction to Machine Learning