Computer Science and Finance interact in multiple ways. Topics in computer science, such as numerical methods and database management systems, are essential to econometricians and financial engineers who like to independently handle increasingly large and complex datasets. On the other hand, topics such as game theory have found numerous applications in edge computing, sensor networks, and social network analysis. More recently, Machine Learning and Artificial Intelligence have become indispensable tools in Finance and are being increasingly applied in empirical methods. The ability to develop sophisticated mathematical models and algorithms and write programmes to analyse big data is a unique combination coveted in both business and academia.
The BxMx Programme is aimed at creating flexible employment opportunities for students at the graduate level of education. The BS in Computer Science + MS in Quantitative Finance combines the strengths of the School of Arts and Sciences and Amrut Mody School of Management to provide students with these critical mathematical and computational skills complemented with Economics and Finance. It allows seamless movement between the two Schools and Programmes and provides students an opportunity to earn the two degrees in five years instead of six. The Programme will prepare them for doctoral studies or careers in Economics, Finance, government, and business.
Programme Outcomes
| Offered by | School of Arts and Sciences and Amrut Mody School of Management |
| Programme | The BxMx Programme (Dual Degree) |
| Degree | Bachelor of Science in Computer Science and Master of Science in Quantitative Finance |
| Minimum Programme Credits | 204 |
| Minimum Major Credits | |
| Credits | |
|---|---|
| Foundation Programme (Three Studios) | 9 |
| First Year Seminar on Critical Thinking and Writing | 3 |
| Credits | |
|---|---|
| Humanities and Languages GER | 3 |
| Social Sciences GER | 3 |
| Biological and Life Sciences GER | 3 |
| Mathematical and Physical Sciences GER CSD101 Fundamentals of Data Science |
3 |
| Performing and Visual Arts GER | 3 |
| GER Elective 1: MAT266 Introduction to Numerical Analysis | 3 |
| GER Elective 2 | 3 |
| GER Elective 3 | 3 |
| Communications GER COM101 Effective Reading and Comprehension Skills |
3 |
| Sports & Wellness GER | 3 |
| Major Requirements | Credits |
|---|---|
| Major Core | 48 |
| MAT281 Multivariable Calculus | 4 |
| CSE100 Fundamentals of Computer Programming | 4 |
| MAT101 Discrete Mathematics | 3 |
| CSC210 Data structures and Algorithms | 4 |
| CSC201 Computer Organisation | 4 |
| CSE340 Operating Systems | 3 |
| STA100 Probability | 4 |
| STA202 Mathematical Statistics | 4 |
| CSE250 Database Management Systems | 3 |
| MAT248 Applied Linear Algebra | 3 |
| CSE525 Theory of Computing | 3 |
| CSE330 Computer Networks | 3 |
| CSE518 Artificial Intelligence (Proposed New courses) | 3 |
| CSE523 Machine Learning (Proposed New courses) | 3 |
| Major Electives | 26 |
| BIO213 Basic Bioinformatics | 3 |
| COM121 Formal Logic | 3 |
| CSC306 Natural Language Processing | 3 |
| CSC314 Sequence Analysis Algorithms | 3 |
| CSC507 Parallel Programming using GPUs | 3 |
| CSC514 Sequence Analysis Algorithms | 3 |
| CSE200 Design and Analysis of Algorithms | 3 |
| CSE300 Software Engineering | 3 |
| CSE511 Algorithms and Optimisation for Big Data | 3 |
| CSE516 Probabilistic Graphical Models | 3 |
| CSE518 Artificial Intelligence | 3 |
| CSE519 Human Computer Interaction | 3 |
| CSE520 Data Analytics and Visualisation | 3 |
| CSE521 Big Data Analytics | 3 |
| CSE524 Parallel and Distributed Systems | 3 |
| CSE526 Advanced Computer Arithmetic: Algorithms and Sub-systems | 3 |
| CSE533 Social Network Analysis | 3 |
| CSE540 Cloud Computing | 3 |
| CSE541 Computer Vision | 3 |
| CSE542 Introduction To Blockchain: Technologies, Approaches and Applications | 3 |
| CSP502 Computer Vision | 3 |
| ECE500 Information and Coding Theory | 3 |
| ECE502 VLSI Design | 3 |
| ECE503 High-Performance Computing | 3 |
| ECE504 / EVD520 Internet of Things | 3 |
| ENR508 Mobile Robots | 3 |
| EVD220 Embedded System Design | 3 |
| EVD520 Internet of Things | 3 |
| MAT396 Numerical Methods | 3 |
| MAT485 Introduction to Quantum Computing | 3 |
| MAT502 Advanced Statistics | 3 |
| MOOC Course - C# Programming for Unity Development Specialization | 3 |
| PHY111 Classical Mechanics-I | 3 |
| STA310 Bayesian Data Analysis | 3 |
| STA330 Population Genetics | 3 |
| STA355 Stochastic Processes | 3 |
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.
| Credits | |
|---|---|
| Programme Core | 25 |
| TOD501 Descriptive and Inferential Statistics | 1.5 |
| TOD601 ANOVA and Regression | 1.5 |
| ECO501 Intermediate Microeconomics | 3 |
| ECO511 Intermediate Macroeconomics | 3 |
| TOD504 Mathematical Methods for Economics | 3 |
| TOD531 Analytics Lab | 1 |
| FAC683 Security Analysis and Portfolio Management | 3 |
| FAC541 Financial Markets and Institutions | 3 |
| ECO520 Econometrics | 3 |
| FAC533 Corporate Investments and Value Creation | 3 |
| Disciplinary Core | 12 |
| FAC631 Derivatives and Risk Management | 3 |
| FAC635 Financial Modelling | 3 |
| FAC638 Fixed Income Securities | 3 |
| FAC639 Modelling Randomness in Financial Markets | 3 |
| Disciplinary Electives | 24 |
| FAC534 Strategic Corporate Finance | 3 |
| FAC630 Behavioural Finance | 3 |
| FAC632 Corporate Restructuring: Mergers & Acquisitions | 3 |
| FAC634 International Finance | 3 |
| FAC637 Business Valuation | 3 |
| FAC636 Financial Econometrics | 3 |
| FAC644 FinTech Ventures | 3 |
| FAC643 Bank Management | 3 |
| FACXXX Venture Capital and Private Equity | 3 |
| FACXXX Algorithmic Trading and Market Microstructure | 3 |
Duration: Five years
Eligibility Criteria
Exit Option: If a student decides to exit the Programme with a Bachelor’s degree, they must fulfil the requirements of the Bachelor of Science (Honours) in Computer Science.