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PGDM in Finance

PGDM in Research & Business Analytics

Syllabi And Curriculum For PGDM Programmes(tentative)

RESEARCH & BUSINESS ANALYTICS

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.

FINANCE/FINANCIAL ENGINEERING

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.

RESEARCH & BUSINESS ANALYTICS

Domain-specific courses

  • Medical Analytics
  • Financial Analytics
  • Algorithms

Sample Electives

  • Big Data
  • Machine Learning and
  • Artificial Intelligence

Select Courses – FIN

Domain-specific courses

  • Asset Pricing
  • Corporate Finance

Sample Electives

  • Computational Techniques in Finance
  • Stochastic Calculus
  • Market Microstructure
  • Derivatives Pricing
  • Financial Time Series

Careers for RBA grads

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

Careers for FIN grads

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

Curriculum

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 program

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  Marketing Management
  • 112  Financial Management
  • 113  Strategic Management
  • 114  Quantitative Methods
  • 115  Discrete Mathematics &
    Graph theory
  • 116  Micro Economics
  • 121 Macro Economics
  • 122 Introduction to Programming
  • 123 Advanced Quantitative
    Methods And Applications
  • 124 Operations Research
  • 125 Finance – I
  • 126 Financial Mathematics
  • 131 Corporate Finance
  • 132 Advanced Macroeconomics
  • 133 Supply Chain Management
  • 134 Introduction to Financial
    Econometrics
  • 135 Database Management
    Systems
  • 136 Stochastic Process

Second year Courses – Research & Business Analytics

  • 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 Applied Multivariate Statistics
  • 253 Fixed Income Models
  • 254  Credit Risk Models
  • 255  Visualization
  • 256 Financial Market Microstructure
  • 257 Algorithms and Data Structures
  • 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
  • 271 Marketing Concepts
  • 272 Organization Behavior
  •   273 Human Resources
  • 274 Natural Language Processing and Text Analytics
  • 275 Dissertation

Second year Courses – Finance

  • 241 Asset Pricing
  • 242 Artificial Intelligence &
    Machine Learning
  • 243F Pricing of Derivatives &
    Options
  • 244F Advanced Analytical models
    for decision-making
  • 245FA Financial Time series
    Analysis
  • 245FB Algorithms
  • 246FC Introduction to Financial
    Markets and Market Microstructure
  • 245DB Medical Analytics
  • 251 Stochastic Differential
    Equations in Finance
  • 252 Game Theory
  • 253F Fixed Income Models
  • 254FA Simulation Techniques in
    Finance
  • 254FB Advanced Asset Pricing
  • 255FA Stochastic Calculus
  • 255FB Numerical Methods in
    Finance
  • 255FC Pricing & Revenue
    Optimization
  • 256FA Risk Models
  • 256FB Stochastic Control in
    Finance
  • 256FC International Business
  • 246FA Taxation
  • 246FB Topics in Behavioural
    Finance
  • 246FC Introduction to Accounting
    & Managemen
  • 261 Banking and Financial
    Services
  • 262 Advance Topics in Economics
    and Finance
  • 263F Credit Risk Models
  • 264F Computational Finance
  • 265FA Deep Learning
  • 265FB Statistical & Empirical
    Methods in Finance
  • 266FC Corporate Valuation/
    Investment Banking
  • 266FA Algorithmic & High
    Frequency Trading
  • 266FB Advanced Topics in
    Financial Engineering
  • 267 Project Report
Admissions For PGDM