Post Graduate Program in Data Science & Finance

The Post-Graduate Program in Data Science & Finance at MDAE is a 1-year Full-Time course.

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The Post-Graduate Program in Data Science & Finance at MDAE is offered jointly with our industry partner Decimal Point Analytics. Our unique teaching methods focus on applied learning and conceptual learning. Students will participate in workshops and seminars with top Data Scientist, Economists and Finance professionals from around the world. Given our corporate connections, students will get the opportunity to participate in live projects. Further, upon completion of the Program, we assist students in internships and full-time job placements. Students from our PG Diploma course in Economics are currently working at leading firms in consulting, finance, data analytics, public policy and industry. The Program is meant for graduates from any discipline who would like to learn Data Analytics with the blend of economics and finance to solve real world problems. The Program is also useful for young professionals who would like to develop applied post graduate skills to further their careers.

Download Placement BrochureLearning Outcomes


  • 1) Rigorous training to be a Data Science expert
    • 425 hours of classroom learning spread over 9 months + 3 months of industry internship/capstone project.
      • 200 hours of industry driven core courses
      • Learn fundamental theories behind data science tools/machine learning models and Artificial Intelligence.
      • Become skilled at creating your own models and deep learning software, while many other courses only teach you how to apply the tools.
  • 2) Become a Leader in the field – understand how to define a problem, identify which machine learning models to use to solve the problem and even create the models, the task of a leader/manager, rather than just apply the tool, the task of a worker.
  • 3) Gain knowledge of Finance – create and optimise financial portfolios, create hedging strategies, portfolio optimisation using machine learning, which is essential to be a scientist in the industry, which other courses fail to teach. 60% of data scientists are employed in Finance.
  • 4) Learn from the best – strong mix of academics and practitioners in faculty. Our faculty are from IIT – IIM and other premier institutes in the country, as well as top Data Analytics firms such as the founder of Decimal Point Analytics. Low student/faculty ratio of 7:1 to ensure personal attention.
  • 5) Access to Top Corporates – support from distinguished corporate mentors who can help shape your future and give life–changing advice. Placement assistance is provided through MDAE’s strong industry connect and alumni network which help in fetching better internships and full-time placements.
  • 6) Develop critical skills – communication skills, presentation, debate, public speaking and team-work skills which are necessary to succeed in any career.

Admission & Eligibility


  • Graduate from any stream with 50% or equivalent score.

Selection Process:

  • Application form with CV/Resume and SOP.
  • 1-hour online Quantitative Techniques test (evaluation fees – Rs. 2000/-).
  • Panel interview.


Foundation Courses

The purpose of these courses is to prepare students for the Core Courses that will follow. Since not all students accepted into the program will have an undergraduate education in mathematics and statistics, the Foundations Courses will introduce them to the essential elements of Mathematics, Statistical theory and Finance. For students who do join the program with some undergraduate training, the Foundations Courses will help to review concepts that they may have forgotten. Foundation courses will cover four subjects:

1) Mathematics

In this course students will learn:

  • Linear algebra,
  • calculus,
  • graphs,
  • Optimisation,
  • numerical methods.

Learning outcomes

  • Familiarize students with undergraduate to postgraduate mathematics
  • Learn how mathematics is useful in analytics.
  • Ability to solve mathematical problems
  • Preparedness for the Core Course

2) Statistics

Data Scientists work with data drawn from the real world, and students are required to become proficient with the basic tools of data analysis. In this course students will learn:

  • What a random variable is, and how to describe it using the concept of probability
  • How to work with common probability distribution functions such as the Binomial, Poisson and Normal distributions
  • How to compute the expected value and variance of a random variable
  • How to work with jointly distributed random variables
  • How to formulate a statistical hypothesis, and how to test it
  • How to compute confidence intervals
  • Markov Chain methods.

Learning outcomes The purpose of this course is:

  • To familiarize students with undergrad to post-graduate statistics.
  • Applications of statistics into business and data analytics, economics and finance, and variance fields of studies.

3) Advance Mathematics

Course objective: The main objective of this course is to develop all the mathematical foundations needed for machine learning, computational finance and other related disciplines.

Select topics: 

  • Applied linear algebra- large vector and matrices and their computational methods, numerical methods for vectors and matrices
  • Mathematical optimization and introductory graph theory.

Learning outcomes: By the end of this course, students would be prepared with the necessary theoretical foundations required for the applied courses of advanced programming, machine learning and computational finance.

4) Finance

Course objective: 

Introduce the candidates to the importance of the Finance function in an Enterprise. The Finance Foundation portion is designed as a gentle introduction to key various areas of finance. Following are the topics discussed under the Foundations section

  •  Ecosystem of the capital markets
  •  Understanding of cash market – Equity and debt – introduction to these asset classes, idea of debt and equity markets
  •  Understanding of derivatives market- types, basic features etc.
  •  Risk Management – importance of risk, types of risk etc.
  •  A basic introduction to portfolio management- idea of fund management
  •  Basics of Corporate Finance concepts like – risk/return, cost of capital, leverages etc.
  •  Introduction to equity valuation models etc.- DCF, DDM etc.
  •  Overview of Financial Statements – understanding linkages

Select topics:

  • Introduction to components of capital markets, risk management, corporate finance, financial statement analysis

Learning outcomes:

1. Candidates will familiarize themselves with key areas in finance

2. Foundation course acts as a springboard for candidates to study advanced topics in Finance that would be covered under the Core portion of the syllabus.


Core Courses

After clearing the Foundation exam, all students are required to take 4 Core Courses

1) Data Management & Programming using Python

Course objective: The primary objective of this course is to provide a firm foundation in the application of data science principles to various fields of study, especially, Economics and Finance. The course will be exclusively taught on Python and particularly caters to programming novices.

Select topics:

  • Basic data types, operators, expressions and statements
  • Advanced data types such as lists, ndarrays, dictionaries, sets
  • Programming building blocks such as the if statement, for and while loops and functions
  • The Python pandas package and its critical objects such as the DataFrame, Series, Index, MultiIndex, GroupBy.

Learning Outcomes: At the end of this course students would have grasped the essentials of core Python concepts and be extremely comfortable processing data on Python.

2) Advance Programming

Course objective:  The main objective of the course is to develop all the programming and algorithm skills needed for more advanced courses such as machine learning.

Select topics: 

  • Implementation of applied linear algebra, graph theory, optimization, etc
  • Data preprocessing, handling large data like high-frequency orders and trades, handling missing data and data normalizations.

Learning outcomes: By the end of this course, students would have grasped an advanced level of programming in Python, including data processing and computations on big data. Specifically, they would learn to write computer programs to compute OHLC statistics of high-frequency data, adjusted closing price, price discovery, newton’s raphson methods and numerical optimizations.

3) Machine Learning

Course objective: The purpose of this course is to familiarize students with modern machine learning models. Most of the courses in this area either focus on the theory of machine learning or explaining tools of the popular models of machine learning models. This course bridges this gap by combining both aspects of teaching machine learning. This is an introductory course for theoretical machine learning.

Select topics:

  • Supervised and unsupervised learning models.
  • Clustering, recommender systems, neural networks and deep learning, tree-based learning and other advanced methods of learning.

Learning outcome: By the end of this course students will be able to apply machine learning tools to understand real-world problems such as detection of price manipulation in markets, stock selection using clustering, classifying Mutual Funds using recommender models, bank loan default and credit rating forecasting using deep learning.

4) Finance

The Finance Core portion is designed as a natural extension to the Foundation course. The Foundation course gives a gentle introduction to various key areas in finance, whereas the Core course goes deeper into explaining the intricacies of the financial domain.

Course objectives:

1. Build on the concepts that were introduced under the Foundations section.

2. The Finance Core portion is designed to help candidates build a sound and in depth understanding of financial concepts. Concepts covered in Core portion are used frequently in the industry. Following are the topics discussed under the Core section

  • Understanding of Debt markets – bond pricing, credit spreads, spot rates etc.
  • Understanding of equity markets – Index construction, beta, etc.
  • Risk Management – VaR, credit risk analytics, risk mgmt in banks
  • Capital Budgeting – project evaluation criteria /capital rationing etc.
  • Understanding Capital Structure decisions & Dividend Policies  
  • In-depth Financial Statement Analysis -Corporates – Income statement, Balance sheet, Cash flow statements, ratios etc.
  • Introduction to Financial statements of Banks
  • Credit Analysis & Rating methodology – LGD/PD, Business, Financial and Structural Risk
  • Business valuation – understanding valuation approaches Asset Approach / Income Approach / Multiples approach. Valuation using various equity valuation models.

Learning Outcomes:

1. Candidates will build expertise and confidence in key areas of finance

2. Course will provide Data Science candidates the financial acumen which they can apply to not just data science roles but also to core finance roles offered in the industry




There are 4 electives on offer and students can choose any 3 electives out of the four

1) Introduction to Fintech

Course Objectives:  The purpose of the course on ” Introduction to Fintech” is to provide the students with a holistic view of the Fintech ecosystem in both the Indian and global context. While most courses in this area provide either finance or a technology perspective of the industry, we aim to bridge the gap between the two through industry frameworks and use cases.
Learning outcome: This course will enable participants in identifying areas where financial technologies can be utilized to create new Fintech solutions as well as for rebuilding products & processes in Financial Institutions. This will also provide a detailed overview on emerging concepts around Open Banking and Digital Transformation along with deep diving into other segments of Fintechs such as Wealthtech, Insuretech, Regtech, etc.


2) Behavioural Finance

Course Objectives: This course complements the traditional finance courses that are mainly based on the conventional paradigm where investors and managers are generally rational.

This course will enable students to compare the traditional finance theories with the underlying prospect theory of behavioural finance. They will be able to describe the process through which the behaviour of investors contribute to asset pricing models and also become familiar with the biases that affect managerial strategic decision making.

Learning outcome:  After completing this course student will know the concepts like Bounded rationality, Prospect theory, challenges to the efficient market hypothesis, key behavioural biases of individual and professional investors as well as top managers, key anomalies in the markets proving the existence of Behavioural Finance.

3) Computational Finance

Course objective: The main objective of this course is to introduce the tools of optimization and machine learning for developing and testing quantitative portfolio strategies.

Select topics: 

  • Portfolio optimization, simulations and computations
  • Testing strategies using real data and decision making

Learning outcome: By the end of this course students will be able to apply the concepts to understand risk management, pricing and payoff and implement these models on Python.

4) Time-Series Econometrics

Course objective: This course is designed to familiarize students with the econometric tools used in time series analysis and implement those tools on real-world economic and financial data.

Select topics: Random walk, unit root process, seasonality, ARMA and ARIMA models, Box-Jenkins methodology, ARCH-GARCH models, etc.

Learning outcomes: By the end of this course, students will be familiar with the theoretical underpinnings of time series analysis, be able to build models with time-series data, run them and interpret the results on R.


Data security workshop: Cyber Ethics and Security: (14 – 16 Hours)

  • Data preservation techniques.
  • Digital security
  • Private Laws
  • Public Laws
  • EU Law

Internship/Capstone Project (3 months) Professional readiness program (throughout the course):

  • Resume building
  • Interview preparation
  • Development of communication skills.


The program adheres to a philosophy of continuous assessment throughout the academic year to ensure that students effectively master the course materials and get enough opportunities to test their mastery. To this end, the students will be graded on the following:

  • Trimester examinations
  • Periodic problem sets
  • Writing assignments/projects/presentations
  • Contribution to classroom discussion
  • Classroom Attendance

The precise breakup of the final course grade into these diverse components, and also the precise form that these components will assume in each course, will remain at the discretion of the faculty instructor, and will be communicated to students via the course syllabus that the instructor will hand out on the first day of the course.

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