Post Graduate Diploma in Data Science & Finance

Jointly offered With Gokhale Institute Of Politics and Economics

Mode: 1 Year, Full-Time, Classroom Based.

Course Introduction

MDAE’s one year, PGP in Data Science Diploma has been designed to equip you with the necessary foundation you need to embark on your journey as a data scientist. The knowledge you gain in this diploma enables you for a career with any organization that practices data science. The Diploma lays special focus on the fields of Finance & Financial Technologies.

The rigorous and technical nature of the course also enables you to strengthen your foundations for higher education (Masters/Ph. D).

Our teaching methods involve a hands-on approach, involving case studies and real-world problems. Every course and each topic therein is taught with the objective of preparing you to apply your knowledge to problems that you will be expected to analyze and solve in your career.

Given that our curriculum design & delivery is self-governed, MDAE can integrate swiftly & seamlessly new skills, areas, and learning tools, considering rapid developments and changes taking place in the industry, thus enabling students to become more agile.

Within a short span, MDAE’s course is recognized as a springboard for students to get into the industry and provides the ideal platform for students to leverage the academy’s growing stature and industry network to shape their careers.

Course Overview

The PGP in Data Science and Finance is the country’s only program that lies at the intersection of Finance, Data Science & Technology. Given that the financial services & more recently, the fintech industry is one of the largest consumers of data scientists, we believe that it is imperative for students to not only have the required technical skills but also have the domain knowledge. While the course will prepare you for an analytics career in the finance/fintech space, the training you will receive will be fungible and applicable to excel in other sectors as well.

At the end of the program, you can rightfully claim knowledge of critical data science, finance and technology skills such as

  • Statistical Analysis
  • Programming, data management & Visualiation tools such as – Python, SQL, Tableau, Power BI
  • Machine learning
  • Domain knowledge of finance – Corporate finance, Financial Statements, Quant Finance
  • New age technologies – Fintech, Robotic Process Automation (RPA’s), Digital Banking HFT’s, Blockchain & Cryptocurrency, etc.

The course comprises 9 months of class-room based training followed by 3 months of an internship/capstone project. This allows you to apply some of the core principles that you have gathered through the course to solve real-world problems.

The program commences with a rigorous foundation course in Mathematics and Statistics.  The padagogy focusses on teaching the basics that are most essential for data scientists such as Combinatorics, Calculus, Linear Algebra and Statistics.  At the same time, the pedagogy is founded on imparting a strong intuition to students.  The applied content in the course will get you ready to grasp the principles and techniques of data analysis and machine learning that will be taught further in the program.

The programming modules are based on the simple myth-busting principle that “any keen graduate from any discipline can program effectively; programming is not the fiefdom of engineers”. The course is devoted to in-class coding by the faculty and students.  You will gain valuable insights into and appreciation for algorithms.

Our continuous assessment patterns are focussed on helping students refine a host of skills that form the bedrock of data science – problem solving, algorithmic thinking, programming, data wrangling and analysis, visualization and machine learning.

The course provides a healthy exposure to various tools such as Microsoft Excel, IDLE/Jupyter Notebook/PyCharm for Python programming MySQL, Power BI and Tableau.

Course Structure & Credits

The year is broken into 4 distinct parts – Foundation, Core, Electives & Internship/Capstone Project, and students need to acquire 22 credits to clear.

Foundations Course – 8-week primer on Mathematics & Statistics.

Core Courses – 4 core courses offered (each 48 hours)

  • Data Programming & Management
  • Foundation & Core Finance
  • Data Wrangling, SQL and Visualization
  • Machine Learning

Details of our core courses can be found here.

Electives – Students need to choose any 3 out of 4 electives (each 30 hours) Students can also audit electives should they choose to.

To view our electives – Please click here.

Internship/Capstone Project:

At the end of 9 months, students are assigned capstone projects or internships through the academy’s careers office where they have a chance to work with practitioners on real world problems and apply theories that they have been taught in class. Some of our project partners include –

Zebpay (a leading cryptocurrency exchange)

Breath AI – A leading health tech product company.

Quick Facts

Start Date (Batch of 22-23) Admissions Open
Application Start Date & Deadline

15th October (Early Bird). Rolling admission pattern.

Duration 12 months
Tuition Fees 4,45,000 +GST
Financial Support Merit & need based scholarships available for early applicants. Scholarships unto 90% awarded
Location Mumbai

Course Outcomes

MDAE is a pioneer in providing applied economics and data science education in the country. Our global faculty, agility in course design and world class industry access ensures that our graduates are well-equipped when starting their professional journey.

To read about placements in detail, click here.

Alumni Testimonials

Yash Beswala – 2021 Data Science Batch, Equity Researcher at JP Morgan:

MDAE has helped me bridge the gap between my undergrad in BBA and the quant intensive course which I want to take up in the future. It has helped me develop strong quantitative and programming skills from basic, which is not generally offered as a post-graduate course in the country. Overall, I think that this course has motivated me to dive further into the world of Fintech and has cleared my misconception of programming in the world of Finance.

Not From Data Science Background?

You can apply for the PGP in Data Science Diploma even if you do not have a science degree. You do however need to demonstrate a certain degree of proficiency in Mathematics & Statistics to be able to cope with the rigour of the course. We have admitted students from disciplines such as commerce, physics, management, psychology, mass media, engineering, chemistry, and other diverse fields into our Data Science Diploma. Graduates of our Data Science course compete confidently with science and engineering graduates in Data Science jobs.

Contact Information

To book a counselling session/speak to our faculty or alumni, please write to us at info@meghnaddesaiacademy.org or call us at 70459 99326.

Enquire here

Begin you application here

Admissions Process

Policy – MDAE follows a rolling admissions policy, which means that candidates who apply early have a higher chance of being selected. It also means that once our seats are filled, applications will be closed. We have 2 cycles in our admissions process – Early Bird Applicants (From Oct – Jan) and then normal admissions cycle. Historically more than 60% of our seats have been filled during early bird admissions so it is important that candidates apply early.

The admissions process is a 3-step process –

  1. Fill application form – Personal details, academic scores & 2 essays. You can start your form here (Add link).
  2. Given an online entrance test – One-hour long, online test comprises either 2 or 3 sections (Math, Logic & basic economics for the PGP in Economics & just the first two sections for Data Science & Finance). A fee of INR 2,500 will be charged for the test. Dates for when the test will take place will be updated on the website. You can be exempted from giving the test if you have – 650+ in GMAT (last 2 years), 90 percentile and above in CAT or 300+ in GRE.
  3. Personal Interview – based on being shortlisted after the form & test.

Fees & Funding

PGP Data Science & Finance – INR 4,45,000 + GST

The fees mentioned above are tuition fees and do not include the cost of accommodation, living expenses that the students will incur.


There are merit and need based scholarships that are available to students at the time of admission. Scholarships upto 90% of the tuition fee are available. The decision for awarding scholarships will be based on merit as well as need and will be decided post the interview of the candidate.

For merit scholarships – there is not separate procedure. You may be offered a scholarship at the time of receiving the offer. In some cases, the admissions committee may require you to give an additional interview.

For need based scholarships – you are required to submit financial details (such as total family income for the past 2 years along with IT Returns) along with your form. You need to fill in this form (attached here) and submit it along with your application.

Funding – Education Loan

Several central and private banks have granted loans to MDAE students. MDAE has also secured partnerships with leading fintech players to help secure loans. If you would like to connect with them, please reach out to the admissions team.

MDAE also provides and easy EMI facility for paying the fees in installments. More details in the offer letter.


  • Who is this course for?

The course is best suited for students as well as young working professionals who are looking at achieving the following outcomes –

  1. Get your first job or transition from your current one – work as a market economist, financial analyst, banker, data scientist, management consultant or research professional.
  2. Get ready for further education – Build the essential quantitative, critical thinking and analytics skills which are unfortunately not focused upon in several undergraduate programs in India but are a pre-requisite for Masters/MBA courses in India and abroad.
  3. Challenge yourself – Get out of the habit of learning by rote and start applying sophisticated tools & techniques to solve real problems.

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 and Statistical theory. 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 three subjects:

1) Mathematics

In this course students will learn:

  • Counting – Permutations and Combinations and Binomial Coefficients/Theorem
  • Introduction to Functions – Graph of Common Functions, Composite, Inverse, Continuity
  • Functions – Limits, Continuity, Asymptotes
  • Introduction to the Concept of Derivatives – Simple rules and examples
  • Derivatives – Product, Chain, Implicit
  • Maxima and Minima – Convexity, Concavity, Inflection
  • Partial Derivatives
  • Applications of Derivatives
  • Constrained Optimization, Lagrangian
  • Introduction to Integration as Limit of a Sum – Introduction to Anti-Derivatives, FTC to connect the dots
  • Common Integration Techniques, Solving Problems
  • Definite Integrals, Area Under the Curve, Producer-Consumer Surplus and Other Problems
  • Linear Algebra (separate module for Data Science students, with the option to audit for Economics students)

Learning outcomes of this course:

  • 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 of this course::

  • 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) Finance

Finance forms a very important function for any organization. It is closely linked with the strategic goals of any organization. A financially well-managed business is appealing to investors, lenders, suppliers, etc. and this helps in the overall growth of an Enterprise in both the short run as well as the long run.

The objectives of this course are:

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

  • The ecosystem of capital markets
  • Understanding of cash market  – Equity and debt – introduction to these asset classes, the idea of debt and equity markets
  • Understanding of derivatives market- types, basic features, etc, introduction to derivatives markets
  • Risk Management – the importance of risk, types of risk, etc.
  • A basic introduction to portfolio management- the idea of fund management
  • Basics of Corporate Finance concepts like – the cost of capital, leverage, etc.
  • Industry Business Lifecycle / Funding lifecycle
  • Overview of Financial Statement Analysis (FSA)

Learning outcomes of this course:

  1. Candidates will familiarize themselves with key concepts required for corporate finance/ financial markets
  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

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:

Installation; IDEs (IDLE, Jupyter Notebook); a programmer’s logic view of a computer; basic data types int, float, string, boolean; variables; the None type; print and input functions, escape sequences; operators – arithmetic, assignment,  relational, Boolean and identity operators; the format function; the sys.argv object; string functions; iterability, subscripting and indexing of objects; Unicode strings; the list data type, list functions, list comprehension; list operators, the tuple data type; the class type; the range class; the for loop; the while loop; nested loops; the enumerate function; the zip function; introduction to itertools; python in-built functions; the if, elif, else statements; mandatory code indentation in Python;  user-defined functions – declaration, arguments, body, return, variable number of arguments, named arguments; lambda functions; nested functions; generator functions; the dictionary data type; items, keys, values of a dictionary, dictionary methods; list of dictionaries; sorting a list of dictionaries; mutable and immutable data types; the numpy package; ndarrays; numpy methods; the pandas package; the dataframe type; the series type; the groupby type; dataframe indexing – iloc and loc; dataframe operations; filtering a dataframe using a boolean array; pandas functions; dataframe columns and index; multi-index; pivot tables; dataframe apply method; pandas merge; sorting of sortable types; the set data type.

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) Machine Learning

Course objective: The purpose of this course is to familiarize the students with modern machine learning models. Most of the courses in this area either focus on the concept or application of machine learning algorithms. This is an introductory course for theoretical machine learning. Furthermore, in this course, most models will be explained with great details such as problem statements, solutions, algorithms, implementation, and solving real-world problems with actual data.

Select topics:

  • Statistical Learning
  • Data Prepossessing / Cleaning
  • Linear Regression
  • Classification
  • Resampling Methods
  • Linear Model Selection and Regularization
  • Non-Linear Regression
  • Tree-Based Methods
  • Support Vector Machines
  • Unsupervised Learning

Learning outcome: By the end of this course, students will be able to formulate real-world problems and their solutions using mathematics and move on to advance machine learning techniques. The course will also prepare students with advanced skills for programming with machine learning models.

3) Core 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.

Select topics:

  • 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

4) Data Wrangling and Analysis

Course objectives: Data wrangling is important to learn if you want to be able to gather, select, and transform data to answer a question or solve a problem. This is essential for two reasons. First, the work of data wrangling can speed the time to develop and test models. Second, it allows for faster analysis and more accurate conclusions. Data wrangling makes data more usable in an organization. Understanding it can help your work better and make sounder decisions based on the data.

Select topics:

Data Wrangling:

Shaping and clean-up of raw data

Metrics of numeric variables, Metrics of categorical variables

Quartile analysis, Z-Score analysis, test of normality, skewness, generating data for a distribution, outlier analysis, crosstab and pivot tables, covariance, correlation, linear regression, exponential regression


Relational databases and database objects; CRUD operations of a database; Anatomy of a SELECT statement; Basic SELECT statements; Clauses: SELECT, FROM, SELECT DISTINCT, WHERE, GROUP BY, ORDER BY, UNION, LEFT JOIN, IN, INNER JOIN, RIGHT JOIN, FULL JOIN LIMIT; Functions and keywords: DATEDIFF, COUNT, COUNT(*), SUM, MIN, MAX, AVG, BETWEEN, NULL; Operators: *, >, >=, <, <=, =, BETWEEN, IN, AND, OR, NOT, AS, IS


The philosophy of Data Visualization – the WHY, WHAT and HOW of Visualization; Univariate Visualization, Bivariate Visualization,  Multivariate visualization.

Visualizations covered across Microsoft Excel, Python (matplotlib), Power BI, Tableau:

Histogram, box and whisker plot, bar and column charts (simple, clustered, stacked, 100% stacked), line chart, area chart, dual-axis line and column chart, heat map, diverging bar chart, waterfall chart, funnel chart, scatter plot, pie chart, donut chart, treemap, sunburst chart, geographical map charts, pareto chart, cards, tornado (butterfly) chart, animated race chart, dashboards

Learning Outcomes:

  • You will be able to build interactive dashboards using Tableau & PowerBI.
  • Describe the data ecosystem, tasks a Data Analyst performs, as well as skills and tools like SQL which are required for successful data analysis
  • Relational Database fundamentals including SQL query language, Select statements, sorting & filtering, database functions, accessing multiple tables
  • Python programming basics including data structures, logic, working with files, invoking APIs, and libraries such as Pandas and Numpy


In this semester you are required to take 1 compulsory course and 2 out of 3 electives:

1) Technologies in Finance I (Compulsory)
Course Objectives:  The primary objective of this course is to provide a firm understanding of the various upcoming fintech companies and its evolution. The course will guide the students in understanding the fintech market better.

Select Topics:

  • Evolution of fintech
  • Insurtech
  • Wealthtech
  • HFT and Algotrading
  • Cryptocurrencies

Learning outcomes: At the end of this course, the students would have grasped in detail about the most prominent fintech sectors and the trend within these sectors.


2) Quantitative Finance

Course objective:

Quantitative Finance is an important area that has a lot of applications in the domain for finance/banking. This specialized skill set has been around for a while now, and it will continue to be in demand from the industry going further. Further, many concepts covered under this course integrate well with ideas in data analytics too as both these topics build on various concepts from math and statistics. Candidates with expertise in quantitative finance theory along with hands on skills to build models will be able to work in good roles in the areas of quantitative analytics/ risk / pricing in banks/financial institutions.
Select topics:
  • Randomness in Assets – Geometric Brownian motion, correlates random processes etc.
  • Black Schole Merton PDE
  • Numerical methods for solving PDEs
  • Value at Risk
  • Rates analytics
  • Volatility smiles, volatility models
  • Credit Risk management

Learning outcomes:

  • Understand key concepts in quantitative finance including GBM, Ito’s, basics of stochastic differential equations etc.
  • Understand the math behind models like the BSM, and solve the same via numerical techniques
  • Building onto the concept of VaR discussed in Financial Analytics. Become proficient in numerical techniques around rates analytics
  • Understand basics of volatilities and popular models for vols
  • Knowledge of option greeks
  • Introduction to credit risk analytics
  • Understand basics of Interest rate models and the key math constructs that go behind it
3) Technologies in Finance II

Course objective: The primary objective of this course is to provide a firm understanding of financial technology with respect to blockchain. The course will guide student in building their own cryptocurrency and smart contracts.

Select topics:

  • Blockchains
  • Smart contract
  • dApps
  • Stable Coins

Learning outcomes:At the end of this course, the students would have grasped in detail about the most the working of blockchain and cryptocurrencies and building a smart contract.

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

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.


Big Data workshop:  (14 – 16 Hours)

  • Hadoop
  • Hive
  • Pig
  • NoSql

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.

Yash Beswala
MDAE Alumni (Batch 2021)

Equity Research, J P Morgan

Abhijeet Singh
MDAE Alumni (Batch 2021)

Analyst, EY

Nakiyah Dhariwal
MDAE Alumni (Batch 2021)

Data Scientist, ZebPay

Nirakar Padhy
MDAE Alumni (Batch 2021)

Data Scientist, Kotak

Manvi Mehta
MDAE Alumni (Batch 2021) Admit

University of Texas at Dallas

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