Post Graduate Program in Data Science & Finance

Program Highlights:

1) Rigorous 1 year course aimed at training students for new-age jobs in financial technologies or core finance/analytics roles.  9 months of classroom training followed by 3 months of a capstone project/internship. 

2) Unique offering – course focusses on 3 important pillars – Core analytics (programming, machine learning, visualization), core finance (corporate finance, valuations, financial statements), and most importantly on how technology, analytics, and finance interact with each other (Fintech, & Quant Finance). 

3) Not only will you intensively learn to program on tools like Python, but also learn how to wrangle/manipulate large data sets, organize data using tools like SQL and create visual dashboards using Power BI & Tableau. 

4) Most importantly, you will build familiarity with emerging areas such as Fintech, Wealthtech, learn how to build your own blockchain, understand robotic process automation and quantitative finance. 

5) The course will provide you with strong access to corporates – through data labs and speaker sessions, you will constantly engage with data practitioners to get insights into how data is solving problems in every sector.

    Download Placement Brochure


    Select profiles from the 2021 batch
    Yash Beshwala
    Equity Research
    Nirakar Padhy
    Data Scientist
    Abhijeet Singh
    Analyst, Technology Consulting
    Manvi Mehta
    Naqiyah Dhariwala
    Data Scientist
    Other Analytics companies/roles where MDAE students and alumni have been placed

    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

    Yash Beswala

    MDAE - Batch of 2021

    MDAE students are creative, innovative, and technology savvy. They demonstrated an above-par understanding of the crypto space and I look forward to them doing great things at ZebPay!

    Prashanth Irudayaraj

    VP - R&D at ZebPay

    Download Placement Brochure

    Why MDAE?

    • 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 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 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.


    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) 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.

    3) 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

    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.

    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 purpose of the course on “Technologies in Finance I” is to provide the students with expert industry guidance and insight into the shifting nature of the financial sector and gain a holistic understanding of the technologies set to shape the future of finance and business.
    Learning outcome:
    • Analyze how competing interests may influence the way FinTech reshapes traditional banking.
    • Determine how FinTech corporations are changing the traditional currency regime
    • Articulate how artificial intelligence and machine learning are used in financial trading
    • Learn about concepts such as Insure Tech, Wealth Tech, Algo Trading, Digital Banking, Cyber Security, Blockchain and Crypto Currencies.


    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 main objective of this course is to build upon the Technology in Finance 1 course and introduce concepts in greater detail.

    Select topics: 

    • Blockchain & Smart Contracts
    • Decentralized Applications(dApps)
    • Cryptocurrencies, Tokens & Stable Coins
    • Liquidity Pools
    • Decentralized lending – AAVE
    • DLT – Distributed Ledger Technology
    • Decentralized Exchange – Uniswap

    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.


    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.

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