Department of Management Studies

Zoom Link for Online Interview of short-listed candidates for Admission to MBA and MBA in Business Analytics Programme, Session 2021-23, Click Admission Portal: https://mbaism.formflix.com/. Kindly check the link for appearing in the online interview.       ||  Results of short-listing process for Online Interview for Admission to MBA and MBA in Business Analytics Programme, Session 2021-23, Click Admission Portal: https://mbaism.formflix.com/ . The instructions for online interviews to be conducted from 20th-24th March, 2021 are provided in the admission portal.

Business Analytics

The two-year program includes eleven core courses in the first and second semester. In the second semester the candidate has to choose a specialization paper from thirteen different specializations. Students have to undergo industrial training for six weeks at the end of second semester. In the third and fourth semester the student has to work on a Dissertation for the partial fulfillment of the program.

Course No. Course Name L T P C
Semester 1
MSC502/MSC503 Business Research Methods/ Business Statistics (Modular) (MS) 3 0 0 9
MSC506 Managerial Economics (MS) 3 0 0 9
MSC522 Data Mining for Business (MS) 4 0 0 12
MSC504/MSC505 Financial Accounting and Reporting /Cost and Management Accounting (Modular) (MS) 3 0 0 9
MCC532 Fundamentals of Machine Learning (M&C) 3 0 0 9
MCC537 Fundamentals of Machine Learning (Lab) (M&C) 0 0 2 2
MSC508 Business Analytics Lab (MS) 0 0 2 2

Total 15 0 4 52
Course No. Course Name L T P C
Semester 2
MSC510 Corporate Finance (MS) 3 0 0 9
MCOE6201 Stochastic Processes (M&C) 3 0 0 9
MCDC6201 Advanced DBMS (M&C) 3 0 0 9
MSC513/MSC514 Marketing Management (MS)/ Human Resource Management (MS) 4 0 0 12
MSC512 Operations Management (MS) 3 0 0 9
MSC523 Advanced Multivariate Analysis (MS) 0 0 3 3
MCDC6206 Advanced DBMS Lab (M&C) 0 0 2 2

Total 15 1 4 53
Course No. Course Name L T P C
Semester 3
MSC597 Thesis Unit 0 0 0 36
Course No. Course Name L T P C
Semester 4

Any five*
 Artificial Intelligence (CSE) (CSO303) 3 0 0 9
 Information retrieval (CSE) (CSD510) 3 0 0 9
 Big Data (MS)(MSD534) 3 0 0 9
 Missing Data Analysis and Survey Sampling (M&C) (MCDE6406) 3 0 0 9
 Time Series Analysis (M&C) (MCDE6401) 3 0 0 9
(Any One*)  Marketing Analytics (MS) (MSD522) 3 0 0 9
 Management of Self and Behavioural Analytics (MS) (MSD523) 3 0 0 9
 Operations Analytics (MS) (MSD525) 3 0 0 9
 Financial Econometrics (MS) (MSD514) 3 0 0 9
 Big Data Lab (MSD535) 0 0 2 2
Total
18 0 2 56
* Electives will be modified as per industry requirements.

Management Principles and Practices (MS) L T P C
3 0 0 9
Course Objectives: The course aims to equip students with basic principles and practices of Management
Theory and prepare them for Managerial positions in Organizations where they may work. The purpose of this course is to provide conceptual clarity and practical finesse so that they deftly handle managerial challenges that keep on arising in the VUCA (Volatile Uncertain Complexity and Ambiguous) business world.
Course Outcome: The students after completing this course will be able to understand the requirements
of the corporate world and able to address the demands that arise in the fast changing scenario.
Unit I Practice of Management: Definition and concepts, Complexities of the Business Environment and the Manager [6L]
Unit II The Evolution of Management Thought and Indian Ethos in Management, Recent advances in Management Practice, Recent Contributors to Management Thought [6 L]
Unit III Task & Responsibilities of a Professional Manager, Basic elements of Management Function, Management Styles [6L]
Unit IV Manager as a change agent, Managerial Decision Making, Cases [6L]
Unit V Corporate Social Responsibility, Corporate Governance, Cases [5L]
Unit VI Values and Ethics for Managers, the morality quotient imperative [5L]
Unit VII Organizational Theory: Definition, Dimensions of Organizational Structure, Types, Determinants, Organizational Design [5L]
Text Books:
1. Management by Koontz and O’Donnell 10th Edition, Tata McGrawhill,
2. Management by Stephen P. Robbins and Mary Coulter 11th Edition, Pearson Education.
References
In addition students would be advised to read selected articles and cases from HBR and other leading journals time to time.

Business Research Methods L T P C
3 0 0 9
Course Objectives: This course is specifically designed to meet the requirements of post graduate students by presenting a comprehensive overview of the conceptual background of research, and its processes and techniques used specially in business scenario.
Learning Outcomes: The students in their managerial capabilities will learn to analyze the business, economic, or social conditions and in-turn assists then in making major strategic decisions.
Unit I Introduction to business research: Research in business; Research process; Defining the research problem and developing an approach. [4L]
Unit II The designs of business research: Classification of designs; Exploratory studies- Secondary data analysis, Qualitative techniques; Descriptive studies- Surveys and observations; Causal studies- Experimental research designs. [6 L]
Unit III The sources and collection of data: Measurement concept; Measurement scales
; Questionnaire and instruments; Sampling design.
[6L]
Unit IV Analysis and presentation of data: Data preparation, Examination of data, overview of hypothesis testing.
]
[2L]
Unit V Report preparation and presentation [2L]
Text Books:
1. Business research Methods (12e Edition), Cooper, Schindler and Sharma (2019), ), Mc Graw
Hill Education. th
Marketing Research: An applied orientation (7 Edition), Malhotra and Dash, (2015), Pearson Pub.

Business Statistics (Modular) (MS) L T P C
3 0 0 9
Course Objectives: This course is expected to provide the student with the fundamentals of statistics which are related to the management and provide a basis for later topics which utilize these statistical concepts.
Learning Outcomes: To understand the basic concepts of statistical methods and their application to a business or an industrial engineering problem. Moreover, students are supposed to learn some application software like MS Excel, SYSTAT, SPSS for data analysis.
Unit I Introduction: the meaning and scope of statistics, some uses of statistical methods, statistical data Presentation of statistical data: tables, graphs and charts.
[2L]
[2L]
Unit II Summarization of statistical data: frequency distribution of observations, measures of central tendency, dispersion, skewness and kurtosis of distributions. [3L]
Unit III Probability: concepts, random variables; Probability Distributions: Binomial, Poisson, Normal distribution [2L]
Unit IV Sampling distributions; Estimation: Point and Interval Estimates of mean and proportion. [2L]
Unit V Testing hypotheses of mean and proportion: one/two-sample tests ; chi-square test; Analysis of variance (ANOVA) [6L]
Unit VI :Simple regression and correlation: Making inferences about population parameters
Introduction to Non-parametric tests
[3L]
Unit VII Introduction to Non-parametric tests [2L]
Text Books:
1. Statistics for Management, 7th edition, Levin & Rubin, Pearson Education Publication
2. Applied Statistics and Probability for Engineers , 6th edition, D. C. Montgomery and G. C. Runger,
John Wiley & Sons.

Managerial Economics (MS) L T P C
3 0 0 9
Course Objectives:
 This course deals with the application of microeconomics to the practical problems of businesses/firms in order to facilitate rational managerial decisions and possible solutions of managerial problems.
 This course helps students to identify, explain and adjust their decisions to the entire gamut of microeconomic context in which
 business would operate. It integrates economic theory with business practice for the purpose of facilitating planning and decision making.
Learning Outcomes:
 Understand the fundamentals of economics.
 Understand the demand and supply and measure its responsiveness to various factors .
 Differentiate between various costs of production.
 Analyze the four basic market structure models and how price and quantity are determined in each model.
Unit I Introduction to Economics- Scarcity and allocation of resources, Distinction between Microeconomics and Macroeconomics, Firm- meaning and objectives; Marginal Analysis and Time Value of Money. [10L]
Unit II Utility Analysis- consumer’s budget constraint, utility maximization; Demand and supply analysis, Government intervention (floor price, ceiling price, tax, etc), Consumer and producer surplus, Price, income and cross elasticity. [10 L]
Unit III Production and Cost analysis-Production functions, production and cost in the short run and long run, Economies of scale and scope, Market Analysis-types of market, profit maximization under perfect competition, monopoly, monopolistic competition, and oligopoly [10 L]
Unit IV Price Discrimination, Pricing Strategies- cost-plus pricing, peak load pricing, product bundling, two-part tariffs, Public Goods and Externalities,
Asymmetric Information, Economics of Uncertainty and Risk .
[10 L]
Text Books:
1. Managerial Economics and Business Strategy, 8th Edition, Michael R Bay & Jeff Prince, McGraw Hill Education (2017).
Microeconomics: Theory and Applications with Calculus, 3rd Edition. Jeffrey M. Perloff, Pearson Education (2017).

Data mining L T P C
3 0 0 9
Objective: This course will provide the learning of the data mining and its importance in business
Outcome: Students will gain the learning on fundamentals of datamining.
Unit I Data warehouse and OLAP Technology: a multidimensional data model, data warehouse architecture [7L]
Unit II Introduction: Data mining functionalities, classification and integration, major issues in data mining, supervised, semi-supervised and unsupervised data, challenge in dealing with the data with data mining. [6L]
Unit III Data pre-processing: data summarization, data cleaning, data integration and transformation and data reduction, feature selection and feature subset selection. [7L]
Unit IV Classification, Clustering and Association Mining Techniques, Interpretation of confusion matrix. [12L]
Unit V Text mining and concepts of natural language processing, Applications and Trends in Data Mining,. [7L]
Book:
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, by Galit Shmueli, Peter C Bruce, Inbal Yahav, Nitin R Patel, Kenneth C. Lichtendahl Jr., ISBN: 978-1-118-87936-8 (2017 or latest)
References :
Data Analytics Using R (Seema Acharya), McGraw hills Publishers

Financial Accounting and Reporting L T P C
3 0 0 9
Course Objectives: This course will enable students to understand the financial reporting system in a corporate framework. The students will be able to appreciate the usefulness of financial information for decision making and equipped with tools to analyze the financial performance of companies.
Learning Outcomes:
1. To understand the concept of Financial Accounting and Reporting in corporate sector.
2. To develop understanding about the financial statements and tools and techniques for analysis of financial statements.
3. To understand the process of financial decision-making using the financial statements.
Unit I Introduction to accounting, Accounting equation, Preparation of financial statements, Accounting Standards – US GAAP, Indian GAAP, IFRS, Accounting Cycle, Journal, Ledger, Trial Balance, Final Accounts [14L]
Unit II Introduction to Accounting for Inventories, Accounting for Receivables, Fixed Assets, Depreciation and Amortization, Accounting for Liabilities, Accounting for Shareholder Equity [3L]
Unit III Statement of Cash Flows, Analysis of Financial Statements, Comparative Statements, Common Size Statements, Ratio Analysis, Du-Pont Analysis [4L]
Text Books:
1. Financial Accounting – A managerial Perspective by R. Naryanswamy, PHI
2. Financial Accounting for Management by Ramchandran & Kakani, Tata McGraw Hill
References
1. Financial Accounting – Reporting & Analysis by Stice & Stice, Thomson, South Western Case Studies - to be provided by the instructor

Cost and Management Accounting (Modular) (MS) L T P C
3 0 0 9s
Course Objectives: This course will help students appreciate the usefulness of cost and management accounting in management decision making. The students will be equipped with tools to trace the cost and estimate the cost of the product or service as the case may be.
Learning Outcomes:
1. To understand the concept of Cost and Management Accounting in business decision making.
2. To develop understanding about the various elements of cost and their classification along with analysis of the various cost structures.
3. To understand the various tools and techniques used in cost management
Unit I Introduction to Cost and Management Accounting, Cost Concepts-Absorption Costing [3L]
Unit II Marginal Costing and CVP Analysis [3L]
Unit III Job Costing - system basic job costing for Manufacturing and Service Companies, Activity Based Costing - Concept, System & Limitations, Preparation of Budget and Budgetary control [12L]
Text Books:
1. Cost Accounting – A managerial Perspective by Horngren, Datar & Foster Pearson
2. Management Accounting - Text, Problems & Cases by Khan & Jain, Tata McGraw Hill
References
1. Handbook of ICAI
Case Studies - to be provided by the instructor

Fundamentals of Machine Learning (M&C) L T P C
3 0 0 9
Objective: All Data Analytics applications are depended on machine learning techniques. This course will provide the students an exposure about how to use machine learning techniques in Data Analytics. Outcome: Students will learn the use of Machine Learning in Data Analytics.
Unit I Classification/Regression techniques such as Naive Bayes', decision trees, SVMs
Unit II Boosting/Bagging and linear and non-linear regression, logistic regression, maximum likelihood estimates, regularization, basics of statistical learning theory
Unit III Perceptron rule, multi-layer perceptron, backpropagation, brief introduction to deep learning models
Unit IV Dimensionality reduction techniques like PCA, ICA and LDA
Unit V Unsupervised learning: Clustering, Gaussian mixture models, Some case studies
References:
1. Kevin P. Murphy and Francis Bach, Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
2. Shai Shalev-Shwartz and Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms

Fundamentals of Machine Learning (Lab) (M&C) L T P C
0 0 3 3
Objective: Data Structures is the basic course of Computer Science. It is required in every field of Computer Science. Objective of this course is to impart knowledge of Data Structures.
Outcome: Students will learn how to implement machine learning techniques using R/Python
Lab I Application of Regression Analysis in Data Analytics.
Lab II Application of Decision Tree and SVM in Data Analytics
Lab III Application of Nonlinear Regression in Data Analytics
Lab IV Problems related to Logistic Regression in Data Analytics
Lab V Application of Statistical Learning in Data Analytics
Lab VI Application of applications of Neural Network and deep Learning in Data Analytics
Lab VII Application of PCA in Data Analytics
Lab VIII Application of ICA and LDA in Data Analytics
Lab IX Application of Supervised Learning in Data Analytics
Lab X Application of Clustering Techniques in Data Analytics
Book
1. B. Lantz, Machine Learning with R, Packt Publishing Limited, 2013
References:
1. P. Mueller and L Massaron, Machine Learning (in Python and R), John Wiley & Sons, 2016

Business Analytics [Lab] L T P C
0 0 2 2
Course Objectives: Enable students for using the computer program s like MS Excel, SPSS for statistical problems.
Learning Outcomes: On successful completion of this course, student should be able to solve statistical problems using computer
Unit I Introduction, Getting Acquainted with Microsoft Excel and SPSS, Using Statistics in Excel, Descriptive Statistics—Central Tendency,
Descriptive Statistics—Variability, Development of charts and plotting in excel
[6P]
Unit II Problems on different Probability Distributions: Binomial, Poisson, Normal distribution using software [9P]
Unit III Problems on t-test , Z-test, ANOVA and Chi-square test , Hypothesis testing and cases using software [12P]
Unit IV :Correlation, simple and multiple regression concept and problems using software
]
[6P]
Unit V Factor Analysis concept and application using software [6P
References:
Text Books:
1. Understanding Educational Statistics Using Microsoft Excel and SPSS, Martin Lee Abott , Wiley
, 2011
2. Statistics for Managers Using Microsoft Excel (5th Edition)David M. Levine David F. Stephan Timothy C. Krehbiel Mark L. Berenson, Prentice Hall, 8th Edition
Reference Books
1. Statistics for Management, 7th edition, Levin & Rubin, Pearson Education Publication
2. Applied Statistics and Probability for Engineers , 6th edition, D. C. Montgomery and G. C. Runger,
John Wiley & Sons.

Corporate Finance L T P C
3 0 0 9
Course Objectives:
 This course will help students to understand the significance of financial management and the role of finance manager in the organization. The students will be equipped with all tools and techniques for efficient financial management in the organization.
Learning Outcomes:
 To understand the concept of financial management in decision making process.
 To develop understanding about the various tools and techniques used for efficient financial management.
 To understand the role of finance manager in the corporate sector.
Unit I Introduction to Financial Management, Objectives, Agency Problem; Concept of Time Value of Money, Capital Markets, Sources of Capital,
Cost of Capital
12L
Unit II Basics of Capital budgeting: Principles and Techniques. 8L
Unit III Leverage: Operating, Financial and Combined Leverage, Introduction to Optimal Capital Structure, Designing capital structure Management of Working Capital: Determinants, computation & working capital financing, Introduction to Dividend Decisions: factors, Bonus Shares & Stock Splits. 19L
Text Books:
1. Financial Management (11th Edition)- By I.M.Pandey – Vikas Publishing
2. Financial Management – Texts, Problem & Cases (7th Edition) – By Khan & Jain – Tata McGraw Hill
References
1. Financial Management (9th Edition) – By Prasanna Chanadra – Tata McGraw Hill
2. Handbook of ICAI
Case Studies - to be provided by the instructor

Stochastic Processes L T P C
3 0 0 9
Course Objectives:
The objective of the course will be to give idea to the students about various Stochastic Processes.
Learning Outcomes:
 This course will be useful for analysis of different financial market data, Business data.
.
Unit I Definition and classification of general stochastic processes. Markov Chains: definition, transition probability matrices, classification of states, limiting properties.
Learning Outcome
This unit will help students to understand basics of Stochastics Processes and Markov Chain.
9L
Unit II Chains with Discrete State Space: Poisson process, birth and death processes. Renewal Process: Renewal Process when Time is discrete, Renewal Process when Time is continues, Renewal Theory and Analysis. Learning Outcome
This unit will help students to understand different type of Point processes and their applications.
11L
Unit III Markov Process with Continuous State Space: Introduction to Brownian motion, Wiener Process, Differential equation of Wiener Process, Kolmogorov Equations.
Learning Outcome
This unit will help students to get the concept different continuous State processes with application in finance.
9L
Unit IV Markov Decision Process, Branching Process
Learning Outcome
This unit will help students to get the concept of Markov decision process and its applications.
6L
Unit V Congestion Process: Queuing Process, M/M/1 Queue
Learning Outcome
This unit will help students to get the concept of Queuing theory and its application.
4L
Reference Books:
Stochastic Processes by J. Medhi, New Age International Publication. Elements of Applied Stochastic Processes by U.N. Bhat, John Wiley and Sons.
Probability and Statistics with Reliability, Queuing, and Computer Science Applications by K.S. Trivedi, Prentice Hall of India.

Advanced DBMS L T P C
3 0 0 9
Course Objectives:
 To provide knowledge of advanced
Learning Outcomes:
 They will able design data bases used for Data Analytics.
Unit I Object oriented model: Nested relations, modelling nested relations as object model, extension of SQL, object definition and query language (ODL, OQL), object relational database model, storage and access methods. Active databases, Advanced trigger structures, SQL extensions. 9L
Unit II Security and Integrity: Discretionary and mandatory access control; Facilities in SQL, access control models for RDBMS and OODBMS. Distributed Database: Basic Structure, fragmentation algorithms, trade-offs for replication, query processing, recovery and concurrency control; Multi- database systems; Design of Web Databases. 11L
Unit III Data Mining and Warehousing: Association Rule algorithms, algorithms for sequential patterns; Clustering and classification in data mining; Basic structure of a data warehouse; Extension of ER Model, materialistic view creation. 11L
Unit IV On line analytical processing and data cube. Deductive databases, recursive query construction, logical database design and data log. 4L
Unit V One or more of the following topics: (i) Temporal database, (ii) Multimedia database, (iii) Text retrieval and mining, (iv) Web mining, and
(v) Any topic of current interest.
4L
Reference Books:
Database System Concepts, Korth, Silberschatz and Sudarshan, McGraw Hill. Database: Principles, Programming, Performance, P. O'Neil, Morgan Koffman
Principles of Database and Knowledge-Base Systems, J.D. Ullman, Computer Science

Marketing Management L T P C
3 0 0 9
Course Objectives:
The course provides a complete perspective on marketing insights, environment and various strategies relevant to the current marketing environment.
Learning Outcomes:
 The students will learn to apply knowledge, concepts, tools to understand challenges and issues in global marketing environment.
Unit I Marketing Concepts; Approaches to Marketing; Marketing Mix; Functions of Marketing; Marketing Environment, The changing marketing
environment
8L
Unit II Consumer Behaviour; The Marketing Process, Market Segmentation, Targeting and Positioning; B2B and B2C marketing 8L
Unit III New Product Development; Product Life Cycle . 8L
Unit IV Physical Distribution – Importance and role of various channels of distribution in marketing 7L
Unit V Integrated Marketing Communication; Branding; Online Marketing; Pricing strategies; Recent Trends in Marketing 8L
Reference Books:
1. Marketing Management- Kotler, Keller, Koshi and Jha- Pearson Pub.
Marketing Management- Rajan Saxena- Tata McGraw Hil

Operations Management L T P C
3 0 0 9
Course Objectives:
 This course introduces the students to the theory and practice of operations management as a functional area in the management of business enterprise. It also includes the methods, strategies and application of various mathematical tools in solving the production and operation related problems. The objective is to understand the strategic role of operations management in creating and enhancing a firm’s competitive advantages
Learning Outcomes:
 At the end of the course the students will be able to:
 (a) acquire a working understanding of the roles/functions of operation management in the context of business enterprise;
 (b) develop skills in solving operation management problems;
 (c) recognize, appreciate, and perform the job of a competent production or operation manager.
Unit I Introduction to Operations Management and basic concepts. Basic forecasting concepts and related models like Moving Average,
Exponential Smoothing and Regressions. Concepts of forecasting error.
8L
Unit II Concept of Aggregate Production Planning and related strategies like Chase, level and Mixed. Basic Concept of Materials requirement Planning and numerical problems. 8L
Unit III Job Shop Scheduling and sequencing strategies, Johnson Rule and Extension of Johnson Rule. Introduction to inventory management. Basic inventory models and problems. Case studies 8L
Unit IV Facility Layout and various algorithms, Facility Location theories and mathematical models (Fixed charged location allocation problem, capacitated problems). 8L
Unit V Concept of Operations Strategy and related examples, Product and Process Design, Concept of JIT. Case studies 8L
Reference Books:
1. Operations Management, Jay Heizer, Barry Render, Jagadeesh Rajashekhar, Pearson Publication., 12th Edition, 2017
2. Operations Management, William J. Stevenson, McGrawhill , 11th Edition.
3. Production and Operations Management, R. Panneerselvam, PHI Publication. , 3rd Edition
4. Case studies to be provided by the instructor.

Human Resource Management L T P C
3 0 0 9
Course Objectives:
 To provide basic inputs regarding the various topics in the area of Human Resource Management
Learning Outcomes:
 The student will be able to appreciate the various processes related to Human Resource Management at the Workplace
Unit I Definition and Concept; Challenges of HRM; HR as a factor of
Competitive Advantage –
6L
Unit II Human Resource Planning; Job Analysis, Job Description, 8L
Unit III Recruiting Talent, Selecting Talent; Appraising and Managing Performance: Process and Types – 10L
Unit IV Compensation: Types & benefits; Maintenance & Separation; Training & Development – 8L
Unit V Gender Issues at work place. Human Resource Information Systems (HRIS) 6L
Reference Books:
1. Mamoria: Personnel Management
2. Dessler and Varkkey: Human Resource Management

Multivariate Data Analysis (MS) L T P C
2 0 1 7
Course Objectives:
 This course will demonstrate the properties of multivariate distributions and their applications.
Learning Outcomes:
 Students will learn about the application of Multivariate Analysis techniques in Data Analytics.
Unit I Overview of Multivariate Methods: Types of Multivariate Technique, Structured approach to Multivariate Model Building, Examination of Data 3L
Unit II Exploratory Factor Analysis: Factor Analysis Decision Process and applications, Cluster analysis – Hierarchal and non-hierarchal clustering procedures (K-Means Clustering algorithm) and applications 6L
Unit III Multiple Regression Analysis: Decision process and illustrations; Classification Technique: Multiple Discriminant Analysis, Logistic regression (Binary Logit analysis) with illustrations 10L
Unit IV Likelihood ratio principles, Hotelling's T2and MANOVA: Decision Process
and illustrations
4L
Unit V Conjoint Analysis: Choice–based conjoint approach, Part worth estimation, Decision process and illustrations 3L
Reference Books:
1. Hair et al.(2015), Multivariate Data Analysis. 7th Ed. Pearson
2. R. A. Johnson and D. W. Wichern (2013), Applied Multivariate Statistical Analysis. 6th Ed. Pearson
3. C. R. Rao (2002), Linear Statistical Inference and its Applications. 2nd Ed. Wiley
4. M. S. Srivastava and C. G. Khatri (1979), An Introduction to Multivariate Statistics, Elsevier North Holland, Inc., New York
5. 4. R. J. Muirhead (2009). Aspects of Multivariate Statistical Theory. 2nd Ed. Wiley- Interscience.

Advanced DBMS Lab L T P C
0 0 2 2
Course Objectives:
 Advanced DBMS is the important course Data Analytics. Learning Outcomes:
 Students will learn how to use and design Data Base in Data Analytics.
Lab. 1. Accessing the database



Lab.2. Basic, intermediate, and advanced SQL



Lab.3. Introduction to Python database toolbox



Lab.4. Introduction to Git



Lab.5. Database access from a programming language



Lab.6. Database metadata access from a programming language



Lab.7. . Create a webpage connected to a database server



Lab.8. Create functions to generate HTML



Lab.9. Term projects: self-conceived or assigned by the instructor



Lab.10. Presentation of the term projects



Reference Books:
1. Database System Concepts, Korth, Silberschatz and Sudarshan, McGraw Hill.
1. Database: Principles, Programming, Performance, P. O'Neil, Morgan Koffman
2. Principles of Database and Knowledge-Base Systems, J.D. Ullman, Computer Science.

Artificial Intelligence L T P C
3 0 0 9
Course Objectives:
This course will introduce the basic principles in artificial intelligence, which covers blind and heuristic search strategies, simple knowledge representation schemes, introduction to CSP problems and use for general purpose heuristic for constraint propagation, genetic algorithm, rule based system, Introduction to probabilistic reasoning, planning and learning neural networks models, Areas of application, natural language processing, will be explored. The PROLOG programming language will also be introduced.
Learning Outcomes:
Understanding of following :
Problem as Search - Converting real world problems into AI search problems and explain important search concepts, such as the difference between informed and uninformed search, the definitions of admissible and consistent heuristics and completeness and optimality. Understanding of various heuristic search techniques, MiniMax search for game playing.
Constraint Satisfaction - Formulation of real world problem as CSP problem and solution for CSP using general purpose heuristics
Genetic Algorithm for optimization
Knowledge representation using first order logic, proofs in first order using techniques such as resolution, unification.
Rule based system and logic programming using Prolog programming language Planning techniques
Bayesian network and reasoning
Fundamentals of learning using neural net, decision tree, naive- Bayes, nearest neighbour, inductive learning
Fundamentals of NLP
Unit I Artificial Intelligence Introduction, Brief history, Problem solving by search: state space, Search and Knowledge representation. Uninformed search : Breadth First Search, Depth First Search, Depth First with
Iterative Deepening and Uniform Cost Search.
5L
Unit II Heuristic Search: Hill climbing, Simulated Annealing, A*, problem reduction, Algorithm, Minimax search. 5L
Unit III Binary and Higher order CSP, Constraint Satisfaction Graph, MRV, Degree, Least Constraining, Forward Checking and Arc Consistency General purpose heuristics for CSP. 5L
Unit IV Introduction to genetic algorithm, operations : selection, crossover, mutation examples. 5L
Unit V Logic based representations (PL, FoL) and inference, Logic Programming: Prolog. Rule based representations, forward and backward chaining, matching algorithms. 5L
Unit VI Planning Techniques: Goal Stack Planning, Constraint posting. 4L
Unit VII Probabilistic Reasoning: Bayesian Network and reasoning 3L
Unit VIII Learning: Neural Network models, Statistical methods: Naive-Bayes, Nearest Neighbour, Decision trees, Inductive Learning. 7L
Unit IX Introduction to Natural Language Processing. 2L
Reference Books:
Artificial Intelligence Modern Approach Third Edition by S. Russell,P. Norvig, PHI Artificial Intelligence Third Edition by Kevin Knight (Author), Elaine Rich (Author),
Artificial Intelligence, Structures and Strategies for Complex Problem Solving George F Luger, Sixth Edition, Pearson.

Information retrieval L T P C
3 0 0 9
Course Objectives:
This Subject provides students with an in-depth knowledge about the Information Retrieval. The students will able to understand the various Retrieval Models, Link Analysis, Social Search techniques and related applications.
Learning Outcomes:
 Knowledge and understanding: Outline the potential benefits Information Retrieval.
Unit I Introduction: Basic IR system structure. 3L
Unit II Retrieval techniques: Boolean retrieval, term-vocabulary, postings-lists, Dictionaries and tolerant retrieval: Wildcard queries, Spelling correction, Phonetic correction 4L
Unit III Inverted indices: Preprocessing steps, tokenization, stemming, stopword removal, term weighting. 4L
Unit IV Models: vector space model, probabilistic model, language models; 3L
Unit V Evaluation: standard test collection, concept of relevance, precision-recall based metrics, reciprocal rank. 3L
Unit VI Relevance feedback and query expansion: Rocchio algorithm. 3L
Unit VII Text classification: Naïve Bayes 3L
Unit VIII Text clustering: Flat Clustering, Hierarchical Clustering. 4L
Unit IX XML Retrieval: Basic concepts, Challenges, Evaluation 3L
Unit X Web search: Structure of Web, web graph, Hidden Web, User intent, Web crawl 3L
Unit XI Link Analysis: Web as a graph, PageRank, Hubs and Authorities. 3L
Unit XII Social search: Community-based search activities, Question Answering,
Collaborative Searching.
3L
Reference Books:
An Introduction to Information Retrieval, By Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze, Cambridge University Press.
Information Retrieval: Algorithms and Heuristics, By David A. Grossman, Ophir Frieder

Big Data (MS) L T P C
3 0 0 9
Course Objectives: This course will deliver the concepts of Big Data and different Big Data analytical Applications
Learning Outcomes:
Students will learn about the application of Big Data in the current business scenario
Unit I Introduction to Big Data and Its Value, Tools and Techniques to do Big Data Analytics, Overview of Descriptive, Predictive and Prescriptive analytics, Supervised, Semi Supervised and Unsupervised concepts. 10L
Unit II Predictive Analytics using Regression analysis, Predictive analytics using Classification: (Uses of logistic regression, classification trees, discriminant analysis, neural networks), Association Mining-Market Basket Analysis, clustering: (k-Mean clustering, Self-organizing Maps), 8L
Unit III Web Scrapping Scrubbing: Analysis and limitations, Text Mining (Preprocessing, Naïve Bayes, Decision Tree, SVM, Random Forest, 10-fold cross validation, Ensemble and Stacking techniques, Wilcoxon sign ranked test for classification). 8L
Unit IV Social Media Analytic – Sentiment analysis, Topic Modeling, Sentiment Classification Using Lexicon based method, Recommender systems. 6L
Reference Books: . BART BAESENS: Analytics in a big data world, the essential guide to data science and its application, Wiley publishing. 1 Text Mining with R: A Tidy Approach 1st Edition by Julia Silge (Author), David Robinson (Author) 2 Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, by Galit Shmueli, Peter C Bruce, Inbal Yahav, Nitin R Patel, Kenneth C. Lichtendahl Jr., ISBN: 978-1-118-87936-8 (2017 or latest)

Missing Data Analysis and Survey Sampling (M&C) L T P C
3 0 0 9
Course Objectives:
The aim of this paper is to teach the students about various techniques which deals with how to manage with the incomplete survey data for providing the desired estimates
Learning Outcomes:
 The students will learn how to using missing data analysis techniques in Data
Unit I Basics of survey sampling: sampling frame, sampling design, basic
principles of survey sampling.
3L
Unit II Types of sampling: Probability sampling and non-probability sampling, Probability sampling: Simple random sampling, Stratified random sampling, Systematic sampling, Cluster and stage sampling. 13L
Unit III Use of auxiliary information at estimation stage, Ratio, product, difference and regression methods of estimation and their properties 8L
Unit IV Introduction of Missing data: Reasons and types of missing data; Techniques to handle the missing data: Imputation method; Mean, Ratio and Regression methods of imputations; Hansen and Hurwitz (1946) technique. 9L
Unit V Sensitive variable; Randomized response techniques– Warner’s, Simmon’s and Two Stage response techniques. 6L
Reference Books:
Sampling Techniques, 3rd Ed, Wiley Eastern Ltd. by Cochran, W.G.
. Sampling Theory of Surveys with Applications, IASRI New Delhi, 1984 Ed. by Sukhatme P V., Sukhatme B. V. and Sukhatme S., and Ashok C.
2. Sampling Theory and Methods, Statistical Publishing Society, Calcutta. by Murthy, M.N.
3. Advanced Sampling – Theory with Applications, Kluwer Publications by Sarjinder Singh.
4. Sampling Theory, Narosa Publications, New Delhi by Desraj and Chandhok P.

Time series analysis L T P C
3 0 0 9
Course Objectives:
The objective of the course will be to give idea to the students about Time Series Analysis.
Learning Outcomes:
 This course will be useful for analysis of different time series data, modelling forecasting
Unit I Discrete parameter stochastic processes, strong and weak stationary, autocovariance and autocorrelation. Periodogram and correlogram analysis. Linear Time Series Models: Stationary and Non Stationary Models, model identifications, parameter estimation and forecasting. 12L
Unit II Conditional Heteroscedastic models and their applications. 9L
Unit III Multivariate Time Series Analysis and their Applications 8L
Unit IV PCA and Factor Models and their applications 6L
Unit V Transfer Function models: identification, fitting and application. 4L
Reference Books:
Time Series Analysis, Forecasting and Control by Box and Jenkins, Pearson Education.
1. Introduction to Time Series and Forecasting by P.J. Brockwell and R.A. Davis, Springer.
2. Analysis of Financial Time Series by Ruey S. Tsay
3. Time Series Analysis and Its Applications with R Examples by R.H. Shumway & D.S. Stoffer.

Numerical optimization L T P C
3 0 0 9
Course Objectives:
The course deals with the basic idea of mathematical programming (Linear and Nonlinear).
Learning Outcomes:
The student will get an exposer on how to work out the computational implementation of a numerical algorithm for solving Linear and Nonlinear Programming Problem and do presentations.
Unit I Linear Programming: Review of various techniques of linear programming problems. 10L
Unit II Unconstraint Optimization: One dimensional- Fibonacci methods, Golden Section, Bisection and Newton Method; Higher dimensional- Random search methods, Nelder and Meed method, Steepest descent method, Grid search method, Conjugate gradient methods 8L
Unit III Constraint Optimization: Kuhn Tucker conditions, Zoutendijk’s method of feasible directions, Sequential programming approach, Cutting plane methods. Rosen’s gradient projection method 8L
Unit IV Penalty Function Methods: Basic approach of the penalty function method, Exterior penalty function method, Interior penalty function method, Augmented Lagrange multiplier method. 6L
Reference Books:
. Nocedaa, J. and Wright, S. J.: “Numerical Optimization”, Springer series in Operations Research, Springer-Verlag, 2006.
1 Chandra, S., Jayadeva and Mehra, A.: “Numerical Optimization with Applications”, Narosa, 2009.
2. Rao, S. S.: “Engineering Optimization: Theory and Practice”, John Wiley, 2009.
3. Bazaraa, M. S., Sherali, H. D. and Shetty, C. M.: “Nonlinear Programming Theory and Algorithms”, Second Edition, John Wiley and Sons, 1993.

Marketing Analytics L T P C
3 0 0 9
Course Objectives:
This course intend to impart students how to build realistic and actionable models of marketing actions and customer reactions by collecting, using different type of data and applying different quantitative and qualitative methods to validate the marketing strategy designs and its implementation. This will be a hands-on course based on the different software like, EXCEL, SPSS, SYSTAT, STATA etc., which will be applied by the students to study the actual business situations.
Learning Outcomes:
This course will strengthen and refine the analytical abilities of students by introducing various marketing analytics tools for developing marketing insights in areas which include segmentation, targeting and positioning, satisfaction management, customer lifetime analysis, customer choice, customer behavior and intention, and product and price decisions etc.
Unit I Introduction to Multivariate statistics and marketing analytics 6L
Unit II Segmentation and cluster analysis 4L
Unit III Regression analysis and relationship estimation 4L
Unit IV Brand choice and logit models 4L
Unit V Measuring customer attitudes and factor analysis 4L
Unit VI Drivers of customer satisfaction and path models 4L
Unit VII Brand positioning- Perceptual maps and multidimensional scaling 4L
Unit VIII New products and conjoint analysis 4L
Unit IX ROMI (Return on Marketing Investment), experiments and ANOVA- promotional mix effectiveness 6L
Reference Books:
1. Multivariate Data Analysis (7th Edition), Hair and Anderson, Pearson Pub.
2. Marketing Research: An applied orientation (6th Edition), Malhotra and Dash, Pearson Pub.
1. Doing Statistical Mediation & Moderation, (13th Edition), Paul E. Jose, The Guilford Press, New York , London
Software: MS Excel, SPSS, SYSTAT, STATA.

Management of Self and Behavioural Analytics L T P C
3 0 0 9
Course Objectives:
To provide basic inputs regarding the various processes related to behaviour of the individual at the work place
Learning Outcomes:
 The student will be able to appreciate the various individual and group processes related to
Human Resource which will equip them to take better decisions towards various processes of development of human resources
Unit I Individual and Interpersonal Behavior-Developing Self Awareness –
concepts of secular & spiritual levels Effective Problem Solving, Managing Stress, Assertiveness, Trust Building,-
10L
Unit II Organizational Success Through Effective Team work, Getting along with
People (co-workers, Boss), Managing Emotions at the work place, Emotional Intelligence
8L
Unit III Developing good work habits, Developing self-confidence and becoming a leader 8L
Unit IV Self, Personality and Psychological Assessment-Evolution, Theory and issues of Psychological Assessment 6L
Unit V Assessment Centre, Summing Up and developing Personal Profile. Current developments in industry – 7L
Reference Books:
1. Psychological Testing – Anastasi Journal articles and Standardized Instruments

Operations Analytics (MS) L T P C
3 0 0 9
Course Objectives: : Recent extraordinary improvements in data-collecting technologies have changed the way firms make informed and effective business decisions. The course on operations analytics focuses on how the data can be used to profitably match supply with demand in various business settings.
Learning Outcomes: In this course, students will learn how to model future demand uncertainties, how to predict the outcomes of competing policy choices and how to choose the best course of action in the face of risk.
Unit I Understanding and defining operations analytics, What involves in operations analytics, Decision Domains in operations analytics, Importance of analytics in Operations & Supply Chain Management, Key issues in operations analytics [6L]
Unit II Basic concepts of random variable, descriptive statistics, common forecasting tools, and quality of forecast [6L]
Unit III Solve operations problems in settings with low uncertainty using optimization models [9L]
Unit IV Evaluate and compare operations decisions when their impact is uncertain using simulation to estimate some common measures of risk and reward. [9L]
Unit V Solve complex operations prooptimization, simulation, and deblems with high degrees of uncertainty using cision trees together. [9L]
Text Books:
1. Albright, S., & Winston, W. (2014). Business analytics: Data analysis & decision making. Nelson Education.
2. Law, A. M., Kelton, W. D., & Kelton, W. D. (1991). Simulation modeling and analysis (Vol. 2). New York: McGraw-Hill.
Reference Book:
1. Chopra, S., & Meindl, P. (2016). Supply chain management: Strategy, planning, and operation. Pearson Publication
Heizer, J. H., & Render, B. (2008). Operations management(Vol. 1). Pearson Education India.

Financial Econometrics (MS) L T P C
3 0 0 9
Course Objectives: The course is designed to
 Provide knowledge of modern econometric techniques commonly employed in the finance literature.
 Develop an understating of statistical tools in the area of finance.
 Introduces financial modelling for research oriented students in finance
 Learning Outcomes: Understand the essential foundations of time series models.
 Construct and evaluate forecast models using financial time-series. Explain and apply models of volatility using financial time-series.
 Understand and estimate the long run relationship between variables using financial time- series.
 Understand, construct and estimate panel data models. Understand and estimate the limited dependent variable models.
Unit I : Overview of the classical linear regression model (CLRM)- Recent development and analysis of the CLRM, CLRM assumptions and diagnostic tests, Univariate time series modelling and forecasting- Moving average processes, Autoregressive processes, ARMA processes, Building ARMA models: the Box--Jenkins approach.
Unit II Multivariate models- Vector autoregressive models, Impulse responses and variance decompositions. Modelling long-run relationships in finance- Stationarity and unit root testing, Cointegration, Equilibrium correction or error correction models, Testing for and estimating cointegrating systems using the
Johansen technique based on VARs

Unit III Modelling volatility and correlation- Autoregressive volatility models, Autoregressive conditionally heteroscedastic (ARCH) models, Generalised ARCH (GARCH) models
Unit IV Panel data models-The fixed effects model, Time-fixed effects model, The random effects model. Limited dependent variable models- The linear probability model, The logit and probit models, Multinomial linear dependent
variables.

References:
1. Introductory Econometrics for Finance, 2nd Edition, Chris Brooks, Cambridge University Press (2014)
2. Introduction to Econometrics, 4th Edition, Christopher Dougherty, Oxford University Press (2011).

COGNOS Lab L T P C
0 0 3 3
Course Objectives:
This course will enhance the analytical skills of the students to create business intelligence.
Learning Outcomes:
Students will learn about the concept of Databases and Data warehouse, MIS Reporting, Demonstration of Dashboards and will gain the expertise to analyse corporate data.
Unit I Overview of Business Intelligence and IBM BI tool
What is Business Intelligence, why is BI important and where can we use it, Components of BI, Business Intelligence Roadmap, what is Data warehouse, why do we need it, Understand OLTP and OLAP Systems, Introduce COGNOS BI, Examine the different studios in COGNOS BI, Identify the different data sources within the studios, COGNOS connection, Demonstration of the different panels of COGNOS BI.
6L
Unit II Framework Manager (metadata modelling)
Introduction to framework manager, Framework manager basics, User interface, navigation, objects, Planning the project and data model, framework manager workflow, Naming conventions for objects in a project, Designing project, Create project, Importing metadata from one and more sources, Exporting metadata, Data sources, Working with data source connections, Create and modify data sources, Improve performance by setting query processing type, Preparing relational metadata for use in reports, Verifying relationships, Working with dimensions, Working with query, Subjects – data, model and stored procedure query subject, Working
with query items
12L
Unit III Analysis Studio
Introduction to Analysis Studio, creating a basic analysis, working with data in crosstab, exploring data, limiting data, calculating data, Sharing data
6L
Unit IV Report Studio: Introduction to report studio, Report studio user interface, Creating, save and run reports , Report templates, Managing reports, Types of reports, List reports, Crosstab reports charts, Formatting a report, Report layout guidelines, Fonts, styles, header, footer and orders, Insert objects , Swap columns and rows, Working with data, Filters, parameters and prompts, Sorting and grouping, Subtotals and calculations, Working with queries, Working with reports, Managing changes in the package, Conditional formatting, Drill-through reports, Drill-up / drill-down reports,
Master-detail reports, Scheduling reports
12L
Unit V Final Dashboard Interpretation Practical exercises 3L
Reference Books:
1. Text book will be provided by IBM
1. Data Warehousing Fundamentals for IT Professionals (Paulraj Ponniah)
2. Dimensional Modeling: In a Business Intelligence Environment: An IBM Redbooks publication
3. Data Integration Blueprint and Modeling: Techniques for a Scalable and Sustainable Architecture (Anthony David Giordano).

Big Data Lab L T P C
0 0 2 2
Course Objectives:
The Practical classes of this course will deliver the skill of big data analytics and its applications.
Learning Outcomes:
Students will gain the experiential learning by analysis the live big data.
Unit I Introduction to R and R Studio, Big data, R coding basics, Tools and Techniques to do Big Data Analytics, Hadoop basics. 6L
Unit II Predictive Analytics using Regression analysis, Predictive analytics using Classification: (Uses of logistic regression, classification trees, discriminant analysis, neural networks), Association Mining-Market Basket Analysis, clustering: (k-Mean clustering, Self-organizing Maps), 8L
Unit III Practical on the following analytics with live data
Web Scrapping Scrubbing: Analysis and limitations, Text Mining (Preprocessing, Naïve Bayes, Decision Tree, SVM, Random Forest, 10-fold cross validation, Ensemble and Stacking techniques, Wilcoxon sign ranked test for classification).
6L
Unit IV Practical on the following analytics with live data
Social Media Analytic – Sentiment analysis, Topic Modeling, Sentiment Classification Using Lexicon based method, Recommender systems.
6L
Reference Books:
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, by Galit Shmueli, Peter C Bruce, Inbal Yahav, Nitin R Patel, Kenneth C. Lichtendahl Jr., ISBN: 978-1-118-87936-8 (2017 or latest)
Data Analytics Using R (Seema Acharya), McGraw hills Publishers