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 and 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

• Knowledge and Comprehension
  • Demonstrate a comprehensive understanding of core principles and theories in computer science and quantitative finance.
  • Recall and explain key concepts, methodologies, and models used in the fields of computer science and quantitative finance.
• Application and Analysis
  • Apply computer science techniques, algorithms, and programming languages to analyse economic data, model economic phenomena, and perform quantitative analysis or solve financial problems and analyse financial data.
  • Analyse and evaluate economic/financial models, econometric/quantitative methods, and statistical techniques to interpret economic data and draw meaningful conclusions or for decision-making and risk assessment.
  • Assess and interpret the implications of computational solutions and economic/financial models in real-world scenarios.
• Synthesis and Evaluation
  • Integrate computer science principles and quantitative finance concepts to develop innovative approaches to economic analysis and modelling or financial problem-solving.
  • Design and implement computational tools and software applications tailored for addressing economic research questions and facilitating data analysis or for financial analysis, modelling, and decision-making.
  • Critically evaluate and compare different economic/financial models, strategies, policies, computational methods, and technologies, considering their strengths, limitations, and ethical implications.
• Application of Technology and Tools
  • Utilise statistical/financial software, programming languages, and computational tools to analyse and visualise economic/financial data effectively.
  • Employ advanced computational techniques, such as machine learning algorithms or simulation models, to enhance economic/financial modelling, forecasting, and policy analysis, risk assessment, and trading strategies.
  • Apply data analytics and visualisation techniques to gain insights from economic data and support evidence-based data-driven decision-making in the economics/finance domain.
• Communication and Collaboration
  • Effectively communicate complex ideas, quantitative analyses, and computational solutions to diverse audiences using appropriate methods and tools.
  • Collaborate with professionals from different backgrounds, such as economists, policymakers, and computer scientists, to solve interdisciplinary problems and engage in team-based projects.
  • Participate in academic and professional discussions, presenting research findings and contributing to scholarly conversations in computer science and quantitative finance.
• Research and Innovation
  • Conduct independent research projects, applying research methodologies and quantitative techniques to explore new areas in computer science and quantitative finance.
  • Innovate and propose novel approaches, algorithms, or technologies to advance economic analysis, modelling, and decision-making, or to address existing challenges or improve financial processes.
  • Evaluate and synthesise existing research, identifying gaps, and proposing areas for future investigation in the intersection of computer science and quantitative finance.