The objective of this course is to establish a strong foundation for the Core Courses that will follow. Through its two modules viz. Mathematics and Statistics, students will be introduced to the essential elements of mathematical and statistical principles that are needed for the Economics and Data Science programs.
Learning Outcomes & Skills
- Rigorous understanding of mathematical and statistical concepts
- Ability to apply mathematical and statistical tools to Economics and Data Science
- Preparedness for the Core Courses
The two modules of the Foundation course are as follows:
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)
- Familiarize students with undergraduate to postgraduate mathematics
- Learn how mathematics is useful in analytics.
- Ability to solve mathematical problems
- Preparedness for the Core Course
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.
- To familiarize students with undergrad to post-graduate statistics.
- Applications of statistics to business and data analytics, economics and finance
After developing a solid quantitative foundation, all students are required to take a set of four Core Courses:
- Applied Microeconomics
- Advanced Macroeconomics
- Data Management & programming
Each of these courses lead the students into an in-depth exploration of topics that every economist is expected to be proficient in. These courses will also prepare students for the Elective Courses, which provide training in more specialized sub-disciplines of finance, public policy and data analytics.
- Rigorous understanding of markets and how they function
- Ability to solve problems in microeconomics and macroeconomics
- Ability to structure and analyze economic data with R programming
- Knowledge of real-world markets, economies and policies
- Ability to critically evaluate news articles about ongoing economic developments in India and abroad
- Ability to critically evaluate academic and policy research in economics
- Ability to write short- and long-form pieces on real-world economics issues
- Ability to prepare and understand simple financial accounts
- Preparedness for stream specialization in the Elective Courses
1) Applied Microeconomics
Course objectives: The objectives of this course is to provide a systematic theoretical analysis of the decision-making behaviour of economic agents and their interactions together with mathematical rigor to theoretical analysis and forecasting by extend theoretical understanding to real world occurrences.
To elucidate, recently there has been discussion about taxing junk food (soft drinks, for example) in an effort to reduce the incidence of obesity. Given the data for the market demand for junk food in the past, students are supposed to answer the following:
Do you think the demand for junk food is elastic or inelastic with respect to price? Based on your knowledge of the price elasticity of demand, do you think the deadweight loss of a soda/junk-food tax would be relatively large or relatively small? Why? Do you think taxing junk food would be a good idea? Based on your analysis, would it really help reduce the number of obese people?
Select topics: Thrust areas are:
- Consumers’, producers’ decision – making mechanism, market equilibrium and competitive goods and factor markets
- Choices under uncertainty and risks, market failure and role of government and markets with asymmetric information.
Learning Outcomes: Student will acquire a comprehensive understanding of the domain knowledge, ability to apply mathematical reasoning to the theory and make estimates regarding competitive market outcomes, equilibriums, and optimal solutions, develop independent critical thinking to understand limitations of competitive markets in delivering efficient solutions and need for government intervention and hence should be able to analytically evaluate efficacies of public policies.
2) Advanced Macroeconomics
Course objectives:The objectives of this course are to understand the principles and practice of macroeconomics and that economics and politics are intertwined. The course will help students make sense of the present-day macroeconomic context globally and in India. Some of the topics that students will be motivated to debate on during this course include:
- The impact of India’s new farm laws on its farmers and the economy
- The Impact of US elections on various global macroeconomic indicators
- The impact of Covid-19 on the Indian economy
- Basics of Macroeconomics such as the Solow Growth model and IS-LM
- Macroeconomic and national income concepts
- Monetary and fiscal policies
- Money, credit and banking
- Exchange rate concepts and exchange rate regimes
- Financialization and the global financial crisis of 2008
Learning Outcomes: At the end of this course, students will have a good understanding of macroeconomic theories and their ability to explain the real world. They will be able to interpret economic data and make sense of them, connect the dots of politics, economics, history and society and understand which of them is the more important force and in what circumstances. They shall be able to opine on and make futuristic statements on the direction of the macroeconomy with awareness of the limitations of such statements. The course will equip students to work in areas related to economic policy and analysis in government and in the private sector. It will also prepare them to undertake further higher studies in economics.
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?
- 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 Management and Programming
Course Objective: This course will teach the students the basics of computer programming and programming using the Python language. In addition to programming skills, the course will also introduce students to algorithmic thinking.
- A programmer’s logic view of a computer including memory management
- data types and variables
- operators: arithmetic, assignment, relational, boolean, binary and identity
- loops – for and while; nested loops
- classes and object-oriented programming
Python data types and variables covered: int, float, string, Boolean, list, tuple, dictionary, set, ndarray, dataframe and related types, range,
Examples of assignments and exercises:
- Live example of enhancing SBI’s downloaded statement and tagging each transaction such as “Is this credit an income?”, “Is this debit a telephone bill?”, “Is this a Swiggy payment?” etc.
- EMI schedule generation
An in-depth coverage of the following Python packages: the core, math, numpy, pandas, random, statsmodel, itertools, sys.
A special focus is laid on the Pythonic way of coding e.g. list comprehension. zip and enumerate functions; the itertools package, generator functions, lambda functions, the dataframe apply function. Specific instruction is imparted to students in the matter of approaching Python interviews.
- Students will be comfortable with the science of programming and the interaction of software with hardware
- They will be prepared to appear for interviews involving Python programming
- They will be adept at solving mathematical problems such as are presented at projecteuler.net
There are 9 elective courses on offer, of which, students will choose 4 for credit (and are allowed to audit one or more of the remaining 6). These 9 are divided into 3 streams of 3 courses each. A student is not required to, but may wish to, specialize in one of the 3 streams, and if he/she decides to do so, then he/she must take all 3 courses in that stream, and 1 additional course to complete the 4 for-credit courses.
- Data Analytics
- Public Policy
1) Data Analytics
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.
- Statistical Learning
- Data Prepossessing / Cleaning
- Linear Regression
- Resampling Methods
- Linear Model Selection and Regularization
- Non-Linear Regression
- Tree-Based Methods
- Support Vector Machines
- Unsupervised Learning
Learning Outcomes: 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.
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?
- 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.
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?
- Randomized Control Trials
- Instrumental Variables
- Propensity Score Matching
- Regression Discontinuity Design
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.
Financial Statements Analysis–
Course Objectives: The key objective of this course is to equip students with a solid understanding of financial statements and its application with real-world examples. The course will focus on analyzing and interpreting financial statements with the aim of performing fundamental analysis of the company and forming a view on its overall prospects. This course will provide Economics students the financial acumen which they can apply to not just in economics-based roles, but also to core finance and risk roles offered in the industry.
- Introduction to financial markets and understanding of the company and industry lifecycles.
- Understanding the structure and concepts of financial statements and be able to interpret them in the context of the business environment
- Understand various accounting terms and key accounting concepts – including terms used under GAAP/IND-AS.
- Create financial spreads in excel, interpret key ratios and make an initial assessment
- Understand applications of FSA – close look at the Fundamental Analysis process.
- Bank Analysis – compare and contrast the business models of a typical corporate to that of a Bank.
- Hands-on practice with live examples to deepen the knowledge and tune into real-life cases.
- Ability to easily interpret financial statements and understand their linkages.
- Be able to read a live Annual Report and use various analytical tools to assess a company.
- Know about various ancillary sources of information available and apply them to perform a comprehensive assessment of a company.
- Ability to easily understand Credit Rating Rationales and Equity Research reports.
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.
- 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
- 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.
Measuring Risk in Equity & Fixed Income Markets–
Course Objectives: Risk Management forms a very important function for any organization. It is closely linked with the strategic goals of any organization. A risk-managed business is appealing to investors, lenders, suppliers etc. and this helps in overall growth of an Enterprise in both the short run as well as the long run.
- Begin with the basics of capital markets and products, this course builds on to further concepts like derivatives, risk analytics etc.
- The portion is designed to help candidates build a sound and in depth understanding of financial and risk concepts. Concepts covered are used frequently in the industry.
- Capital Market Ecosystem
- Understanding of Debt markets – bond pricing, credit spreads, spot rates etc.
- Understanding of equity markets – Index, Beta, etc..
- Understanding of Derivatives markets – Linear (Forwards, Futures, FRA etc.) and non-Linear products (Options, Interest rate options etc.)
- Option Pricing models- Binomial , Black Scholes Merton, Black’s model
- Options trading strategies for risk management
- Risk Management – Market Risk, Credit Risk analytics, Risk mgmt in banks
- Capital and Regulatory – Organizational policies, regulatory guidelines etc.
- Candidates will build expertise and confidence in key areas of finance and risk management
- Course will provide Economics candidates the financial acumen which they can apply to not just for economist roles but also to core finance and risk roles offered in the industry
- Candidates will get a flavour of some hands-on modelling activities during the course which will help them connect theory with practice
3) Public Policy
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.
- 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.
Public Policy of Development
Course Objectives: This course will provide an understanding of how economics can be used to understand development policies in the real world. Students will learn to analyze complex public policy issues in development as well as evaluate the impact of different policy approaches to a particular problem.
- What determines the decisions of poor households in developing countries?
- What are the different types of risks faced by poor households and how can we mitigate them?
- How do we make schools work better for poor citizens?
- How do we make poor citizens healthy?
- How do we analyze the effectiveness of microfinance on economic and social development?
- What is the scope for policy interventions in India and what policies have been tried out?
Learning Outcomes: By the end of the course, students will be able to analyze complex public policy issues in development as well as evaluate the impact of different policy approaches to a particular problem.
International Political Economy
Course Objectives: Political Economy studies the intricate relationships between markets and states, between money and power, and between economics and politics, and is essential for students hoping to find employment in public policy roles in today’s global context.
- Why policymakers must pay attention to the interplay between economics and politics in designing effective policy
- What the main economic theories of trade are, and why these theories prescribe that countries should liberalize trade
- Why countries often choose not to liberalize trade, and what forms trade cooperation has in fact taken in the last 100 years
- What the main economic theories of FDI are
- How countries compete to attract multinational corporations
- Why China’s accession to the WTO in 2001 is a significant event in the history of multilateral trade liberalization
- What the Trans-Pacific Partnership means for its signatories and for those who have chosen not to sign the TPP
- What the political-economic considerations for liberalizing international finance are (with particular reference to the Euro project and its apparent failure)
- The fundamental principles of exchange rate economics, and why global financial stability requires the intervention and oversight of multilateral institutions such as the IMF
Writing a Dissertation is an integral part of the courses offered at MDAE because it initiates the students to the process of research which is the means to stimulate curiosity as well as satisfy it in a scientific manner. It opens minds to new possibilities and explore further on subjects and widens the knowledge base.
At MDAE, students work on a research topic in their interest for three months and are mentored by the faculty, industry experts, and the members of its Academic Board. The students are assisted by their supervisors in various ways including search for literature, data collection, data entry, and manuscript development.
The top four dissertations are read by the Chairman of the Academic Board, Lord Meghnad Desai and he selects the best paper from among them. These four papers are compiled as an e-book.
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:
- End-of-course examination and project
- Periodic problem sets
- Writing assignments/Programming assignments
- Contribution to classroom discussion and presentation
- 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 course instructor, and will be communicated to students via the course syllabus that the instructor will hand out prior to the course.
MDAE trains students to become practitioners to work in Banks, Consulting, Data Analytics, Policy and Think tanks. Our curriculum is designed by India’s leading Economists and Finance professionals from JP Morgan, Wellington Fund management, Aditya Birla Group and the Mint. Our unique teaching methods focus on applied learning and case studies. Our students get Industry Exposure through Mentorship programs, Econ Labs and Speaker Series. Over the past 4 years, more than 90 percent of our students are placed in corporates, banks and policy think tanks. MDAE students are working in companies such as Fractal Analytics, Reliance, Morgan Stanley, Deutsche Bank, Praxis, Deloitte, Aditya Birla Group, JP Morgan, Decimal Point, IDFC Institute, Gateway House.
On successful completion of the program, the students receive a certificate of “Post Graduate Program in Economics”
MDAE has incorporated some academic innovations in its program:
Speaker Series- The Speaker Series serves as an out-of-the-classroom venue for students to encounter exciting intellectual developments in the disciplines of economics and finance. A prominent academic or practitioner delivers a talk on a topic drawn either from their research or their work, and a discussion follows with members of the audience. These events are open to all member students, corporates, academics and the general public.
Workshops/Econ Labs- The Workshops/Econ Labs closely track what the students have learned in the classroom, and broaden and deepen their understanding of it. During each of these events, a prominent professional economist delivers a talk on a concrete real-world problem that he or she has grappled with, and that problem will serve as an illustrative application of concepts learned in the courses. Each of the Core Courses will offer Workshops/Econ Labs, while each of the streams in the Elective Courses will also offer its own set of Workshops/Econ Labs.