Data Science and Economics *

*Interdisciplinary major. Not offered as minor. Only offered as major. No minor combination possible.
The internet revolution has brought about radical changes in the way we lead our lives. The digital tsunami has inundated us with information from sources as diverse as biology, sensors, video streams, medical images, consumer behaviour, internet search and has led to unique insights and increased outcomes in the quality of life. The data revolution has been made possible by increasing computing speeds facilitated by advances in efficient computational algorithms for timely processing and analysis.  Data continues to be created at an exponential pace and its complexity continues to increase. The traditional methods of data analysis such as Mathematics and Statistics cannot handle this deluge of information which has necessitated the genesis of an interdisciplinary field to tackle the emerging challenges. This field, broadly labelled Data Science, is broadly an amalgamation of Mathematics, Statistics, Applied Sciences, and Computer Science and draws from them an appreciation and practical utility of mathematical, computational, and scientific principles to understand and solve problems of practical interest.

Students specializing in Data Science and Economics will have a sound knowledge of fundamental mathematics, make statistical inferences, be adept with computational processes, data processing, and data management along with critical domain knowledge while looking at data in context. By studying Economics along with Data Science, they will not only be trained in the concepts and methods of Economics but also will be in a position to process and gain insights into socio-economic and business data, and use them effectively to solve practical problems and predict future trends.  Students who specialize in this field will have career options in fields as diverse a business, government, medicine, advertising, entertainment, computing technologies among many others.

SPECIALISATION AIMS

The Data Science and Economics Major intends to:

1. Equip students with the necessary foundational mathematical and statistical skills required to solve problems of practical interest.
2. Provide an understanding of the process undertaken to arrive at a data model for gaining insights.
3. Develop habits that foster independent thought and a critical approach to data.
4. Impart advanced training in analytical techniques and computational methods for solving problems.
5. Expose students to a range of problems from diverse areas along with their associated conceptual models, and the appropriate methods employed to solve them.
6. Develop a solid grasp of the concepts, theories, analytical frameworks within the discipline of Economics
7. Develop an analytical, contextual and interdisciplinary understanding of concepts, theories and associate them with real life situations
8. Excel in economic analysis, economic model building, decision making and intensive academic research.
9. Train students for a career in industry, academics or research where data science is integral to the operations.

MAJOR OUTCOMES: After successful completion of the Major, the student will be able to:

1. Demonstrate analytical skills applicable to Mathematical and Statistical methods with an extensive repertoire of problem solving and logical thinking methods
2. Understand, reason, and draw sound conclusions about data in context.
3. Demonstrate advanced programming skills and facility with the use of multiple platforms for analysis of data.
4. Use advanced algorithmic techniques for efficient processing of data.
5. Demonstrate advanced skills in Machine Learning, Deep Learning, AI, Natural Language Processing, Computer Vision, Big Data processing and Cloud Computing.
6. Propose research ideas and take initial steps towards addressing them.
7. Exhibit an ability to relate and explain core economic terms, concepts and theories
8. Demonstrate the ability to practice economic way of thinking
9. Demonstrate in-depth knowledge and understanding of varied economic concepts and theories and their applications
10. Collect, process data from primary and secondary sources, perform analysis, and generate relevant insights
11. Construct, apply and solve economic models using quantitative tools
12. Communicate orally and in writing, analysis of data to a diverse audience

40 MAJOR COURSES

Introduction to Programming

This is a first course in problem solving through computer programming; no previous programming experience is assumed. Programming is introduced as an executable form of mathematics.  The course brings a clean separation between the problem, the model and the machine  and the 2 basic binding times: program development and program execution

Introduction to Discrete Mathematics

This course aims to cover the basics of discrete mathematics. Discrete mathematics is the study of discrete mathematical structures which do not rely on the notion of continuity. It introduces fundamental mathematical structures and various proof techniques and methods for solving different kind of problems. This course prepares the student to do advanced courses in applied mathematics and computer science.

Elements of Probability

This course is about chance and uncertainty.  Probability provides us a measure of uncertainty. It is aimed at the first or second-year college student as an introduction to the rudiments of probabilistic thinking and demands no more mathematical maturity than the ability to count and familiarity with elementary high school algebra.  The emphasis will be on problem solving and applications of simple probability concepts to the real world.

Introductory Calculus

This course introduces students to the rudiments of calculus and prepares them for study in courses which require calculus-based techniques. It focusses primarily on applications and covers the basics of limits, continuity, differentiation and integration of one variable. This course is challenging for those who have done calculus in high-school and yet introduces the basics to those whose mathematical preparation is less advanced

Calculus of One Variable

Calculus forms the foundation for a variety of subjects and finds applications in fields like Physics, Engineering, Economics, and Finance among others. In this course students will learn the concepts and techniques of single variable Differential and Integral Calculus

Programming in C++ with Lab

C++ is a versatile programming language that spans the gamut from low level programming to high level programming. This course introduces elements of low-level programming in C++ that requires understanding of low-level computer organization. It also teaches show high level aspects such as object oriented and generic programming are used in C++. The course connects the two levels so that students have a good understanding of basics of C++. Case studies, hands-on learning, and real-world problem-solving exercises will be included in the pedagogy to assist students obtain the skills needed for employment.

Microeconomics I

This course offers a basic introduction to the microeconomics. It aims to provide students with basic theories and models that help in analysing different market forms by understanding the behaviour of consumers and firms, demand and supply of goods, and services and resources in the economy. Several complex processes in the world can be well understood with the help of the fundamental models in microeconomic theory. It will give them an insight into how humans and firms take decisions and how their decisions in turn affect each other. A good command over microeconomics is necessary for critically appraising public policy and other economic functions.

Macroeconomics I

This is the first module in a two-module sequence that introduces students to the basic concepts of Macroeconomics. Macroeconomics deals with the aggregate economy. This course provides an overview of the basic concepts, measurements and institutions assisting in the functioning of an economic system. The course familiarizes students with macroeconomic tools, theory and policies. It will facilitate the students to understand economic problems at macro level and correlate theories to everyday economic scenarios. It also introduces students to various micro-founded theories of macro behaviour, e.g., consumption, investment behaviour of the households and firms and the demand for money.

Linear Algebra

This course emphasizes matrix and vector calculations and applications. It delves deeply into the theory of Matrices and other algebraic constructs such as Vector spaces, Determinants and Linear Transformations with particular emphasis on understanding the underlying theory and develop the analytical skills to prove theorems.

Intermediate Multivariate Calculus

This course deals with functions and calculus of several variables. It follows the course on Single Variable calculus. Topics covered include geometry of 2 and 3 dimensions, Partial differentiation, scalar and vector fields and multiple integration.

Introduction to Probability and Statistics

This course provides an elementary introduction to probability theory and its application to statistics with emphasis on the theorems and proofs of univariate statistics. Addressed to a beginning Mathematics Major, it provides a foundation for advanced courses in probability and statistics.

Data Mining for Business Intelligence

Data mining (DM) is the technique of extracting useful pattern or knowledge from large amount of data which is generated by business organizations now-a-days. It can be used to discover hidden relationships of variables or for prediction of future events based on historical data. Hence, organizations are adopting data mining technologies in order to understand/predict business outcomes more accurately and build their business intelligence (BI) capabilities. This course intends to build basic understanding of core data mining techniques which are very common these days for data-driven decision making.  Case studies, hands-on learning, and real-world problem-solving exercises will be included in the pedagogy to assist students obtain the skills needed for employment.

Data Structures and Algorithms

This course builds on the programming skills acquired in Introduction to Programming. It introduces program design, analysis, and verification in relation to the study of data structures. Data structures are common constructs to store and manipulate data, and they are important in the construction of sophisticated computer programs. Students are introduced to some of the most important and frequently used data structures and their algorithms: lists, stacks, queues, trees, hash tables and files. This course is programming intensive where students are expected to write a variety of programs ranging from simple to build more elaborate structures. The emphasis of programming component will be to write clear, modular programs that are easy to read, debug, verify, analyze, and modify.

Databases for Data Science

Databases are store houses of data. Reading, writing, updating, and deleting records in the database are an integral part of data operations. This course introduces the basics of relational database systems and progresses to prepare the student to design more sophisticated data models and optimizing queries in SQL. The course will also introduce the rudiments of NoSQL database systems.  Case studies, hands-on learning, and real-world problem-solving exercises will be included in the pedagogy to assist students obtain the skills needed for employment.

Microeconomics II

This course on Microeconomics continues from ‘Microeconomics I’ and it aims to provide student participants exposure to recent and advanced theories and models of microeconomics. A good command over Microeconomics is necessary for analysing the micro-foundations of macroeconomic activities and critically appraising public policy and its implications.

Macroeconomics II

This course is a sequel to Macroeconomics I. This course provides in-depth knowledge of some relevant macroeconomic models to understand macroeconomic fluctuations. The course aims to improve the understanding the economic behaviour of an economy using classical and Keynesian models of business cycle analysis. The course also provides an analytical approach towards macroeconomic problems. This course also introduces students to open economy macro issues involving flexible exchange rate regime. At the end, it provides both theoretical and empirical perspective of macroeconomic policy-making by looking at monetary and fiscal policies.

Numerical Methods

This course gives an introduction to the basic techniques for solving problems in science and engineering using numerical methods. It provides students with an understanding of the concepts and knowledge of the theory and practical application of numerical methods.

Mathematical Optimization

Optimization is the process of maximizing or minimizing an objective function that models a quantity of interest (e.g. cost, price, effort, distance capacity…) arising in various disciplines in the presence of complicated constraints. In this course students will learn various techniques of optimization for both constrained and unconstrained problems with applications to problems arising in various disciplines

Econometrics I

Econometrics is a set of research tools used to estimate and test economic relationships. The methods taught in this introductory course can also be employed in any economic setups, and in many social science disciplines. The aim of this course is to provide the students with the skills helpful in filling the gap between being “a student of economics” and being “a practicing economist.” The emphasis of this course will be on understanding the tools of econometrics and applying them in practice.  The course will integrate theory with the application of regression analysis and will help students in understanding and interpreting regression output. This course will build the foundation for any advanced level regression analysis using real-world data. The course will begin with a solid understanding of OLS regression in a two-variable set-up to help students buid their foundations in basic regression analysis. Then the course will discuss about the specification issues in regression analysis and delve into multiple linear regression. Further discussion and deliberation on specification issues will continue. Finally, the course will conclude with the discussions of econometrics issues when OLS may not be a suitable estimation method, and hence will lay the foundation for higher level econometric analysis. This course is an applied course in econometrics where students will become proficient at using econometric/statistical software to analyze data drawn from different field of study in economics.

Machine Learning for Data Science I

Machine Learning is an important computational tool to create knowledge and gain insights from large amounts of data. This course, which is the first of two courses will provide a broad introduction to machine learning, datamining, and statistical pattern recognition using supervised and Unsupervised learning methods. Topics to be covered include Regression, K -Nearest neighbors, Classification, Dimensionality Reduction, Decision Trees and Random Forests, Principal Component Analysis and Clustering Analysis. The approach will be to gain practical knowledge to quickly and effectively apply the concepts learned to new contexts. R and Python will be used extensively.  Case studies, hands-on learning, and real-world problem-solving exercises will be included in the pedagogy to assist students obtain the skills needed for employment. The approach will involve case studies, hands-on learning, and real-world problem-solving exercises to assist students acquire the skills necessary for the profession. The pedagogy will include case-studies, hands-on learning, and real-world problem-solving activities that helps in equipping the student with the skills required in the workforce. The approach will involve case studies, hands-on learning, and real-world problem-solving exercises to assist students acquire the skills necessary for the profession.

Banking and Insurance

The course introduces the students to current and traditional theories in Industrial Economics. It examines the internal structure of the firm and analyses its behaviour. It analyses the various aspects of strategic interactions between firms and determines the industrial structure and market conduct. It also discusses the role of policy in the context of competition and industrial policies and regulation. This course also uncovers the linkages between theories of economics and strategic management. Some tools and concepts from microeconomics, innovation and industrial organization are examined in the context of real-world business scenarios and their relevance to corporate policy making is critically evaluated (using game theory).

Introduction to Data Visualization

An integral aspect of data analysis is to derive insights quickly and efficiently and communicate the findings in an easy-to-understand fashion. One of the most effective ways to accomplish this is through visualisation of data. This course prepares the student by teaching the core principles of data visualization through hands-on practice on real-world data. It will employ the latest visualisation tools and cover basic and advanced charts/visuals including dashboard design, that are being increasingly employed in the field of data science. Programming tools and frameworks such as R, Tableau, Javascript, Google Chart API's, PowerBI, Python libraries etc will be used in the course The pedagogy will include case-studies, hands-on learning, and real-world problem-solving activities that helps in equipping the student with the skills required in the workforce.

International Economics

The objective of this course is to inform the basic models of international trade and to examine the effects of international economic policies on domestic and world welfare. This course covers the main theories of world trade and helps in explaining the composition, direction, and consequences of international trade, its determinants and effects of trade policy. This course is primarily theoretical but students will also be exposed to real-world examples.

Econometrics II

Econometrics II is an advanced level econometrics course. This course requires mathematical rigour and understanding of economic theories. This course will integrate theory with application of different advanced level regression techniques and will help students understand and interpret regression output. The course will allow students to build advanced level regression models and will enable them to analyze different types of datasets (cross-section data, panel data, and time-series data) in economics. The course is a hands-on, applied course where students will become proficient at using analytical software to analyze data drawn from different areas of economics. At the end of the course, students will also learn to conduct extensive literature review and write a comprehensive research paper in economics using different econometric models.

Applied Probability and Simulation

This course introduces probabilistic distributions and stochastic processes. It builds on knowledge acquired from elementary courses in probability and equips them to understand and apply advanced concepts to relatively more complex problems arising in diverse fields where uncertainty is a decisive factor.

Analysis and Forecasting of Time Series

Time series are data sets that provide sequential information. These span all processes that vary with time and examples range from daily temperature variation, stock prices, price of goods, evolution of interest rates to motion of planets to name a few. This course deals with mathematical and statistical processes that can be used to describe and simulate time series data, and introduces modelling techniques for making forecasts.  The pedagogy will include case-studies, hands-on learning, and real-world problem-solving activities that helps in equipping the student with the skills required in the workforce.

Developmental Economics

This course seeks to introduce students to the contemporary development discourse by exposing them to both classical and modern approaches to the analysis of growth and developmental issues such as poverty and inequality, and also by involving them in data-driven discourses on the differential development trajectories of different countries. The curriculum attempts to encourage students to critically evaluate the role of markets as well as various national and international institutions in solving contemporary developmental issues. The course also introduces current empirical research to help students get a feel of the recent literature in the field of development economics.

Big Data Computing

The advent of internet and digital economy has led to an exponential increase in data.  Data is generated at different speeds and in diverse formats. Processing this data efficiently and quickly is extremely critical for businesses. This course introduces the vocabulary and the concepts of big data problems, applications, systems and the techniques of big data computing.  It will introduce computing frameworks such as Apache Spark, Hadoop, data storage technologies such as in structured and unstructured database systems such as SQL and NoSQL distributed databases, along with streaming platforms.  Real world case-studies will be used as examples.

Machine Learning for Data Science II

This course builds on the first course on Machine Learning for Data Science focusing on neural networks and deep learning. Neural networks are a means of doing machine learning, in which, the computer mimics biological neural nets to learn and perform some task by analyzing training examples. The examples have been hand-labeled in advance (Supervised Learning). It is one of the fastest growing fields and underlie many of the advanced technologies of the modern age. The topics that will be covered include basic neural networks, convolutional neural networks and recurrent neural networks.  Methods to train and optimize the architectures and methods to perform effective inference with them, will be the main focus. Students will be expected to practice their skills using deep learning libraries such as Tensorflow, PyTorch and their derivatives. The approach will involve case studies, hands-on learning, and real-world problem-solving exercises to assist students acquire the skills necessary for the profession.

Quantitative Macro-Finance

This course is intended to provide students with an understanding of the relationship between financial markets and the macro-economy. The first part of the course focuses on theoretically establishing the link between the macro economy and asset prices. Topics include the behaviour of returns of different asset classes over the business cycle, the relationship between returns and inflation, and the implications for expected returns and portfolio choice. The second part of the courses will introduce time series econometric methodologies such as liner time series models (univariate and multivariate), forecasting and volatility models. Finally, the third part of the course will use these techniques to help students conduct empirical research using real life data on macro economy and financial markets.

Bayesian Data Analysis

The Bayesian approach to statistics provides a flexible framework for statistical inference, modelling, and prediction. In comparison to the frequentist approach to probability, the Bayesian approach assigns probability distributions to both the data and unknown parameters of the problem and provides a meaningful way to incorporate ‘prior’ knowledge which is used to arrive at ‘posterior’ predictions. This is an important model building framework and is used in conjunction
with machine learning. The course will begin by describing the fundamentals of Bayesian inference by examining some simple Bayesian models.  It will then progress to exploring more complex models such as linear regression and
hierarchical models in a Bayesian framework.  Bayesian computational simulation methods including Markov Chain Monte Carlo will be progressively introduced based on the context of the models with emphasis placed on testing. Case studies, hands-on learning, and real-world problem-solving exercises will be included in the pedagogy to assist students obtain the skills needed for employment.

Applied Multivariate Statistics

Multivariate statistics deals with data that arise when several interdependent variables are measured simultaneously. They are ubiquitous and are generated in all disciplines. The analysis of such multivariate data is challenging and requires advanced statistical techniques which are implemented using computers. This course aims to give you a good understanding of the conceptual ideas that underpin the analysis of multivariate data. The pedagogy will include case-studies, hands-on learning, and real-world problem-solving activities that helps in equipping the student with the skills required in the workforce.

Advanced Microeconomics I

Microeconomics-I is the master-level course which teaches the fundamentals of microeconomics. Students will learn the advanced concepts related to consumer and firm behavior, market imperfections, and welfare implications. This course will help students apply the microeconomic tools to analyse several policy questions applicable to the context of the real-world economy. The pedagogy will include case-studies, hands-on learning, and real-world problem-solving activities that helps in equipping the student with the skills required in the workforce.

Advanced Macroeconomics I

This course on is the first course on the two semesters long Macroeconomics courses and it aims to exposure students to both the theoretical and applied aspects of macroeconomic analysis.  The objective of this class is to introduce students to the main theories concerning the central questions of modern macroeconomics. Macroeconomics is often divided into two main areas of study: the determinants of long-run economic growth and the causes and consequences of short-run business cycles. We will begin this course by focusing on long-run questions. Why are some countries rich and other countries poor? What is needed for a country to sustain economic growth over an extended period?  By the end of the course, you will have learned how to set up, solve, and work with simple quantitative versions of these models on a computer. By the end of the course the student should be able to successfully  acquire, transform, interpret and discuss real world macroeconomic data; demonstrate an ability to critically assess applied macroeconomic research papers; present a balanced and well-informed opinion on a variety of macroeconomic policy issues; understand and interpret modern macroeconomic models; critically assess different macroeconomic policy options and provide clear and concise policy advice suitable for senior policymakers.  The approach will involve case studies, hands-on learning, and real-world problem-solving exercises to assist students acquire the skills necessary for the profession.

Deep Learning and Computer Vision

Modern technological developments have achieved revolutionary breakthroughs in computer vision. From mobile phone to drones, healthcare and autonomous driving cars, electronic cameras have become ubiquitous in today's age.  Efficient automatic processing of images is very critical in many of these fields.  Machine learning algorithms such as Neural Networks have revolutionized the field of computer vision, making new developments increasingly closer to deployment that benefits end users.  Aimed at students who have a background in machine learning, this course will not only cover the basics but also recent advancements in these areas and the application of these methods to real-world applications. The topics covered include CNNs, RNNs, Attention Models, Regenerative models among others. The pedagogy will include case-studies, hands-on learning, and real-world problem-solving activities that helps in equipping the student with the skills required in the workforce.

Natural language Processing

This course explores advanced topics in Natural Language Processing (NLP) to enable students to develop sophisticated NLP applications and understand the underlying concepts and methodologies. Students will learn how to extract information from text, develop speech-based applications, analyze linguistic structures, build conversational agents, apply machine learning algorithms, and evaluate NLP systems. Hands-on experience with Python libraries such as NLTK, Gensim, and spaCy will be a core component of this course. Case studies, hands-on learning, and real-world problem-solving exercises will be included in the pedagogy to assist students obtain the skills needed for employment.

Advanced Microeconomics II

This course will introduce students to the fundamental concepts of game theory. Students will learn the applications of game theory models. Several topics include simultaneous-move games, extensive games with perfect and imperfect information, public good games and information asymmetry. The pedagogy will include case-studies, hands-on learning, and real-world problem-solving activities that helps in equipping the student with the skills required in the workforce.

Advanced Macroeconomics II

This course will survey selected contemporary topics in macroeconomic research, treated from a general and rigorously mathematical standpoint. Emphasis will be put on developing analytical and modeling skills that will enable the interested student to approach problems close to the research frontier, as well as to contribute originally to these research fields, either theoretically or empirically. This course will build on Macroeconomics I to provide an advanced understanding of the field along with applications. 
The course will focus on the following broad topics: i) growth theory and policy; ii) growth and inequality; iii) theories of structural and frictional unemployment, unemployment policy, and growth models that account for unemployment; iv) coordination problems in macroeconomics. Most of these topics have common methodological features, which are to be found in standard dynamic optimization techniques-optimal control theory and dynamic programming in particular. Therefore, we will pay some attention to developing such techniques in a way that is as rigorous as possible given the time constraints. The approach will involve case studies, hands-on learning, and real-world problem-solving exercises to assist students acquire the skills necessary for the profession.

Applied Game Theory

Game theory offers a lens that enables us to analyze policy processes. In this course, we will mainly cover the applications of game topics. The first part will focus on the theory of markets, price determination, and market strategies. In the second part, "Behavioral Game Theory and Economic Experiments", Students will be exposed to the methods of applying experiments using actual goods rather than induced valuation. In the methodology section, students will learn how the field experiments differ from the lab-based economic experiments. In addition, they will study some of the recent developments in experiments and explore further applications of experiments in the context of gender, resource, and environmental issues. The approach will involve case studies, hands-on learning, and real-world problem-solving exercises to assist students acquire the skills necessary for the profession.