Department of ECONOMICS NCR 

Syllabus for

1 Semester  2022  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MEA131N  MICROECONOMIC THEORY AND APPLICATIONSI  Core Courses  4  4  100 
MEA132N  MACROECONOMIC THEORY AND POLICYI  Core Courses  4  4  100 
MEA133N  PRINCIPLES OF DATA SCIENCE  Core Courses  3  3  100 
MEA134N  MATHEMATICAL FOUNDATION FOR DATA ANALYTICS  Core Courses  4  4  100 
MEA135N  STATISTICAL METHODS FOR ECONOMICS  Core Courses  4  4  100 
MEA136N  RESEARCH METHODOLOGY  Core Courses  2  2  50 
MEA171N  PYTHON PROGRAMMING  Core Courses  5  4  150 
2 Semester  2022  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MEA231N  MICROECONOMIC THEORY AND APPLICATIONSII  Core Courses  4  4  100 
MEA232N  MACROECONOMIC THEORY AND POLICYII  Core Courses  4  4  100 
MEA233N  ECONOMETRIC METHODS  Core Courses  4  4  100 
MEA234N  ADVANCED MATHEMATICAL ECONOMICS  Core Courses  4  4  100 
MEA235N  RESEARCH MODELLING  Core Courses  2  2  50 
MEA241BN  STOCHASTIC PROCESS  Discipline Specific Electives  4  4  100 
MEA242AN  PUBLIC ECONOMICS  Discipline Specific Electives  4  4  100 
MEA242BN  FINANCIAL ECONOMICS  Discipline Specific Electives  4  4  100 
MEA271N  R FOR ANALYTICS  Core Courses  6  5  150 
3 Semester  2021  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MEA331N  INTERNATIONAL ECONOMICS  Core Courses  4  4  100 
MEA332N  ECONOMICS OF GROWTH AND DEVELOPMENT  Core Courses  4  4  100 
MEA333N  APPLIED ECONOMETRICS  Core Courses  4  4  100 
MEA341N  BEHAVIORAL ECONOMICS  Discipline Specific Electives  4  4  100 
MEA371N  APPLIED MACHINE LEARNING  Core Courses  6  5  150 
MEA372N  DATA VISUALIZATION  Discipline Specific Electives  5  4  150 
MEA381PN  SPECIALIZATION PROJECT  Discipline Specific Electives  4  2  100 
4 Semester  2021  Batch  
Course Code 
Course 
Type 
Hours Per Week 
Credits 
Marks 
MEA481N  INDUSTRY INTERNSHIP  Core Courses  0  10  300 
MEA482N  RESEARCH PUBLICATION  Core Courses  0  2  100 
 
Department Overview:  
The Department of Economics, CHRIST (Deemed to be University) Delhi NCR Campus, formed in 2019 consists of a faculty pool with rich experience in teaching, research and consultancy. The Department has five fulltime faculty members with specialisation in Development Economics, Rural and Health Economics, Quantitative Economics, Agricultural Economics, Resource Economics, involving in advanced research.  
Mission Statement:  
Vision Establish an identity as a department of high standard in teaching and research in Economics. Mission Equip students with advanced knowledge and skill sets to address real world economic problems and undertake cutting edge research on contemporary economic issues.  
Introduction to Program:  
The Master of Science in Economics and Analytics is an intensive program that will guide students through economic modelling and theory to computational practice and cuttingedge tools, providing a thorough training in descriptive, predictive and prescriptive analytics. Students will be equipped with a solid knowledge of econometric and machine learning methods, optimization and computing. These bigdata skills, combined with knowledge of economic modelling, will enable them to identify, assess and seize the opportunity for datadriven value creation in the private and public sectors. Students will be trained to contribute significantly to empirical and applied work in the upcoming field of Economics.  
Program Objective:  
Assesment Pattern  
CIA  70% ESE  30%  
Examination And Assesments  
CIA  70% ESE  30% 
MEA131N  MICROECONOMIC THEORY AND APPLICATIONSI (2022 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 



Learning Outcome 

CO1: Demonstrate the analytical and critical skills relevant to economics thinking. CO2: Demonstrate the rigorous quantitative training that analytical economics requires. CO3: Apply the microeconomic theory to microlevel real world economic problems. 
Unit1 
Teaching Hours:10 

Introduction, Demand and Supply Analysis


 
Unit2 
Teaching Hours:15 

Theory of Consumer Behavior


 
Unit3 
Teaching Hours:20 

Theory of Cost, Revenue and Production


 
Unit4 
Teaching Hours:15 

Price and Output Determination


 
Text Books And Reference Books:
1. Pindyck, Robert & Rubinfeld, Daniel (2013), Micro Economics, 8th Edition, Pearson Education, USA. 2. Besanko, D. and Braeutigam, R. (2015) Microeconomics, 5th Edition, Wiley India 3. Nicholson, W, and Snyder, C (2021) Microeconomic Theory: Basic Principles and Extensions, Cengage Learning, USA  
Essential Reading / Recommended Reading
 
Evaluation Pattern CIA I20 CIA II 25 CIA III 20 Attendance5 ESE30  
MEA132N  MACROECONOMIC THEORY AND POLICYI (2022 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

This paper aims at strengthening the knowledge of important macroeconomic variables and their role in determining the equilibrium level of output and employment and provides insights into the factors influencing the capital inflows and outflows in an open economy model. It helps the students to understand the theoretical foundation of macroeconomics and the contribution of different schools of thought to the further development of macroeconomics. 

Learning Outcome 

CO1: Identify the determinants of various macroeconomic aggregates such as output, unemployment, inflation, productivity and the major challenges associated with the measurement of these aggregates. CO2: Understand the theoretical foundation of macroeconomics and the contribution of different schools of thought to the further development of macroeconomics. CO3: Describe the main macroeconomic theories of short term fluctuations and long term growth in the economy. CO4: Analyze the existing idea of different schools of thought/ theories. To check whether the ideology of those theories is working practically? To have some idea on why those theories have not been able to influence/ different economic conditions CO5: Understand the factors influencing the Balance of Payment and analyse the cause of disequilibrium in the Balance of payment. 
Unit1 
Teaching Hours:10 
Introduction and Output Determination


The development of macroeconomics Circular flow of money and product, Actual and potential output GNP identity on the product, income, and disposition sideThe government sector and foreign sectorClassical theory of income and employment Behaviour of Aggregate Demand and Aggregate supply, money and prices in the classical model Keynes’ theory of employment Role of Aggregate Demand Consumption function, investment demand Effective demand Determination of equilibrium income Theory of multiplierDerivation of Investment, expenditure and trade multiplier  
Unit2 
Teaching Hours:10 
Product and Money Market Equilibrium


Equilibrium income and the interest rate determination in the product market Equilibrium income and the interest rate determination in the money market Derivation of IS and LM curvesShift in IS and LM curvesSimultaneous equilibrium Fiscal and monetary policy effects on demandInteraction of monetary and fiscal policies Aggregate supply in the short run and long runSupply side disturbances and reactionsDemand side disturbances and reactionsDetermination of equilibrium income, employment, rate of interest and price level.  
Unit3 
Teaching Hours:10 
Supply of Money and Demand for Money


Financial intermediation — a mechanistic model of bank deposit determination; A behavioural model of money supply determination, a demand determined money supply process; RBI approach to money supply; High powered money and money multiplier; budget deficits and money supply; control of money supply. Neoclassical and Keynesian Synthesis  
Unit4 
Teaching Hours:10 
Demand for Money and PostKeynesian approaches


Classical approach to the demand for money — Quantity theory approach, Fisher’s equation, Cambridge quantity theory, Keynes’s liquidity preference approach, transaction, precautionary and speculative demand for money — aggregate demand for money; demand for money — Patinkin and the Real Balance Effect, Approaches of Baumol and Tobin; Friedman and the modern quantity theory; Crisis in Keynesian economics and the revival of monetarism  
Unit5 
Teaching Hours:10 
OpenEconomy Macroeconomics


MundellFleming model — Asset markets, expectations and exchange rates; Monetary approach to the balance of payments.  
Unit6 
Teaching Hours:10 
Monetary Institutions & Monetary Policy


Monetary transmission mechanism and targeting Inflation Money growth and interest rates Interest rate rules Taylor rule Rules versus discretion Central Bank autonomy Dynamic inconsistency of monetary policy credibility and reputation Coordination of fiscal and monetary policy, Rationale and impact of reforms since 1991 on BOP.  
Text Books And Reference Books: 1. N. Gregory Mankiw. (2012). Macroeconomics. 8th Edition, Worth Publishers. 2. Dornbusch, Fischer, Startz. (2010). Macroeconomics. 11th Edition, Tata McGraw Hill.  
Essential Reading / Recommended Reading 1. Burda and Wyplosz (2009). Macroeconomics: A European Text, Fifth Edition, Oxford University Press, New York. 2. Graeme Chamberline& Linda Yueh (2006). Thomson Learning. 3. N. Gregory Mankiw. (2012). Macroeconomics. 8th Edition, Worth Publishers. 4. Dornbusch, Fischer, Startz. (2010). Macroeconomics. 11th Edition, Tata McGraw Hill. 5. M. Maria John Kennedy (2011). Macroeconomic Theory, PHI Learning Private Limited, New Delhi. 6. H. L. Ahuja. (2012). Macroeconomics: Theory and Policy. 18th Revised Edition, Sultan Chand Publishers. 7. Brain Snowdown, Howard Vane and Peter Wynarczyk. (1995). A Modern Guide to MacroEconomics: An Introduction to Competing School of Thought, Edward Elgar Publishing. 8. Edward Shapiro. (2011). Macroeconomic Analysis. 5th Edition, Galgotia Publication Ltd. 9. Ackley. G. (1978). Macroeconomics: Theory and Policy, Macmillan, New York. 10. Mishkin Frederic (2007), The Economics of Money Banking and Financial Markets, 8th ed Addison Wesley Longman Publishers. 11. Bain, Keith & Howells,Peter (2009), Monetary Economics: Policy and Its Theoretical Basis, Palgrave. 12. .Friedman, Ben & Hahn F.H. (Eds.), (1990), Handbook of Monetary Economics, Vols. 1, 2, & 3, North Holland Publishers. 13. Langdana Farrokh (2009), Macroeconomic Policy: Demystifying Monetary and Fiscal Policy, 2nd Edition, Springer.  
Evaluation Pattern CIA I: 20 % CIA II: 25 % (Mid Semester Examination) CIA III: 20 % Attendance: 05 % ESE: 30%  
MEA133N  PRINCIPLES OF DATA SCIENCE (2022 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

The principles of data science deals with the econometric scientific methods of analyzing data. Today, we live in a big data world, where the amount of data generated everyday is very huge, therefore we need methods to clearly transform and analyze data. Therefore, machine learning, which is included in this syllabus, does the job. Also, the students here are introduced into different scenarios and methodologies to get results out of data. 

Learning Outcome 

CO1: Understand the modern big data econometric methods. C02: Annotate empirical data modelling with machine learning algorithms. CO3: Annotate empirical data modelling with machine learning algorithms. 
Unit1 
Teaching Hours:9 
Introduction to Data Science


Preparing and gathering data and knowledge  Philosophies of data science  data all around us: the virtual wilderness  Data wrangling: from capture to domestication  Data science in a big data world  Benefits and uses of data science and big data  facts of data  data science processes  
Unit2 
Teaching Hours:9 
Data Science Process


Overview of the data science process  retrieving data  Cleansing, integrating, and transforming data  Exploratory data analysis  Build the model  Presenting finding and building applications on top of them  
Unit3 
Teaching Hours:9 
Machine Learning


Machine learning – Modeling Process – Training model – Validating model – Predicting new observations –Supervised learning algorithms – Unsupervised learning algorithms  
Unit4 
Teaching Hours:9 
First Steps in Big Data


First steps in big data  Distributing data storage and processing with frameworks  Case study: Assessing risk when loaning money  Join the NoSQL movement  Introduction to NoSQL  Case Study  
Unit5 
Teaching Hours:9 
Databases


The rise of graph databases  Introducing connected data and graph databases  Text mining and text analytics  text mining in real world  text mining techniques Data Visualization Introduction to data visualization – Data visualization options – Filters – MapReduce – Dashboard development tools.  
Text Books And Reference Books: 1. Godsey, B. (2017). Think Like a Data Scientist, Manning Publications 2. Cielen, D. & Meysman A, (2016). Introducing Data Science, Manning Publications  
Essential Reading / Recommended Reading 1. Grus, J. (2019). Data science from scratch: first principles with python. O'Reilly Media, Inc. 2. O'Neil, C., & Schutt, R. (2013). Doing data science: Straight talk from the frontline. " O'Reilly Media, Inc. 3. Rajaraman, A., & Ullman, J. D. (2011). Mining of massive datasets. Cambridge University Press. 4. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning, Springer  
Evaluation Pattern CIA I: 20 % CIA II: 25 % (Mid Semester Examination) CIA III: 20 % Attendance: 05 % ESE: 30%  
MEA134N  MATHEMATICAL FOUNDATION FOR DATA ANALYTICS (2022 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

Linear Algebra plays a fundamental role in the theory of Data Analytics. This course aims at introducing the basic notions of vector spaces, Linear Algebra and the use of Linear Algebra in applications to Data Analytics. 

Learning Outcome 

CO1: Understand the properties of Vector spaces. CO2: Use the properties of Linear Maps in solving problems on Linear Algebra. CO3: Demonstrate proficiency on the topics Eigenvalues, Eigenvectors and Inner Product Spaces CO4: Apply mathematics for some applications in Data Analytics 
Unit1 
Teaching Hours:15 

INTRODUCTION TO VECTOR SPACES


Vector Spaces: Rn and Cn, lists, Fn and digression on Fields, Definition of Vector spaces, Subspaces, sums of Subspaces, Direct Sums, Span and Linear Independence, bases, dimension.  
Unit2 
Teaching Hours:15 

LINEAR MAPS


Definition of LinearMaps Algebraic Operations on L(V,W)  Null spaces and InjectivityRange and SurjectivityFundamental Theorems of Linear MapsRepresenting a Linear Map by a MatrixInvertible Linear MapsIsomorphic Vector spacesLinear Map as Matrix Multiplication  Operators  Products of Vector Spaces  Product of Direct Sum  Quotients of Vector spaces.  
Unit3 
Teaching Hours:15 

EIGENVALUES, EIGENVECTORS, AND INNER PRODUCT SPACES


Eigenvalues and Eigenvectors  Eigenvectors and Upper Triangular matrices  Eigenspaces and Diagonal Matrices  Inner Products and Norms  Linear functionals on Inner Product spaces. Multiple commodity markets ISLM Model MundellFleming Model  
Unit4 
Teaching Hours:15 

BASIC MATRIX METHODS FOR APPLICATIONS


Matrix Norms – Least square problem  Singular value decomposition Householder Transformation and QR decomposition Non Negative Matrix Factorization – bidiagonalization.  
Text Books And Reference Books: 1. S. Axler, Linear algebra done right, Springer, 2017. 2. Eldén Lars, Matrix methods in data mining and pattern recognition, Society for Industrial and Applied Mathematics, 2007.  
Essential Reading / Recommended Reading 1. E. Davis, Linear algebra and probability for computer science applications, CRC Press, 2012. 2. J. V. Kepner and J. R. Gilbert, Graph algorithms in the language of linear algebra, Society for Industrial and Applied Mathematics, 2011. 3. D. A. Simovici, Linear algebra tools for data mining, World Scientific Publishing, 2012. 4. P. N. Klein, Coding the matrix: linear algebra through applications to computer science, Newtonian Press, 2015.  
Evaluation Pattern
 
MEA135N  STATISTICAL METHODS FOR ECONOMICS (2022 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 



Learning Outcome 

CO1: Demonstrate the history of statistics and present the data in various forms. CO2: Infer the concept of correlation and regression for relating two or more related variables. CO3: Demonstrate the probabilities for various events. 
Unit1 
Teaching Hours:10 

Organization and Presentation of data


Origin and development of Statistics, Scope, limitation and misuse of statistics. Types of data: primary, secondary, quantitative and qualitative data. Types of Measurements: nominal, ordinal, discrete and continuous data. Presentation of data by tables: construction of frequency distributions for discrete and continuous data, graphical representation of a frequency distribution by histogram and frequency polygon, cumulative frequency distributions (inclusive and exclusive methods).  
Unit2 
Teaching Hours:15 

Descriptive Statistics


Measures of location or central tendency: Arithmetic Mean, Median, Mode, Geometric mean, Harmonic mean. Partition values: Quartiles, Deciles and percentiles. Measures of dispersion: Mean deviation, Quartile deviation, Standard deviation, Coefficient of variation. Moments: measures of skewness, Kurtosis.  
Unit3 
Teaching Hours:10 

Correlation and Regression:


Correlation: Scatter plot, Karl Pearson coefficient of correlation, Spearman's rank correlation coefficient, multiple and partial correlations (for 3 variates only). Regression: Concept of errors, Principles of Least Square, Simple linear regression and its properties.  
Unit4 
Teaching Hours:10 

Basics of Probability


Random experiment, sample point and sample space, event, algebra of events. Definition of Probability: classical, empirical and axiomatic approaches to probability, properties of probability. Theorems on probability, conditional probability and independent events, Laws of total probability, Baye’s theorem and its applications.  
Unit5 
Teaching Hours:15 

Probability Distribution


Binomial Distribution and their properties with practical examples, Poisson Distribution and their properties with practical examples, Normal Distribution and their properties with practical examples,  
Text Books And Reference Books: Text Books
 
Essential Reading / Recommended Reading
 
Evaluation Pattern Evaluation Pattern
 
MEA136N  RESEARCH METHODOLOGY (2022 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 

Max Marks:50 
Credits:2 

Course Objectives/Course Description 



Learning Outcome 

CO1: Demonstrate the knowledge of the range of qualitative and quantitative research methods potentially available. CO2: Differentiate between the role of practitioners and the role of researchers. CO3: Demonstrate the smallscale research project independently CO4: Demonstrate the understanding of and ability to critically reflect upon issues of ethics and research 
Unit1 
Teaching Hours:8 

Introduction


 
Unit2 
Teaching Hours:6 

Review of Literature


 
Unit3 
Teaching Hours:8 

Sampling Design


 
Unit4 
Teaching Hours:8 

Hypothesis Testing and Report Writing


 
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern CIA Only CIA I 10 Marks CIA 210 Marks CIA 330 Marks  
MEA171N  PYTHON PROGRAMMING (2022 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 

Max Marks:150 
Credits:4 

Course Objectives/Course Description 

The objective of this course is to provide comprehensive knowledge of Python programming paradigms required for Data Science. 

Learning Outcome 

CO1: Demonstrate the use of builtin objects of Python CO2: Demonstrate significant experience with python program development environment CO3: Implement numerical programming, data handling and visualization through NumPy, Pandas and MatplotLib modules. 
Unit1 
Teaching Hours:9 
Introduction to Python


Structure of Python ProgramUnderlying mechanism of Module ExecutionBranching and LoopingProblem Solving Using Branches and LoopsFunctions  Lists and MutabilityProblem Solving Using Lists and Functions  
Unit2 
Teaching Hours:9 
SEQUENCE DATA TYPES AND OBJECTORIENTED PROGRAMMING


Sequences, Mapping and Sets Dictionaries Classes: Classes and InstancesInheritanceExceptional HandlingIntroduction to Regular Expressions using “re” module.  
Unit3 
Teaching Hours:9 
USING NUMPY


Basics of NumPy  Computation on NumPyAggregationsComputation on ArraysComparisons, Masks and Boolean ArraysFancy IndexingSorting ArraysStructured Data: NumPy’s Structured Array.  
Unit4 
Teaching Hours:9 
DATA MANIPULATION WITH PANDAS I


Introduction to Pandas ObjectsData indexing and SelectionOperating on Data in PandasHandling Missing DataHierarchical Indexing  Combining Data Sets Aggregation and GroupingPivot TablesVectorized String Operations Working with Time SeriesHigh Performance Pandas and query()  
Unit5 
Teaching Hours:9 
VISUALIZATION AND MATPLOTLIB


Basic functions of Matplotlib Simple Line Plot, Scatter PlotDensity and Contour PlotsHistograms, Binnings and DensityCustomizing Plot Legends, Color BarsThreeDimensional Plotting in Matplotlib.  
Unit6 
Teaching Hours:30 
Lab Programs


1. Demonstrate usage of branching and looping statements 2. Demonstrate Recursive functions 3. Demonstrate Lists 4. Demonstrate Tuples and Sets 5. Demonstrate Dictionaries 6. Demonstrate inheritance and exceptional handling 7. Demonstrate use of “re” 8. Demonstrate Aggregation 9. Demonstrate Indexing and Sorting 10. Demonstrate handling of missing data 11. Demonstrate hierarchical indexing 12. Demonstrate usage of Pivottable 13. Demonstrate use of andquery() 14. Demonstrate ScatterPlot 15. Demonstrate 3D plotting sciPy  
Text Books And Reference Books: Essential Readings 1. Jake VanderPlas (2016) ,Python Data Science Handbook  Essential Tools for Working with Data, O’Reily Media,Inc. 2. Zhang.Y (2016) , An Introduction to Python and Computer Programming, Springer Publications  
Essential Reading / Recommended Reading 1. JoelGrus (2016), DataScience from Scratch First Principles with Python, O’Reilly Media, 2. T.R.Padmanabhan(2016), Programming with Python, Springer Publications.  
Evaluation Pattern CIAs Only  
MEA231N  MICROECONOMIC THEORY AND APPLICATIONSII (2022 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

The main objective of the course is to introduce both traditional as well as modern ideas and theoretical concepts in microeconomics. It also deals with a fundamental understanding of market theory, theory of factor pricing, theory of general equilibrium and welfare economics. It also includes understanding the role of institutions by focusing on transaction costs, absolute property rights and relative property rights. 

Learning Outcome 

CO1: Define and explain the basic concepts and hypothesis in Microeconomic Theory and their relations CO2: Define market, categorize markets and analyse perfectly competitive markets CO3: Demonstrate basic knowledge and skill in the use of cost and managerial concepts and techniques as management tools for planning, controlling, evaluating performance and making decisions. 
Unit1 
Teaching Hours:10 
Factor Pricing


Neoclassical approach: Marginal productivity theory  in perfect and imperfect product and factor markets; Product exhaustion theorem; Elasticity of technical substitution, technical progress and factor shares.  
Unit2 
Teaching Hours:10 
Market Failure and Game Theory


Asymmetric Information, The Payoff Matrix of a Game, Nash Equilibrium, Mixed Strategies, The Prisoner’s Dilemma, Repeated Games, Games of Coordination, Games of Competition and Games of Coexistence.  
Unit3 
Teaching Hours:10 
Non Collusive Oligopoly


Applications: strategic behaviour of firms in a market–Bertrand, Cournot and Stackleberg models – and entry deterrence  
Unit4 
Teaching Hours:15 
Theories of Distribution


Macro theories of distribution – Ricardian, Marxian, Kalecki and Kaldor’s  
Unit5 
Teaching Hours:15 
General Equilibrium and Welfare Economics


Existence, stability and uniqueness of partial equilibrium and general equilibrium. Pareto optimality; Fundamental Theorems of Welfare Economics; Theory of secondbest –Arrow’s impossibility theorem.  
Text Books And Reference Books: 1. Pindyck, Robert & Rubinfeld, Daniel (2013), Micro Economics, 8th Edition, Pearson Education, USA 2. Henderson, J.M. and R.E. Quandt (2003), Microeconomic Theory: A Mathematical Approach, McGraw Hill, New Delhi.  
Essential Reading / Recommended Reading 1. Furubotn, E. G., & Richter, R. (2010). Institutions and economic theory: The contribution of the new institutional economics. University of Michigan Press. 2. Andreu MasColell, M D Whinston and J R Green (1995), Microeconomic Theory, Oxford University Press. 3. Kreps, D. M. (1990). A course in microeconomic theory. Princeton university press. 4. Krugman, P., & Wells, R. (2015). Macroeconomics, 4th.edition. 5. Koutsoyiannis, A. (1979), Modern Microeconomics, (2nd Edition), Macmillan Press, London. 6. Mukherjee, Anjan (2002), An Introduction to General Equilibrium Analysis, Oxford University Press. 7. Osborne, Martin J. (2009), An Introduction to Game Theory, Oxford University Press. 8. Sen, Anindya (2007), Microeconomics: Theory and Applications, Oxford University Press, New Delhi. 9. Varian, H. (2000), Microeconomic Analysis, W.W. Norton, New York.  
Evaluation Pattern CIA I: 20 % CIA II: 25 % (Mid Semester Examination) CIA III: 20 % Attendance: 05 % ESE: 30%  
MEA232N  MACROECONOMIC THEORY AND POLICYII (2022 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

This course aims at strengthening the knowledge of important macroeconomic variables and their role in determining the equilibrium level of output and employment and provides insights into factors influencing the capital inflows and outflows in an open economy model. It helps the students to understand the theoretical foundation of macroeconomics and the contribution of different schools of thought to the further development of macroeconomics. Upon successful completion of this course, the students will be able to: critically evaluate the consequences of basic macroeconomic policy options under differing economic conditions within a business cycle. 

Learning Outcome 

CO1: Identify the determinants of various macroeconomic aggregates such as output, unemployment, inflation, productivity and the major challenges associated with the measurement of these aggregates. CO2: Understand the theoretical foundation of macroeconomics and the contribution of different schools of thought to the further development of macroeconomics. CO3: Describe the main macroeconomic theories of short term fluctuations and long term growth in the economy. CO4: Analyse the existing idea of different schools of thought/ theories. To have some idea on why those theories have not been able to influence/ different economic conditions CO5: Understand the factors influencing the Balance of Payment and analyse the cause of disequilibrium in the Balance of payment. CO6: Evaluate the consequences of basic macroeconomic policy options under differing economic conditions within a business cycle. 
Unit1 
Teaching Hours:15 
Theories of the Interest Rate and Monetary Policy


Real and monetary theories of the interest rate Keynesian theory, Wicksellian theory, Fisher’s theory, Hicksian theory Credit market imperfections Adverse selection and moral hazard  
Unit2 
Teaching Hours:15 
Theories of Money Demand and Interest rates


The Classical and Neoclassical views on holding money Real and monetary theories of the rate of interest: liquidity preference and loanable funds theories of interest The term structure of interest rates: Pure Expectations, Pure segmentation and Substitutability theories Portfolio theories of demand for money BaumolTobin approach to transaction demand for money Tobin’s portfolio optimization approach Friedman’s quantity theory of money.  
Unit3 
Teaching Hours:15 
Business Cycles, post Keynesian Macroeconomics


Measurement, Endogenous theories (Hicks, Goodwin, Kaldor), Exogenous theories  Real Business Cycle Theories  Real Business Cycle School and inter temporal substitution of labour Real Business Cycle theory technology shocks neutrality of money and flexibility of wages and prices Real Business cycle view on the great depression The modern monetarism, major postulatesKeynesian policy framework New Classical macroeconomics Stagflation trendThe SupplySide economicsmajor implications. Great recession and Covid19 Pandemic.  
Unit4 
Teaching Hours:15 
Post Keynesian Macroeconomics and Rational Expectation revolution


The new classical critique of microfoundations, the new classical approach; Policy implications of new classical approach — empirical evidence. Rational expectation revolution  
Text Books And Reference Books: 1. William. H. Branson (2005). Macroeconomic Theory and Policy, Third Edition, All India Traveller Book Seller Publishers, New Delhi. 2. D.N. Dwivedi. (2005). Macroeconomics: Theory and Policy. 2^{nd }Edition, Tata McGraw Hill Education. 3. Levacic and Rebman. (1982). Macro Economics: An Introduction to Keynesian and Neoclassical Controversies. 2^{nd }Edition, Macmillan Publishers.  
Essential Reading / Recommended Reading 1. Burda and Wyplosz (2009). Macroeconomics: A European Text, Fifth Edition, Oxford University Press, New York. 2. Graeme Chamberline& Linda Yueh (2006). Thomson Learning. 3. N. Gregory Mankiw. (2012). Macroeconomics. 8^{th }Edition, Worth Publishers. 4. Dornbusch, Fischer, Startz. (2010). Macroeconomics. 11^{th }Edition, Tata McGraw Hill. 5. M. Maria John Kennedy (2011). Macroeconomic Theory, PHI Learning Private Limited, New Delhi. 6. H. L. Ahuja. (2012). Macroeconomics: Theory and Policy. 18^{th }Revised Edition, Sultan Chand Publishers. 7. Brain Snowdown, Howard Vane and Peter Wynarczyk. (1995). A Modern Guide to Macroeconomics: An Introduction to Competing School of Thought, Edward Elgar Publishing. 8. Edward Shapiro. (2011). Macroeconomic Analysis. 5th Edition, Galgotia Publication Ltd. 9. Ackley. G. (1978). Macroeconomics: Theory and Policy, Macmillan, New York.  
Evaluation Pattern CIA I : 20 % CIA II : 25 % (Mid Semester Examination) CIA III: 20 % Attendance: 05 % ESE: 30%  
MEA233N  ECONOMETRIC METHODS (2022 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

On completion of the course students should be able to: ● Formulate, estimate, test and interpret suitable models for the empirical study of economic events; ● Acquire the ability to evaluate the performance of alternative econometric models through the appropriate use of tests.
● Attain knowledge reading and interpreting applied economic articles 

Learning Outcome 

CO1: Formulate, estimate, test and interpret suitable models for the empirical study of economic events CO2: Acquire the ability to evaluate the performance of alternative econometric models through the appropriate use of tests. CO3: Attain knowledge reading and interpreting applied economic articles CO4: Demonstrate the analytical and critical skills relevant to economics thinking CO5: Demonstrate the rigorous quantitative training that analytical economics requires CO6: Demonstrate good understanding of theoretical and applied econometrics and be able to perform econometric analysis and estimation, by understanding their application in Economics. 
Unit1 
Teaching Hours:12 
Regression Analysis


Linear regression model, two variables and multi variables, BLUE property, general and confidence approach to hypothesis testing, partial effects and elasticity, goodness of fit, model evaluation  
Unit2 
Teaching Hours:12 
Extension of Linear Regression Models


Consequences and detection of multicollinearity, heteroskedasticity, and autocorrelation; and remedial measures  
Unit3 
Teaching Hours:12 
Dummy Variables


Regression on qualitative and quantitative variables, dummy variable trap, structural stability of regression models, Chow test, piecewise linear regression model  
Unit4 
Teaching Hours:12 
Simultaneous Equation Models


Simultaneity bias, structural versus reduced form, identification: rank versus order condition, exact and over identifications, triangular model, methods of estimation including indirect least squares, twostage least squares and threestage least squares, LIML and FIML  
Unit5 
Teaching Hours:12 
Distributed Lag Models


Formation of expectations, naïve expectation versus adaptive expectations models, partial adjustment models, distributed lag models; Koyck’s model, Almon lag, polynomial distributed lag models, end point restriction, rational expectations models  
Text Books And Reference Books: 1. Wooldridge, J., Introductory Econometrics: A Modern Approach, SouthWestern 2. Gujarati, N.D., Basic Econometrics, fourth edition, McGraw Hill, 2003  
Essential Reading / Recommended Reading 1. Koutsoyiannis A. (1979). Theory of Econometrics (2nd Ed.) Palgrave Macmillan. 2. Maddala G. S. (1992) Introduction to Econometrics (2nd Ed.) Macmillan Publishing Company. 3. Gujarati D. N. & Porter D. C. (2010). Essentials of Econometrics, 4thMcGraw Hill International Edition. 4. Brooks C. (2019). Introductory Econometrics for Finance 4th Ed. Cambridge Univ. Press. 5. Hill, C., Griffiths, W. E., & Lim G. C. (2018). Principles of Econometrics, 5th Edition. Wiley. 6. Ramanathan, R. (2002). Introductory Econometrics with Applications, 5th edition, Thomson Asia Private Limited.  
Evaluation Pattern CIA I : 20 % CIA II : 25 % (Mid Semester Examination) CIA III: 20 % Attendance: 05 % ESE: 30%  
MEA234N  ADVANCED MATHEMATICAL ECONOMICS (2022 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

The main objectives of the course are to train the students to grasp the use of mathematical techniques and operations to analyse economic problems and to introduce students to various economic concepts which are amenable to mathematical treatment. 

Learning Outcome 

CO1: Exhibit a sound understanding of mathematical techniques discussed. CO2: Formulate economic problems in mathematical terms. CO3: Apply the relevant tools for analyzing economic problems. 
Unit1 
Teaching Hours:12 
Introduction to Mathematical Economics Equilibrium (Or Static) Analysis


Equilibrium analysis in EconomicsDefinition of equilibriumSolution of equilibrium Single vs. multiple equilibriumPartial vs. general equilibrium. Application: single vs. multiple commodity markets
 
Unit2 
Teaching Hours:6 
Integration


Areas under curveDefinite and indefinite Integration, Application Consumer Surplus and Producer Surplus  
Unit3 
Teaching Hours:15 
Unconstrained Optimization


Concavity, Convexity, Quasi concavity, Quasi convexity Optimization of functions of one variable Main concepts First order conditionsSecond order conditions (sufficient conditions) Applications: Profit maximization (one product) under:  perfect competition  monopoly. – Monopolistic –Oligopoly (Collusive and Non Collusive Oligopoly Models  Cournot model, stackelberg model) Optimization of functions of more than one variable The differential version of optimization conditions Extreme values of function of two variables and comparative static aspect of optimization Application: Profit maximization (two products) under perfect competition extreme values of function of n variables. Applications: i)Monopolist selling in segmented markets  
Unit4 
Teaching Hours:15 
Constrained Optimization Problems


Two variables, one constraintLagrangemultiplier methodFirst order conditionsSecond order conditions, Hessian Border Condition. Applications: Utility maximization and consumer demand (two goods, one period)Utility maximization and consumer demand (one goods, two periods) perfect access to international capital markets.financial autarky welfare implications  
Unit5 
Teaching Hours:12 
Difference and Differential Equations and Economic Applications


First order linear difference equations Second order difference equations First order differential equations Second order differential equations Application: Cobweb Market Model, Dynamic stability of Market price  
Text Books And Reference Books: 1. Simon, C. P., & Blume, L. (1994). Mathematics for economists (Vol. 7). New York: Norton. 2. Dowling, E. T. (2001). Introduction to mathematical economics. McGrawHill. 3. Alpha Chiang and Kevin Wainwright (2004), Fundamental Methods of Mathematica Economics. McGrawHill Book Company, 4th Edition (Chiang) 4. Carl P. Simon and Lawrence Blume (2006), Mathematics for Economists, W.W. Norton & Company (Simon) 5. Hoy, M., Livernois, J., McKenna, C., Rees, R., & Stengos, T. (2011). Mathematics for economics. MIT press.  
Essential Reading / Recommended Reading 1. Allen R G D (1974). Mathematical Analysis for Economists, McMillan Press and ELBS, London. 2. Wainwright, K. (2005). Fundamental methods of mathematical economics/Alpha C. Chiang, Kevin Wainwright. Boston, Mass.: McGrawHill/Irwin,. 3. Allen R G D (1967). Macroeconomic Theory, McMillan Co., Ltd., 4. Chiang A C (1986). Fundamental Methods of Mathematical Economics, McGraw Hill, New York. 5. Koutsoyiannis A. 2003. Modern microeconomics, 2nded, ELBS with McMillan. 6. Monga G S. 2001 Mathematics and Statistics for Economics, Vikas Publishing House Pvt. Ltd., Delhi. 7. Yamane, Taro (1981) Mathematics for Economists, Prentice Hall of India, New Delhi. 8. Mehta and Madnani (2005) Mathematics for Economists, Sultan Chand and Sons, New Delhi.  
Evaluation Pattern CIA I : 20 % CIA II : 25 % (Mid Semester Examination) CIA III : 20 % Attendance: 05 % ESE : 30%  
MEA235N  RESEARCH MODELLING (2022 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

The course is designed to train and equip the students to carry out research. The course enables the students to


Learning Outcome 

CO1: Develop a strong theoretical background which would help the students to better understand applicability of various methods and tools in different economic contexts or scenarios. 
Unit1 
Teaching Hours:30 

Research Modelling and Implementation


There is only CIA for this paper. Research work carried out in this semester is divided in two parts. Part A constitutes data collection and preprocessing in which students should carry out the following tasks and submit the document for the same before the MSE. Literature survey of existing data sets or any primary data sets in the respective area ● Gather the datasets from various sources (like visiting websites, universities, person, creating individually, etc.) ● Steps in preprocessing
Part B constitutes modelling and implementation of their research work. Students should perform the following tasks: ● Methodology ● Evaluation and Discussion of Results ● Limitations, Conclusions and Scope for future enhancements ● Plagiarism report
 
Text Books And Reference Books:
1. Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Cengage learning. 2. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press. 3. Khandker, S. R., Koolwal, G. B., & Samad, H. A. (2009). Handbook on impact evaluation: quantitative methods and practices. World Bank Publications. 4. Gujarati, D. N. (2021). Essentials of econometrics. SAGE Publications. 5. Angrist, J. D., & Pischke, J. S. (2008). Mostly harmless econometrics. Princeton university press.
 
Essential Reading / Recommended Reading
Research articles and publications from peerreviewed journals and established government reports.
 
Evaluation Pattern CIA I10 CIA II10 CIA III30  
MEA241BN  STOCHASTIC PROCESS (2022 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 



Learning Outcome 

CO1: Identify classes of states in Markov chains and characterize the classes. CO2: Compute probabilities of transition between states and return to the initial state after long time intervals in Markov chains. CO3: Determine limit probabilities in Markov chains after an infinitely long period. CO4: Solve the Industrial and realworld problems 
Unit1 
Teaching Hours:12 

Introduction to Stochastic Processes


 
Unit2 
Teaching Hours:12 

Poisson Process


 
Unit3 
Teaching Hours:12 

Branching Process


 
Unit4 
Teaching Hours:12 

Renewal Process


 
Unit5 
Teaching Hours:12 

Stationary Process


 
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern CIA 120% CIA 225% CIA 320% ESE  50% Attendance 5%  
MEA242AN  PUBLIC ECONOMICS (2022 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

The objective of this course is to present an introduction to public economics, which analyzes the impact of public policy on the allocation of resources and the distribution of income and wealth in the economy. We study various market failures as a justification for public action, and analyze how government policies can improve market outcomes. We address the financing side of government and consider key issues in tax policy along with contemporary issues in redistribution and social policy. Finally, selected analytical tools in public economics are discussed. Upon successful completion of this course, the students will be able to:


Learning Outcome 

CO1: Apply the basic tools, central concepts and models to solve problems in key topics in Modern Public Economics CO2: Summarize the assumptions, relevance, and limitations of the models CO3: Identify and analyse policy problems in public economics CO4: Apply analytical tools to assess arguments appearing in the policy debates. 
Unit1 
Teaching Hours:10 

Introduction And Background


Public Economics, Theory and Policy, Positive Versus Normative Analysis of Government Action ParetoCriterion, Theory of The Second Best, General Vs. Partial Equilibrium Theory, Theorems of Welfare Economics  
Unit2 
Teaching Hours:15 

Externality Theory and Public Goods


Economics of Externalities, Properties of Public Goods, Optimum’s Provision of Public and Private Goods, Political Economy: Lindahl Pricing, Voting TheoryArrow’s Impossibility Theorem, ClarkeGrove’s Mechanism,  
Unit3 
Teaching Hours:15 

Taxation in Theory and Practice


Taxation In Economics and Around the World, The Equity Implications of Taxation: Tax Incidence, Tax Inefficiencies and Their Implications for Optimal Taxation, Taxation and Evidence: Commodity, Income and Capital  
Unit4 
Teaching Hours:10 

Redistribution and the Social Policies


Social Insurance and Security: The New Function of Government, Health Economics and Private Health Insurance, Private Saving for Retirement, Political Economy of Redistribution, Income Distribution and Welfare Programs  
Unit5 
Teaching Hours:10 

Empirical Methods for Public Economics


Constrained Utility Maximization, CostBenefit Analysis, Decision Criteria for Benefit–Cost Analysis, Policy Analysis Involving Risk and Uncertainty, The Expected Utility Model, Risk Aversion and The Willingness to Pay  
Text Books And Reference Books: 1. Gruber, J. (2019). Public Finance and Public Policy. Worth Publishers. 2. Myles, G. D. & Hindriks, J. (2013). Intermediate Public Economics. MIT Press Limited.  
Essential Reading / Recommended Reading 1. Atkinson, A. B. & Stiglitz, J. E. (2015). Lectures on Public Economics. Princeton University Press. 2. Bellinger, W. K. (2015). The Economic Analysis of Public Policy. Taylor & Francis. 3. Varian, H. R. (2019). Intermediate Microeconomics with Calculus. W.W. Norton.  
Evaluation Pattern
 
MEA242BN  FINANCIAL ECONOMICS (2022 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

The course enables the students to ● Familiarize students with the financial system and its components viz. financial instruments, financial institutions, financial markets and financial regulations. ● Acquaint them with contemporary theories about the workings of different financial markets including money market, capital markets (bonds, stocks and hybrids) and derivative markets. ● Introduce them with the policy and regulatory framework within which financial institutions are required to function. 

Learning Outcome 

CO1: Apply economics models to understand the functions of financial markets and products. CO2: Analyze, interpret and present financial data CO3: Explain the alternative approaches to economic problems 
Unit1 
Teaching Hours:10 
The Demand for Securities


The time dimension – Present value and duration – The calculation of yields on zero coupon bonds – The term structure of interest rates – The risk dimension – Measurement of risk. Bivariate distributions–Conditional probabilities and expected values – Estimating the mean and variance of returns – Expected utility.  
Unit2 
Teaching Hours:10 
The Supply of Securities Regulations Governing Supply of Securities


– General characteristics of securities – Government bonds – Indexlinked bonds – Corporate Securities – equities, bonds, convertible securities – Stock market operations – Money market funds – Claims on financial institutions.  
Unit3 
Teaching Hours:10 
Securities Markets and Efficiency of Stock exchanges


The over the counter stock market – Operational efficiency and the Efficient Market Hypothesis(EMH) – The weak, semistrong and the strong form of EMH.  
Unit4 
Teaching Hours:10 
The Determination of Equity Prices


Shares as claims on future dividends and on corporate net worth – The Capital Asset Pricing Model (CAPM) – The simplest form – Estimating betas Implications for portfolio management – Validity of CAPM – Arbitrage Pricing theory. An alternative approach – Stock indices – Bombay Sensitive Index, Bombay National Index, Dow Jones Industrial Index(DJI), New York Stock exchange composite index(NYSE).  
Unit5 
Teaching Hours:10 
Security Analysis and Market Efficiency


A modern view of security analysis – Macroeconomic developments and securities markets– Performance of securities markets – Industry growth, structure and firm specific factors Uses and pitfalls of Price / Earnings ratios.  
Unit6 
Teaching Hours:10 
Financial Instruments


Derivatives Uses of Derivatives – Futures contracts and futures markets – Forward contracts – The origins of Futures trading – Basic elements and organization of futures contract.  
Text Books And Reference Books: 1. Chandra, P. (2017), Investment Analysis and Portfolio Management (5th edition), McGraw Hills Education. 2. Shapiro, A. C. (2012), Multinational Financial Management (9th edition), Wiley. 3. Campbell, J. Y., Lo, A. W., & Mackinlay, A. C. (1997). The Econometrics of Financial Markets (2nd edition) Princeton University Press.  
Essential Reading / Recommended Reading 1. Houthakker H. S. & Williamson P. J. (1996), The Economics of Financial Markets, Oxford University Press 2. Eichberger, J and Harper, I. R. (1997), Financial Economics, Oxford University Press 3. Ross, S. A., & Westerfield, R. (2018), Fundamentals of Corporate Finance (12th edition), McGraw Hill Education. 4. Fabozzi (2009), Bond Markets (7th revised edition), Pearson Publications, USA, February 27 5. Fama E.F. (1970), Efficient Capital Markets: A Review of Theory and Empirical Work, Journal of Finance, 25 May, pp. 383417 6. Fama, E. F. (2021). Efficient capital markets II (pp. 122173). University of Chicago Press. 7. Graves, Affleck, Hegde, J.S. & Miller, R. (1994),Trading Mechanisms and the Components of the Bid Ask Spread, Journal of Finance, 44, pp. 14711488. 8. Barsky, R. and Long, J. De (1993), Why Does the Stock Market Fluctuate, Quarterly Journal of Economics, 108, pp. 291311 9. Black, F., Jensen, M.C. & Scholes, M.A. (1972),The Capital Asset Pricing Model: Some Empirical Tests in M.C. Jensen (ed.), Studies in the Theory of Capital Markets, Praeger, New York.  
Evaluation Pattern CIA 1 20 CIA 2 25 CIA 3 20 ESE 30 Attendance 5  
MEA271N  R FOR ANALYTICS (2022 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 
Max Marks:150 
Credits:5 
Course Objectives/Course Description 

This course is planned to give the students the basic knowledge in R programming language and to make them familiar with the flexible graphical capabilities of R. It also covers the Statistical computational features of R and exploratory analysis and modeling using R 

Learning Outcome 

CO1: Understanding data using statistical tool CO2: Demonstrate graphical representation of data using R CO3: Apply their knowledge of various tools create R programs CO4: Design and create applications which can handle multivariate data. CO5: Evaluate the correlation between data and apply Exploratory Data Analysis 
Unit1 
Teaching Hours:10 

Introduction and preliminaries


 
Unit2 
Teaching Hours:10 

Lists and Data Frames


 
Unit3 
Teaching Hours:10 

Data Exploration for Univariate and Bivariate


 
Unit4 
Teaching Hours:10 

Data Exploration for Multivariate Data


Data Exploration for Multivariate DataMultivariate Data Storing multivariate data in R data frames, Accessing and manipulating data in R data frames, view multivariate data, apply( ) family functions  apply( ), sapply( ), lapply( ), tapply( ), dplyr package select( ), filter( ), arrange( ), rename( ), mutate( ), group_by( ), %>%, summarize( ).  
Unit5 
Teaching Hours:10 

Correlation and Data Visualization


Pearson correlation, Spearman rank correlation lattice package in R  1D, 2D, 3D plots using lattice, ggplot2 package in R 1D, 2D, 3D plots using ggplot2  
Unit6 
Teaching Hours:10 

Regression and Diagnostic Tests


Multiple Regression, Qualitative Regressor Models, Qualitative Response Regression Models, Checking the assumptions of Regression Model and Model Diagnostics.  
Text Books And Reference Books: 1. Allen B. Downey, “Think Python: How to Think Like a Computer Scientist‘‘, 2nd edition, Updated for Python 3, Shroff/O‘Reilly Publishers, 2016 2. Kerns, J. (2010).“Introduction to Probability and Statistics Using R” (First Edition).  
Essential Reading / Recommended Reading 1. John V Guttag, ―Introduction to Computation and Programming Using Python ‘‘,Revisedand expanded Edition, MIT Press, 2013 2. Robert Sedgewick, Kevin Wayne, Robert Dondero, ―Introduction to Programming in Python: An Interdisciplinary Approach, Pearson India Education Services Pvt. Ltd., 2016. 3. Timothy A. Budd, ―Exploring Python‖, McGraw Hill Education (India) Private Ltd 2015. 4. Kenneth A. Lambert, ―Fundamentals of Python: First Programs‖, CENGAGE Learning, 2012. 5. Charles Dierbach, ―Introduction to Computer Science using Python: A Computational ProblemSolving Focus, Wiley India Edition, 2013.  
Evaluation Pattern
 
MEA331N  INTERNATIONAL ECONOMICS (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

The course provides a deep understanding about the broad principles and theories, which tend to govern the flow of trade in goods, services and capital — both shortterm and longterm — at the global level. The contents of the course help them to examine the impact of the trade policies followed both at the national and international levels as also their welfare implications at macro level and the distribution of gains from trade to North and South with particular reference to India, also the likely consequences on income, employment and social standards and possible policy solutions 

Learning Outcome 

CO1: Identify and understand various trade theories, analyze the various types of restrictions of international trade. CO2: Analyze the links between trade, international finance, economic growth and globalization, with a particular emphasis on the experiences of developing countries. CO3: Analyze the relationship between Foreign Trade Theory and Economics Development. CO4: Critically evaluate the consequences of some of the International Trade policy. CO5: Critically comment on and participate in current debates on international economic policy. 
Unit1 
Teaching Hours:12 
Core Trade Models


Interregional versus international trade  Mercantilist doctrine of the balance of trade  Adam Smith and absolute advantage theory of trade  Ricardo and comparative advantage, its limitations  production possibility curve  Community indifference curve. The Standard Theory of International tradeThe PPC with increasing cost, Community Indifference Curves and Equilibrium, The Basis for and Gains from TradeSmall country with increasing cost, Demand and Supply, Offer Curves and Terms of Trade (ToT), Trade flows, Economic Geography and the Gravity model  
Unit2 
Teaching Hours:12 
HeckscherOhlin Theory and Empirics


Comparative advantage in Heckscher Ohlin Model  definitions of factor abundance  relationship between factor prices and commodity prices  Factor price equalization theorem  Factor intensity reversal  the empirical evidence on Heckscher Ohlin theorythe Leontief Paradox  
Unit3 
Teaching Hours:12 
New Trade Theories


Economies of Scale and International Trade, Imperfect Competition and International TradeTrade Based on Product Differentiation, Measuring IntraIndustry Trade, Trade Based on Dynamic Technological Differences Product Cycle Models, “New new” trade theory: Melitz Model and extensions  
Unit4 
Teaching Hours:12 
Trade Policies under Alternative Assumptions


The rationale of tariffs infant industry argument Partial equilibrium analysis of a Tariff, General Equilibrium Analysis of a Tariff in a Small Country The Stolper–Samuelson Theorem Effective Protection and Optimum Tariff, Nontariff Barriers quotas and subsidies, VER, Dumping, Economic IntegrationCustoms Unions and Free Trade Areas, Trade creation and Trade diversion , The Theory of second best  
Unit5 
Teaching Hours:12 
Balance of Payment and Exchange rate


The Balance of Payments and National Account; Accounting Balances and the Balance of Payments, Case Study: The BoP of India Exchange Rates: Meaning, Determinants, Equilibrium in FX market, Spot and Forward Rates, Currency Swaps, Futures, and Options, Purchasing Power Parity; Sluggish Price and Overshooting Exchange Rate Model; Effect of Interventions in the Foreign Exchange Market, The ExchangeRate Regime Choice and a Common Currency Area: Policy Assignment Problems; International Policy Coordination International Trade & Financial Organizations The International Trading and Monetary System: Past, Present, and Future, The Role of the IMF, WTO, and Other International Financial Organizations  
Text Books And Reference Books: 1. Krugman, P. R., Obstfeld, M. & Melitz, M.(2018). International Economics: Theory and Policy (11th ed.). 2. Grossman, G. M. & Rogoff K. (1997). Handbook of International Economics (Vol. 3). 52 NorthHolland.  
Essential Reading / Recommended Reading 1. Feenstra, R. C. (2004), Advanced InternationalTrade:Theory andEvidence. Princeton University Press. 2. Leamer, E.(2001). International Economics. Worth Publishers. 3. Markusen, J. R., Melvin, J. R., Kaempfer, W. H. & Maskus, K. E. (1995). International Trade: Theory and Evidence. McGrawHill. 4. Sodersten, B. & Reed, G.(1994). International Economics (3rd ed.). Macmillan. 5. Appleyard, D. &Field, J. (2013). International Economics. McGrawHill. 6. Vanags, A. (2001). International Economics. University of London, Subject Guide. 7. Ethier, W.(1997) Modern International economics (3rd ed.). W.W. Norton &Co. 8. Winters, A. P.(1991) International Economics (4th ed.). Routledge. 9. Bhagwati, J. N. (1987). International Trade: Selected Readings (2nd ed.). Cambridge,MA: MIT Press. 10. Bhagwati, J. N., Panagariya, A. & Srinivasan, T. N. (1998). Lectures on International Trade (2nd ed.). MIT Press. 11. Cline, W. R. (1997). Trade and Income Distribution. Institute for International Economics. 12. Cohen, S. D., Blecker, R. A. &Whitney, P. D.(2003).Fundamentals ofU.S. ForeignTrade Policy: Economics,Politics, Laws, and Issues (2nd ed.). Westview. 13. Collins, S. M. (Ed.) (1998). Imports, Exports, and the American Worker. Brookings Institution. 14. Dosi, G., Pavitt, K. & Soete, L. (1990). The Economics of Technical Change and International Trade. NYUPress. 15. Findlay, R. (1995). Factor Proportions, Trade, and Growth. MIT Press. Foray, D. & Freeman, C. (Eds.) (1993). Technology and the Wealth of Nations: The Dynamics of Constructed Advantage.Pinter  
Evaluation Pattern  
MEA332N  ECONOMICS OF GROWTH AND DEVELOPMENT (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

The course discusses about fundamental models used to analyse theoretical and empirical issues in economic growth and development. The main objective of the course is to familiarize students with the problem of development in underdeveloped and developing economies. In addition, this course also discusses the major theoretical developments in areas of Growth economics and policy discourses. 

Learning Outcome 

CO1: Use both classical and modern theories of growth and development to analyze the problems of the developing world. CO2: Understand the roles of population growth and human capital in the development problem. CO3: Analyze macroeconomic policies aimed at facilitating development and their implications. CO4: Use the tools developed in this course to analyse the development problems of selected nations. 
Unit1 
Teaching Hours:10 
Introducing Economic Development


How the Other Half Live; Economics and Development Studies; What Do We Mean by DevelopmentTraditional Economic Measures; The New Economic View of Development; Amartya Sen’s “Capability” Approach; Development and Happiness ; Three Core Values of Development;; The Three Objectives of Development. The Millennium Development Goals; Sustainable Development Goals.  
Unit2 
Teaching Hours:16 
Classic Theories of Economic Growth and Development


Four Approaches of classical theories of growth; Development as Growth and the Linear Stages Theories ; Rostow’s Stages of Growth; The Harrod Domar Growth Model; Obstacles and Constraints Necessary versus Sufficient Conditions: Some Criticisms of the Stages Model; StructuralChange ModelsThe Lewis Theory of Development;:Structural Change and Patterns of Development; The InternationalDependence Revolution The Neocolonial Dependence  
Unit3 
Teaching Hours:16 
The Neoclassical Models of Growth


Growth Models with Exogenous Saving Rates (the Solow–Swan Model); The Fundamental Equation of the Solow–Swan Model; The Steady State; The Golden Rule of Capital Accumulation and Dynamic Inefficiency; Ramsey model with infinitely lived agents Application to household behaviour and interaction with the government  Ricardian equivalence; Models of Endogenous GrowthTheoretical Dissatisfaction with Neoclassical Theory The AK Model  Long run AK model – AK model with externalities New Growth theory: Human capital, Externalities and ideas; endogenous technological progress and development.  
Unit4 
Teaching Hours:8 
Contemporary Models of Development and Underdevelopment


Theories of endogenous growth with special reference to Romer‘s modl, underdevelopment as coordination failure, multiple equilibria, the big push theory and Lebenstence Theory of Critical Minimum Efforts.  
Unit5 
Teaching Hours:10 
Empirical studies of regional datasets


Two concepts of convergence Convergence across the US states, Japanese Perfectures, European regions and other regions across the world recent research works  
Text Books And Reference Books: 1. Barro, R. J. & X. SalaiMartin (2003). Economic Growth (2nd ed.). MIT Press. 2. Todaro, M.P. & Smith S.C. (2015). Economic Development (12th ed.). AddisonWesley. 3. Meier, G. M. & James E. R. (2005). Leading issues in Economic Development (8th ed.). Oxford University Press. 4. Thirlwall, A.P. (2006). Growth and Development (8th ed.). Palgrave Macmillan.
 
Essential Reading / Recommended Reading 1. Ray, D. (2009). Development Economics. Princeton University Press. 2 . Pomeranz, K. (2000). The Great Divergence: China, Europe and the Making of the Modern World. Princeton University Press.  
Evaluation Pattern CIA 1 20 CIA 2 25 CIA 3 20 ESE 30 Attendance 5  
MEA333N  APPLIED ECONOMETRICS (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

The course covers Multiple Regression, Model Specification, Qualitative Response Models, Time Series Econometrics and Panel Data Econometrics with a focus on applications in the field of economics in general. The course is meant to equip students with Applied part of the subject matter. The objective of the course to develop a comprehensive set of tools and techniques for analyzing various forms of Crosssectional, Time Series and Panel Data frameworks. 

Learning Outcome 

CO1: Understand core concepts and methods used in the estimation of economic relationships. CO2: Demonstrate the analytical and critical skills relevant to economic thinking. CO3: Apply econometric software packages to employ various techniques taught using various types of data. CO4: Interpret and critically evaluate applied work and econometric findings. 
Unit1 
Teaching Hours:15 

A Review of Regression Analysis


Simple and multiple linear regression model – Assumptions; OLS and properties of estimators; GaussMarkov theorem; partial regression coefficients; The coefficient of determination r2 and the adjusted r2. Hypothesis testing  The ConfidenceInterval Approach, The TestofSignificance Approach, and the pvalue approach.  
Unit2 
Teaching Hours:10 

Model Specification and Diagnostic Testing


Model selection criteria, types of specification errors, omission of relevant variables, inclusion of irrelevant variables, incorrect functional form, errors in measurement, tests of specification errors.  
Unit3 
Teaching Hours:10 

Qualitative response models


The Nature of Qualitative Response Models, The Linear Probability Model (LPM), applications of LPM, alternatives to LPM – Logit and Probit models.  
Unit4 
Teaching Hours:15 

Time Series Econometrics


Nature of the time series data, stationarity, testing stationarity graphical analysis, correlogram, unit roots tests  ADF and PP, Spurious regression, Cointegration and Error Correction Mechanism.  
Unit5 
Teaching Hours:10 

Panel Data Econometrics


Nature of Panel data, Panel data models and estimation techniques: pooled OLS regression, fixed and random effects models.  
Text Books And Reference Books: Koutsoyiannis, A. (1979). Theory of Econometrics (2nd Ed.) Palgrave Macmillan. Maddala, G. S. (1992) Introduction to Econometrics (2nd Ed.) Macmillan Publishing Company. Gujarati, D. N., Porter, D.C., & Gunasekar, S. (2017). Basic Econometrics. (5th ed.). McGrawHill.  
Essential Reading / Recommended Reading RA Greene, Econometric Analysis, Macmillan, 1993. Wooldridge, J. M. (2014). Introductory Econometrics: A Modern Approach (4th ed.). New Delhi: Cengage Learning  
Evaluation Pattern
 
MEA341N  BEHAVIORAL ECONOMICS (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 



Learning Outcome 

CO1: An understanding of the theoretical and empirical underpinnings of behavioural economics. CO2: Demonstrate how we can meaningfully predict and influence human behaviour 'for good'. CO3: Examine applications and case studies from real world policy settings. CO4: Develop a methodology, mindset, and framework to design and implement behavioural change techniques to policy problems. 
Unit1 
Teaching Hours:10 

Introduction


 
Unit2 
Teaching Hours:10 

Making Choices Under Risk: Prospect Theory


 
Unit3 
Teaching Hours:10 

Inter temporal choice


 
Unit4 
Teaching Hours:10 

Strategic interaction


 
Unit5 
Teaching Hours:10 

Nudges, Policy and Happiness


 
Unit6 
Teaching Hours:10 

Animal Spirits


 
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern CIA120 Marks CIA225 Marks CIA320 Marks Attendance5 Marks ESE30  
MEA371N  APPLIED MACHINE LEARNING (2021 Batch)  
Total Teaching Hours for Semester:90 
No of Lecture Hours/Week:6 

Max Marks:150 
Credits:5 

Course Objectives/Course Description 

This course enables students to ● Understand the differences between supervised and unsupervised machine learning models ● Optimize the models and understand the effect of algorithm parameters’ modification ● Combine various models and create strategies to overcome commonly faced challenges in machine learning algorithm implementation ● Implement machine learning models in various economicsrelated applications 

Learning Outcome 

CO1: Understand the basic concepts, applications and different types of learning in respect of Machine Learning Algorithms. CO2: Apply various supervised and unsupervised algorithms to various datasets and analyze the impact of hyperparameter tuning. CO3: Compare and evaluate the performance of machine learning algorithms. CO4: Evaluate advanced machine learning models with respect to benchmark discoveries and applications. CO5: Create machine learning models to facilitate the application needs in economics domain. 
Unit1 
Teaching Hours:10 
INTRODUCTION TO MACHINE LEARNING


Learning: Human Comprehension, Cognition, Past Experiences, Predicting Future, Thought Process, Inputs and Output System, Human Memory, Information, Machine Comprehension of Information, Data, Representation of Data, Data Processing, Data Storage, Data Processing, Types of Data: Structured, Unstructured, SemiStructured, HumanMachine Mapping of Peripherals Machine Learning: Definition, Objectives, Components, Features, Applications. Traditional Programming v/s Machine Learning, Types of Machine Learning: Supervised, Unsupervised, Semisupervised and Reinforcement Learning, Predictive Models, Techniques, Statistical Inferences Subsets of Machine Learning: Natural Language Processing, Image Processing, Computer Vision, Robotics, Export Systems, Neural Networks, Deep Learning, Generative Networks, Big Data Analysis, ASR, Texttospeech, Extreme Learning Machines, Genetic Algorithms, Optimization Problems, Latest Advancements in Artificial Intelligence and Machine Learning, Learning from Examples  
Unit2 
Teaching Hours:15 
SUPERVISED LEARNING AND PROBABILISTIC APPROACHES


Decision Functions, Distance Measures, Outliers, Gradient Descent, Probably Approach Correct (PAC) Learning Regression and Classification Problems, Linear Regression, KNN for Regression, Types of Classification Methods, MultiClass and Multiple Class Classification, Logistic Regression, DistanceBased Classification, KNearest Neighbors, Histogram Estimators, Naive Bayes Classifier, Decision Trees, Support Vector Machines, Generalization to Multivariate Data, Uncertainty in Multiclass Classification, Notes on Imbalanced Classification Probabilistic Approaches in Machine Learning, Maximum Likelihood Estimation, Bernoulli Density, Multinomial Density, Gaussian Density, Bayes Estimator, Parametric Classification.  
Unit3 
Teaching Hours:15 
UNSUPERVISED LEARNING, DIMENSIONALITY REDUCTION AND ENSEMBLE LEARNING


Types of Unsupervised Learning, Challenges in Unsupervised Learning, Distance Measures in Clustering, Elbow Method, Types of Clustering: Connectivitybased, Centroidbased, Distributionbased, Densitybased, Fuzzy Clustering, and Constraintbased Clustering, KMeans Clustering, Spectral Clustering, Hierarchical Clustering, DBSCAN, Mixture Densities, ExpectationMaximization Algorithm, Comparison of Clustering Algorithms, Supervised Methods after Clustering Subset Selection, Principal Component Analysis (PCA), Independent Component Analysis, Linear Discriminant Analysis (LDA), Using Low Variance and High Correlation Filter, Multidimensional Scaling, Random Forest, Backward Feature Elimination, Forward Selection, Isomaps, Singular Value Decomposition, Applications of Dimensionality Reduction Combining Multiple Learners: Ensemble Learning, Voting, Weighted Averages, Stacking, Bagging, Boosting, AdaBoost, CatBoost.  
Unit4 
Teaching Hours:10 
STRATEGIES TO OVERCOME CHALLENGES


Challenges in ML: Insufficient Quantity of Data, Nonrepresentative and Poor Quality Data, Irrelevant Features, Estimation of Missing Values, Parameter Estimation Preparing Data for ML Algorithms: Generalization, Normalization, Sampling, Overfitting, Underfitting, Bias, Variance, BiasVariance Tradeoff, Relation between Bias, Variance Overfitting and Underfitting, Learning Noise in a Dataset, Ideal Machine Learning Model, Process of Model Selection, Effect of Data Preparation on Results, Encoding, Nominal and Ordinal Representation, Feature Transformation and Feature Engineering, Feature Selection  Various Methods Testing and Validating: Performance measures  Confusion matrix, PrecisionRecall Tradeoff, F1 Score, ROC Curve, Error Analysis, Changing Parameters of Algorithm, Hyperparameter Tuning, Model Selection, Algorithm Tuning  
Unit5 
Teaching Hours:30 
MACHINE LEARNING IN ECONOMICS


Evaluation of TimeSeries Data, Big Data Analysis, Stock Exchange Analysis, Role of Recurrent Neural Networks in Time Series Data Prediction, Modelling Supply and Demand Functions, Forecasting, Identifying Loan Defaulters, Decision Making in Financial Sectors, Impact in Agricultural Economics Lab Programs Hours: 1. Introduction to Machine Learning Libraries in Python 2. Data Exploration and Prediction of Numeric Values 3. Classification  Logistic Regression 4. Classification  KNN, Naive Bayes 5. Classification  Support Vector Machines 6. Clustering  K Means, Elbow Method 7. Clustering  Agglomerative Clustering 8. Dimensionality Reduction  PCA and LDA 9. Analysis of Time Series Data  Stock Market Prediction 10. Analysis of Supply and Demand Based Applications 11. Identifying Insurance/Loan Defaulters 12. Geographic Location based Data Analysis  
Text Books And Reference Books: 1. Müller, A. C. & Guido, S. (2017). Introduction to Machine Learning with Python A Guide For Data Scientists. O’Reilly book. 2. Alpaydin, E. (2005). Introduction to Machine Learning. Prentice Hall of India. 3. Russell, S. & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.  
Essential Reading / Recommended Reading 1. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press. 2. Hastie, Tibshirani & Friedman (2008). The Elements of Statistical Learning (2nd ed.). Springer. 3. Basuchoudhary, Atin, Bang, James, T., Sen, Tinni (2017). Machinelearning Techniques in Economics. Springer International Publishing.  
Evaluation Pattern CIAs only  
MEA372N  DATA VISUALIZATION (2021 Batch)  
Total Teaching Hours for Semester:75 
No of Lecture Hours/Week:5 
Max Marks:150 
Credits:4 
Course Objectives/Course Description 

The course enables students to ● Understand and Analyze Visualization case studies ● Understand the nuances of Data Visualization ● Analyze data using Tabular Reports and Charts ● Catch and Debug data errors ● Visualize Data to derive actionable insights ● Create reports based on specifications ● Study patterns in data using charts ● Derive Actionable insights from raw data 

Learning Outcome 

CO1: Import data and prepare data in Tableau. CO2: Conceptual understanding of charts and functions. CO3: Visualise/Analyse Data using different types of Charts. CO4: Create dashboards in Tableau. CO5: Explain and Present data insights using Visualization Techniques. 
Unit1 
Teaching Hours:9 
Working with Data in Tableau


Foundational principles of Tableau, basics of connecting to data, exploring and analyzing the data visually, examine and filter data, clean and shape data, joins and blends. Connecting to data sets, data section and worksheets  
Unit2 
Teaching Hours:9 
Data Visualization Best Practices


Data Science overview, Data Visualization, modelling, data preparation, communication and presentation. Descriptive, predictive and prescriptive analytics. Techniques for making Data Visualization useful and beautiful. Case Study: (Minard’s Map, Broadstreet Cholera Map, Interactive Government Budget, US Population study, Film Dialogue, Selfie City)  
Unit3 
Teaching Hours:9 
Tableau Fundamentals


Marks and Filters, Charts Types: Scatter, Bar, Line, Gantt, Heat Map, Tree Map, creating calculated field, dates in tableau, jittering, multiple mark types, and dual axis chart.
 
Unit4 
Teaching Hours:9 
Tableau Advanced


Working with Time Series, Understanding Aggregation and Granularity, Area Charts, Highlighting and Filters, Animation Additional Charts: Pareto Chart, Box and Whisker plot, Map Chart, Calculate Z Score, Population Pyramid  
Unit5 
Teaching Hours:9 
Tableau Interactive Dashboard


Trends, Clustering, Distributions, and Forecasting, enhance your data visualizations with statistical analysis. Trend models, clustering, distributions, and forecasting Telling a Data Story with Dashboards, demonstrates how Tableau allows you to bring together related data visualizations in a single dashboard. Static view of various aspects of the data, or a fully interactive environment (dynamically filter, drill down, and interact with the data visualizations)  
Unit6 
Teaching Hours:30 
Lab Programs


1. Install Tableau and create different kind of basic charts on existing dataset in Tableau (like Superstore) Scatter, Bar, Line, Gantt, Heat Map, Tree Map 2. Create advanced charts on existing dataset Pareto Chart, Box and Whisker plot, Map Chart, Population Pyramid 3. Design and auto generate a dataset on an assigned domain (using Python or Excel) 4. Identify key reports in the assigned domain, Create and visualisekey reports 5. Creating a Dashboard to support a business case, use a couple of calculated fields in the same 6. Designing a Key Performance Indicator (KPI) (eg. Best Striker in Football in EPL, Team Ranking in Cricket) 7. Telling a story using visual reports and a dashboard 8. Collate all work done in this course into a single pdf based report  
Text Books And Reference Books: 1. Milligan, Joshua N(2019). Learning Tableau 10. Packt Publishing Ltd,. 2. Sleeper, Ryan(2018). Practical Tableau: 100 tips, tutorials, and strategies from a Tableau Zen master. " O'Reilly Media, Inc."
 
Essential Reading / Recommended Reading 1. Few, Stephen(2003), "Information dashboard design." 2. Knaflic, Cole Nussbaumer(2019),Storytelling with Data: Let's Practice!. John Wiley & Sons, 3. Ware, Colin(2010). Visual thinking for design. Elsevier  
Evaluation Pattern CIAs only  
MEA381PN  SPECIALIZATION PROJECT (2021 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:2 
Course Objectives/Course Description 

The course is designed to provide a realworld project development and deployment environment for the students. 

Learning Outcome 

CO1: Identify the problem and relevant analytics for the selected domain. CO2: Apply appropriate design/development strategy and tools. 
Unit1 
Teaching Hours:30 
INTRODUCTORY PHASE


· Project Topic identification
· Identify the contributing research papers, domain of work and specialization concept to be implemented  
Unit2 
Teaching Hours:30 
PROJECT EVALUATION PHASE


Final Report preparation Final Report Submission  
Text Books And Reference Books: NA  
Essential Reading / Recommended Reading NA  
Evaluation Pattern Evaluation Pattern CIA only  
MEA481N  INDUSTRY INTERNSHIP (2021 Batch)  
Total Teaching Hours for Semester:0 
No of Lecture Hours/Week:0 
Max Marks:300 
Credits:10 
Course Objectives/Course Description 

The Vision of Christ University is “Excellence and Service” and this can be achieved through the holistic development of individuals enabling effective contribution to society. Christ University provides the nurturing ground for all stakeholders to realize academic, personal, interpersonal and societal growth and upliftment. Industry internship focuses on learning by doing and making students more responsible and dynamic so that they can harness their hidden potential and get ready to take up tasks and challenges of the industry with confidence and motivation. Industry Internship provides students with exposure to life beyond academics enabling them to solve reallife problems. It provides students with practical knowledge of the application of Economics and Analytics in the industry and also the importance of discipline, hard work and dedication. This internship aims to widen the horizons of the students to make informed decisions regarding their future and career. The students shall learn new skills and make good professional interpersonal relationships. The skills learned during the Industry Internship will also have a bearing on students’ placements and career planning. Internship Report: The student shall work with the organization as an intern for a period of 4 months and submit an internship report of 2000 words in consultation with the allotted faculty guide. The student shall ensure regular weekly contact with the faculty guide during the entire period of the internship. The student will report to the faculty guide every week and apprise him/her of the weekly progress of the internship. In addition to the submission of the internship report, the student will also present his internship report before a panel of examiners followed by a Question and Answer session. 

Learning Outcome 

CO1: To apply the knowledge of Economics and Analytics to undertake various tasks and duties assigned in the industry CO2: To acquire industryspecific skills through practical experience, research and experiential learning. CO3: To develop personal, interpersonal and societal skills. 
Unit1 
Teaching Hours:0 

NA


NA  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading NA  
Evaluation Pattern
 
MEA482N  RESEARCH PUBLICATION (2021 Batch)  
Total Teaching Hours for Semester:0 
No of Lecture Hours/Week:0 

Max Marks:100 
Credits:2 

Course Objectives/Course Description 

Research Publication enables students to raise their intellectual abilities and contribute to the existing literature through research. It also enables them to dive deeper into researchable problems and come up with novel ideas and bring them to the forefront by publishing them as a research article/paper in esteemed research journals. The student will be allotted a faculty guide to supervising the research work. The students are expected to undertake quality research in consultation with the faculty guide. The student will be in regular contact with the faculty guide and try to complete the research work in the stipulated time. The faculty guide will guide the student in all matters related to the finalization of the topic, writing of the research article/paper, analysis of data, selection of the journal and communications with the journal. 

Learning Outcome 

CO1: To apply the knowledge of Economics and Analytics to undertake and publish a research article/paper. CO2: To learn specific data analysis skills required for research in Economics and Analytics. CO3: To acquire academic writing skills required by journals for publication purposes. 
Unit1 
Teaching Hours:0 
NA  
Text Books And Reference Books: A Guide to Research and Publication Ethics A Text Book As per UGC Guidelines for UG, PG, MPhil and PhD, New Delhi Publisher  
Essential Reading / Recommended Reading NA  
Evaluation Pattern Publication in a reputed Journal 