Post Graduate Diploma in Data Science & Finance

FAQ’s

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.

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.

Why should I pursue a full-time course in data analytics

1. Part time course is suitable for people who are already working as Analysts. To build a career as a Data Scientist requires rigorous training. Full time commitment is therefore necessary for a non-specialist to gain a deeper understanding of the concepts.
2. Part-time courses only teach you how to apply the tools; MDAE’s full-time rigorous course will teach you how to create your own models and deep learning software. You will learn the building blocks of machine learning which is an integral part of artificial intelligence.
3. Part-time course does nothing for your network or peer learning. A full time course increases your network with specialists in the field – faculty, corporate mentors, and your peers. About 30% of learning happens through your peers in the classroom
4. You learn lifelong skills – presentation, public speaking, debating, communication skills – all of which are critical for corporate success. A part time course does not emphasize these skills.

How is this course is different from other Data Science course.

1. This is the first diploma program in India which lies at the intersection of Data Analytics and Finance (1/3 of the course is finance). So, students with the regular Data science curriculum also learn the domain knowledge of Finance which help them understand real world problems and prepares them to take leadership positions. It eventually helps your growth in competitive jobs. You will be better equipped to work in the financial sector which contributed more than 60% of all jobs in the analytics sector.
2. We believe in teaching you “How to think and Not what to think!” In comparison to most programs that train students how to use data-science tools, our program teaches students how to build these tools. That is, you learn how to build the car rather than just learn how to drive it.
3. Students gain access to top companies – through MDAE’s deep linkages with the corporates and alumni network
4. Students get to learn from the leaders in Data Science industry and academicians at the cutting edge of their field. This helps the students to learn effectively what is required in the course.

Does this qualify as the 16th year for foreign universities?

Yes this qualifies as a 16th year of education. Our alumni, over the past 5 years, have a strong track record of securing admissions into leading foreign universities for Masters, MBA and even Ph. D programs. Universities such as Harvard, Georgetown, Chicago, Rotman, Boston, Georgia State, George Washington, Oxford, LSE, and many more have recognized and admitted alumni from MDAE.

Who should I contact if I have queries regarding my application status?

You can write to us at info@meghnaddesaiacademy.org or call us on 7045999326.

Does MDAE assist in Accommodation

MDAE assists students to take up private accommodations in the vicinity. We provide you with a verified list of brokers who then will assist the student in finding the accommodation.

Can I be exempted from the entrance test?

You can be exempted from giving the test if you have – 600+ in GMAT, 90 percentile and above in CAT.

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 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.

Core Courses

After clearing the Foundation exam, all students are required to take 5 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) 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

3) Econometrics

Course objectives: Empirical data analysis using statistical and mathematical tools forms an integral part of contemporary economics. The objective of this course is to introduce students to the fundamental ideas and tools in Econometrics in an applied and intuitive way. The distinguishing factor of this course is its copious use of the R programming language to illustrate the applications of the econometric tools learnt and hands-on modeling using real-world data. To illustrate, over the time of this course, students will be encouraged to answer questions of the following nature using econometric models with R.

• Given data on the number of daily downloads of a fitness app and its advertisement expenditure on print, radio and digital media, run an econometric model to find which advertisement platform worked best for the app. 
• What would be the implications for the model if print ads were done only twice during the given time period?
• How would you model the data if the company decides to increase its spend on radio when print ads are taken off and increase expenditure on print ads when radio ads are taken off air?

Select topics:

• Bi-variate and multivariate linear regression
• Assumptions under the linear regression model and their failure
• Extensions to the linear regression framework- log-log models, quadratic models, etc.
• Logistic and probabilistic regression models.

Learning Outcomes: By the end of this course, students will have a good theoretical and practical understanding of the fundamental econometric models and be well equipped to apply them on real world data using tools such as R. This course will build the basic foundation for careers as economists, finance professionals and data analysts and in general for any job role involving data analysis.  

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

SQL:

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

Visualization:

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

5) 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.

Elective

In this semester you are required to take 2 out of 6 electives:

1.Time Series Econometrics-

Course objective: Much of the data in economics and finance are indexed over time and present specific challenges in modeling and forecasting. This course is designed to equip students with the econometric tools used specifically in time series data analysis and implement those tools on real-world economic and financial data. The course will make rigorous use of the R programming language to illustrate the concepts and give students a hands-on experience of modeling and forecasting time series data. Some of the questions that this course will motivate students to answer are:

• How do we model India’s GDP and forecast it for the next two quarters?

• Do financial asset prices and goods prices move in tandem with each other?       

Did the pandemic cause a structural break in India’s IIP?    

Select topics:

• Stationarity and seasonality in time series data
• ARMA and ARIMA models and the Box-Jenkins methodology
• ARCH-GARCH models
• Vector Auto Regressions (VAR)
• Cointegration and Granger Causality

Learning Outcomes: By the end of this course, students will bTe 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. they shall be ready to take up roles in the industry in the fields of finance, macroeconomics and data analytics that involve building models and forecasting trends for the economy.

2.Advanced Econometrics for policy analysis–

Course Objectives: Recent Nobel prizes in Economics given to the likes of Abhijeet Banerjee, Esther Duflo, David Card and others are enough evidence to show the growing importance of natural experiments in the field of economic policy analysis.  This important course covers recent developments in the field of Econometrics where causal inferences are used to understand and evaluate policy outcomes. The course will equip students with advanced tools in Econometrics used in evaluating public policy outcomes and beyond. R programming will be used extensively to support the teaching. During this course, students can expect to answer the following type of questions based on econometric modeling.

• How successful has the Mid-day meal scheme been in improving the nutritional standards of school students in India? 
• Does purchasing health insurance necessarily lead to better health outcomes?
• How did Nobel laureate David Card establish the relationship between a hike in the minimum wage and employment levels in the US?  

Select topics:

• Randomized Control Trials
• Instrumental Variables
• Propensity Score Matching
• Regression Discontinuity Design
• Difference-in-Difference

Learning Outcomes: By the end of this course, students will be able to assess the different contexts in which these econometric techniques can be applied and how to apply them on real-world data. They will be in a position to evaluate public policies using modern econometric tools of causal inference. This course will prepare students for a career in development economics, public policy, data analytics and other allied fields.

3.Behavioral and Experimental Economics: A Public Policy Perspective 

Course Objectives: The goal of this course is to help students think creatively and critically about public policy issues by providing an understanding of how behavioral and experimental economics can be used to understand policies in the real world. The course will introduce cutting-edge research in behavioral and experimental economics and its implications for public policy. A particular emphasis will be given on behaviorally informed tools, such as default rules, and norms to study a range of issues at the intersection of behavioral economics, and public policy.

Select Topics:

• Existing theories in economics using experimental data and understanding the behavioral basis for the same.
• Theories in behavioral economics and their applicability.
• Behavioral insights to inform individual, household, and social decision-making.
• Complex public policy problems through the lens of principles of behavioral economics.
• Analyze data from experiments to evaluate and fine-tune policies that could not be easily tested with naturally occurring data.

Learning Outcomes: By the end of the course, students will be able to conduct experiments (both in the laboratory and in the field) and use the data from the experiments to evaluate theories as well as to test and fine-tune policies that could not be easily tested with naturally-occurring data. Students will also be able to analyze complex public policy issues as well as compare the merits and demerits of different policy approaches to a particular problem using insights from behavioral economics.

4) Technologies in Finance

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.

5.Corporate Finance-

Course Objectives: Corporate finance is concerned with understanding what financial managers should do to increase company value. This course is designed to introduce essential aspects of financial decision-making in businesses. The primary objective is to provide the framework, concepts, and tools for analyzing financial decisions based on fundamental principles of modern financial theory. We will work with live examples to study the application of these concepts. This course will provide Economics students the financial acumen which they can apply to not just for economics-based roles, but also to core finance and risk roles offered in the industry.

Select Topics:

• We will build on the concept of time value of money and understand how this concept can be used for capital budgeting decisions.
• Financial statement analysis, key ratios and free cashflow concepts
• Understand Risk and return, Cost of Capital and Capital Asset Pricing Model (CAPM)
• Why is capital structure important?
• Payout policy – can dividend decisions impact corporate value?
• Understand how companies are valued?
• Why do companies resort to Mergers and Acquisitions – understand the basics of Merger mechanics

Learning Outcomes:

• This course will help in understanding the financial aspects of managerial decisions which create value for the business.
• Key learning outcomes will be to apply skills in evaluating capital budgeting decisions by using different project evaluation criteria; perform time-value calculations by using financial mathematics; understand the capital structure and dividend decisions and application of various valuation techniques to value businesses.

6.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

Workshops

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.

Assessment

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.