UNDERGRADUATE PROGRAM

Turning Passion to Purpose

* Interdisciplinary majors. Not offered as minors. Only offered as majors. 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. 

PROGRAM AIMS

The Data Science and Economics Specialisation 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.

PROGRAM 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

COURSES (CORE AND ELECTIVE)

38 MAJOR COURSES

Introduction to Computational Modelling

Introduction to Probability and Statistics

Introduction to Visualization

Introduction to Programming

Introduction to Algorithm Design

Econometrics II

Introduction to Discrete Mathematics

Databases for Data Science

Applied Probability and Simulation

Elements of Probability

Microeconomics 2

Analysis and Forecasting of Time Series

Introductory Calculus

Macroeconomics 2

Developmental Economics

Calculus of One Variable

Numerical Methods

Big Data Computing

Fundamentals of Data Structures

Mathematical Optimisation

Advanced Machine Learning for Data Science *

Microeconomics 1

Econometrics I

Bayesian Data Analysis *

Macroeconomics 1

Machine Learning for Data Science

Advanced Microeconomics I *

Linear Algebra

International Economics

Advanced Macroeconomics I *

Intermediate Multivariate Calculus

Quantitative Macro-Finance *

Applied Game Theory *

Applied Multivariate Statistics *

Deep Learning and Computer Vision *

Advanced Microeconomics II *

Natural language Processing *

  Advanced Macroeconomics II *

* 4th year undergraduate courses

Introduction to Programming

This is a first course in computer programming for those with little or no previous programming experience. It equips the student with basic tools to efficiently solve problems on the computer. Programming places considerable emphasis on algorithms and requires you to specify every step of the solution process. By doing so, it hones your analytical and logical skills and in the end makes you a better problem solver.

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.

Introduction to Computational Modelling

This course shows how computers can be used to model phenomena in the world using elementary computational approaches such difference equations. Induction serves as the reasoning principle to plan and understand such programs. This course also introduces gradient descent and elementary neural networks as a model of natural phenomena.

Introductory Calculus

This course introduces students to the rudiments of calculus and prepares them for study in courses which require calculus-based techniques. It focuses 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 whose mathematical preparation is less advanced.

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 students 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.

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 develops the analytical skills to prove theorems.

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

Microeconomics I

This course on Microeconomics offers a basic introduction to the working of market systems. It aims to provide student participants some basic theories and models and deals with the consumer behaviour and firms, demand and supply of goods, and services and resources in the economy. This will help them to understand how several complex processes in the world functions. 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 course on Microeconomics offers a basic introduction to the working of market systems. It aims to provide student participants some basic theories and models and deals with the consumer behaviour and firms, demand and supply of goods, and services and resources in the economy. This will help them to understand how several complex processes in the world functions. 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.

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.

Fundamental of Data Structures

This course will introduce students to the common data structures and their applications. It introduces the concepts and techniques of structuring and operating on Abstract Data Types in problem solving. It will equip them with a range of approaches and established algorithms for solving common classes of problems. Common sorting and searching algorithms will be discussed, and the complexity and comparisons among these various techniques will be studied. The course takes a practical approach, focusing on coded examples and applications.

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. His course aims to provide students with working knowledge of functions in two or more variables, their partial derivatives, geometric interpretation of the derivatives, demonstrate the applications of these concepts in problems of finding extrema with or without constraints, and introduce the tools and techniques of evaluating Multiple Integrals.

Ordinary Differential Equations

This course aims to introduce students to the basic theory of ordinary differential equations and the modelling of diverse practical phenomena by ordinary differential equations by a variety of examples. Students will learn both quantitative and qualitative methods for solving these equations.

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-foundation of the macroeconomic activities and critically appraising public policies and its implications.

Macroeconomics II

This course provides in-depth knowledge of some relevant macroeconomic models. The participant will study these core macroeconomic theories in detail. The course aims to improve the understanding the economic behaviour of an economy. The course also provides an analytical approach towards macroeconomic problems.

Introduction to Algorithm Design

This course explores efficient problem-solving methods that are useful for data science and Scientific computations. It will review common data structures and their applications and introduce a wide range of approaches and established algorithms for solving common classes of problems. It will cover common programming paradigms like Divide and Conquer, Greedy algorithms, Dynamic Programming to solve a wide variety of problems. Some common Graph algorithms will also be covered.

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. A practical approach with real world examples and case-studies from diverse sources will be adopted.

Mathematical Optimisation

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

This course aims to introduce the students to the various econometric techniques as applied to cross-section data. The course begins with discussion of techniques under stringent assumptions. These assumptions are later relaxed, and the problems, tests and corresponding remedial measures are discussed. Some knowledge of statistics is assumed.

Econometrics II

This course aims to build upon ‘Econometrics I’ by introducing the students to techniques of analysing time-series and panel data as well as other advanced methods.

International Economics

The focus of this course is to discuss models and issues in international trade theory and policy. It covers the main theories of international trade. It also examines issues and models of international trade policy.

Applied Probability

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.

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.

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.

Developmental Economics

This course will help students understand the underlying theories of development. Apart from learning about the historical progression of growth and development studies, this course also encourages creative thinking in terms of analysing the relevance of several of these theories in today's world. The course also introduces empirical research papers to help students get a feel of recent studies that have come out in the field of development economics.

Machine learning for Data Science

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.

Advanced Machine Learning for Data Science

This is the second of a two-part course which will build upon the foundations laid in the first course. This course begins with emphasising the difference between prediction and explanation, estimation of treatment effects using machine learning, dimensionality reduction and variable generation. The methods that will be covered include Causal Trees and Forests, Double Robust Machine Learning, and Text Mining with applications to economics.

Applied Multivariate Statistics

This course will introduce the theory and applications of multivariate statistical methods. While emphasis will be on conceptual knowledge of the statistical tools and techniques used to analyse multivariate data, it also focuses on applying these techniques to the real world using statistical packages. A background in calculus, probability and statistics is desirable.

Advanced Microeconomics I

This is a masters-level course that is suitable for students who have completed their training in intermediate microeconomics at the undergraduate level and seek to enhance their understanding of the subject. The course aims to provide students an appreciation of some commonly applied mathematical as well as game-theoretic tools and techniques in microeconomics, along with certain important applications to consumer theory and production. It addresses conceptual and applicational issues pertaining inter alia to mathematical functions, static optimization techniques, duality and envelope theorem, and static and dynamic games of complete information. In doing so, the course also provides a sound understanding of the applicability of various techniques in specific contexts. The end-term assessment consists of a research paper presentation on a topic covered during the course.

Advanced Microeconomics II

This course is suitable for students who have completed the Microeconomics I course and seek to enhance their ability to rigorously analyze the discipline. The course aims to provide students an understanding of the notion of stability of a single-market equilibrium and methods of comparative statics, optimization under uncertainty, as well as Bayesian games and their applicability in analyzing imperfectly competitive markets. This is achieved by addressing each topic with a minimum level of analytical rigour. The end-term assessment consists of an up to 2,000-word paper critically reviewing the literature on a topic covered during the course.

Advanced Macroeconomics I

The course is envisaged to familiarize the students with introductory level concepts of macroeconomic theories, policies, and applications. It primarily entails the theories pertaining to the determination of real income, employment, and interest rate in a macroeconomic framework. The course also includes some theories and concepts of the determination of exchange rates in an open-economy model.  It aims to provide a thorough understanding of several contending macroeconomic theories from different (thought) schools, including classical, neoclassical, Keynesian, and monetarist. The course is designed to discuss issues and challenges in macroeconomic policies, particularly at the globalized level. The course also offers an understanding on major mathematical tools used in modern macro analyses. The course also furthers the understanding on short-run and medium-term dynamics as captured by business cycles. More specifically, it will discuss several types of macroeconomic shocks and how an economy responds to these shocks in a dynamic setup.

Advanced Macroeconomics II

This course offers a thorough understanding of advanced-level concepts in macroeconomics. The course content is designed to provide students with a comprehensive understanding on general equilibrium models. More specifically, an explicit focus on analysis for real business cycles, new Keynesian and Dynamic Stochastic General Equilibrium (DSGE) framework. The course also offers exposure to some key concepts pertaining to calibration techniques and simulations. The course further entails advance, endogenous growth models. The primary aim of the course is to develop theoretical insights to understand and analyze policies (particularly in the short run). In order to cover the problems of the contemporary macroeconomy, the course pays more attention to the understanding of macroeconomic issues in a globalized framework. 

Applied Game Theory

The design and presentation of this course make it suitable for a diverse audience in terms of academic background. However, a semester's duration of undergraduate-level exposure to both game theory and mathematics is desirable. The content provides students with detailed exposure to different categories of games with widespread real-world applicability in fields including economics, psychology, sociology, international relations, political science, etc. The course addresses static and dynamic games of both complete and incomplete information and discusses plenty of applications. The treatment of this course is highly mathematical throughout, and assessments are commensurately challenging.

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 establishes the link between the macro economy and asset prices. This course will study the relationship between financial markets and the macro-economy. 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 and finally the third part of the course will use these techniques to analyse real life linkages between the 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.

BIG DATA COMPUTING

The advent of the 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 such as Spark, Kafka.  Real world case-studies will be used as examples.

Introduction to Visualisation

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.