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PGDM in Finance

PGDM in Research & Business Analytics

Syllabi And Curriculum For PGDM Programmes(tentative)


The specialization in Research and Business Analytics offers electives such as machine learning, artificial intelligence, big data, and domain specific analytics courses such as medical analytics and financial analytics, in order to cater to industry demandin such fast growing areas.


The specialization in Finance includes in-depth theoretical and empirical coursework in asset pricing and corporate finance. Electives offered include stochastic calculus, derivatives pricing, computational techniques in finance and market microstructures.


Domain-specific courses

Financial Risk Analytics
Medical Analytics
Machine Learning
Deep and Reinforcement
Analytics in Business
Algorithms and Data Structures

Select Courses – FINANCE

Domain-specific courses

Asset Pricing Corporate
Financial Market
Stochastic Calculus
Financial Time Series
Derivatives and Options


Students with the RESEARCH & BUSINESS ANALYTICS specialisation are ready for careers as data scientists, business analysis and intelligence, management reporting and control, datadriven consulting and policy formulation

Careers for FINANCE

Students with the FINANCE specialization are ready for careers in quantitative finance roles like structuring, trading, and risk management


The MSE PGDM is designed to train students for technically challenging jobs with financial institutions, consulting services and analytical companies. They also provide a unique combination of knowledge of complex theories with rigorous exposure to the underlying mathematical-statistical theories and practical financial modeling to enhance the ability of the students to meet the demands of today’s industry and companies committed to data-driven decision-making. Experts from industry further enrich the student ability. The two-year program is divided into six terms with class room instruction, and one term in the summer which consists of an internship in the industry. Both Research & Business Analytics (RBA) as well as Financial Engineering (FIN) undergo a similar first year, with a common syllabus in the first three terms. The focus is on learning foundational Mathematics, Microeconomics, Macroeconomics, Stochastic Calculus, Accounting, Finance with an introduction to Programming, Algorithms and Marketing

Objectives of the Programme

Develop a strong foundation for interdisciplinary work

Combine course work from the areas of Finance, Economics, Management, Business Analytics and Data Science

Exposure to latest trends in academics and industry through projects and internship

First Year Courses – Common for RBA & FIN

  • 111 Quantitative Methods
  • 112 Financial Management
  • 113  Microeconomics
  • 114 Calculus and Differential Equations
  • 115 Probability
  • 116 Analytics in Business
  • 121 Programming in Python
  • 122 Finance I
  • 123  Macroeconomics
  • 124 Optimization
  • 125  Statistical Inference and Modeling
  • 126 Machine Learning I
  • 131 Financial Econometrics
  • 132 Corporate Finance
  • 133 Big Data
  • 134  Supply Chain Management
  • 135  Advanced Macroeconomics
  • 136  Stochastic Process

Second year Courses

  • 241 Deep and Reinforcement Learning
  • 242 Financial Time Series Analysis
  • 243 Asset Pricing I
  • 244 Derivatives and Options
  • 245 Financial Risk Analytics in Banking and Financial Services
  • 246 Stochastic Calculus
  • 247 Programming in SQL
  • 251 AI Applications in Business
  • 252 Advanced Analytical Models for Decision Making
  • 253 Fixed Income Models
  • 254 Credit Risk Models
  • 255 Visualization
  • 256 Financial Market Microstructure
  • 257 Algorithms and Data Structures

List of Electives:
258 Applied Multivariate Statistics

  • 261  Information Theory and Cryptography
  • 262 Topics in Data Science
  • 263 Topics in Financial Engineering
  • 264 Computational Finance

List of Special Electives

  • 260 Advanced Visualization
  • 265 Asset Pricing II
  • 266 Medical Analytics
  • 267 Robotics
  • 268 Quantum Computing
  • 269 Projects in R / Python
  • 270 Algorithmic and High Frequency Trading
  • 271 Marketing Concepts
  • 272 Organization Behavior
  •   273 Human Resources
  • 274 Natural Language Processing and Text Analytics
  • 275 Dissertation
Admissions For PGDM