Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision) L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory work CE= Continuous Evaluation, SEE= Semester End Examination SEMSETER-I GANPAT UNIVERSITY FACULTY OF MANAGEMENT STUDIES Programme MBA Branch/Spec. Business Analytics Semester I Version 2.0.0.0 Effective from Academic Year 2020-21 Effective for the batch Admitted in June 2020 Subject code IA01MPO Subject Name MANAGEMENT PRINCIPLES AND ORGANIZATIONAL BEHAVIOUR Teaching scheme Examination scheme (Marks) (Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total L TU P TW Credit 4 0 0 4 Theory 60 40 100 Hours 4 0 0 4 Practical Objective: To familiarise the students with the fundamental concepts of Management and highlight approaches in organisation behaviour, which helps to enhance employability Course Outcome: CO 1: The students get in depth knowledge of management concepts and principles like planning, organizing, coordination, and decision making. CO 2: The Students learn about the individual and group behaviour in an orgnaisation. CO-3: the decision making ability of the students can escalate through various role play and case study base teaching pattern. Theory syllabus Unit Content Hrs 1 Concepts of Management & Manager, Managerial Functions, Skill, Task and Role of Manager, Levels of Management, Scientific Management Theory, Organisational Theory, Behavioural Theory, Integration Theory, Contemporary Issues in Management, Mc Kinsey’s 7-S approach 8 2 Planning : Nature, Purpose, Types and Process for Planning , concepts and types of Objectives, Concept of MBO, MBE, MBWA, Policies, Procedures and Strategies Decision Making: Approaches, Decision Making under Certainty, Uncertainty and Risk, Group Decision Making, Guidelines. Organising: Introduction, Importance of Organizing,concepts of division of work, concepts and types of Departmentalization, Concepts and Types Span of control, Decentralisation, Sources and Types of Power, Delegation of Authority Line and staff authority. Coordination: Definition - Characteristics - Objectives - Principles – Techniques. Staffing: Concept, Importance in the Organization, Introduction to HR Management 14 3 Motivation - Elements - Importance - Methods – Morale; Definition and Importance of Motivation, Early Theories in Motivation, Contemporary Theories in Motivation, Motivational Tools in Organization Leading and Leadership: Introduction, Theories, Characteristics of Leading, Importance of Leading, Functions of Leading, Role of Leadership in Contemporary Business, Theories of Leadership, Contingency Theories of Leadership. 14
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Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
SEMSETER-I
GANPAT UNIVERSITY
FACULTY OF MANAGEMENT STUDIES
Programme MBA Branch/Spec. Business Analytics
Semester I Version 2.0.0.0
Effective from Academic Year 2020-21 Effective for the batch Admitted in June 2020
Subject code IA01MPO Subject Name MANAGEMENT PRINCIPLES AND ORGANIZATIONAL
BEHAVIOUR
Teaching scheme Examination scheme (Marks)
(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total
L TU P TW
Credit 4 0 0 4 Theory 60 40 100
Hours 4 0 0 4 Practical
Objective:
To familiarise the students with the fundamental concepts of Management and highlight approaches in organisation
behaviour, which helps to enhance employability
Course Outcome:
CO 1: The students get in depth knowledge of management concepts and principles like planning, organizing,
coordination, and decision making.
CO 2: The Students learn about the individual and group behaviour in an orgnaisation.
CO-3: the decision making ability of the students can escalate through various role play and case study base teaching
pattern.
Theory syllabus
Unit Content Hrs
1 Concepts of Management & Manager, Managerial Functions, Skill, Task and Role of Manager, Levels of
Uma Sekaran - Organisational Behaviour Text & Cases, Tata McGraw Hill Public Company Ltd., New Delhi, 2005.
V.S.P.Rao ,V.Hari Krishna “Management- Text and Cases”, Excel Books, New Delhi 2009.
Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
GANPAT UNIVERSITY
FACULTY OF MANAGEMENT STUDIES
Programme MBA Branch/Spec. Business Analytics
Semester I Version 1.0.0.0
Effective from Academic Year 2020-2021 Effective for the batch Admitted in June 2020
Subject code IV02QST Subject Name Quantitative Statistics for Managers
Teaching scheme Examination scheme (Marks)
(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total
L TU P TW
Credit 3 3 Theory 60 40 100
Hours 3 45 Practical
Pre-requisites:
Objective: The objective of this course is to teach various concepts of Basic Statistics and Quantitative methods of
Analytics.
Course Outcome:
On the completion of the course, the student will learn
Co-1: the basics of Statistics required for Business management
CO-2: the statistical tools implied to analyse the data
CO-3: various methods of forecasting based decision making techniques
CO-4: the basic sample testing theories
Theory syllabus
Unit Content Hrs
1 Basic Statistical Methods: Measures of Central tendency: Mean, Median, Mode and Dispersion: Range, Inter Quartiles, Standard Deviation, Coefficient of Variation. Theory of Probability – Definition and Rules of Probability, Baye’s Theorem; Probability Distribution – Discrete distribution – (Binomial and Poisson), Continuous distribution – (Normal & Exponential). Decision Tree Analysis
12
2 Statistical Tools & Techniques: Co-Relation & Regression; Single Linear Regression; Multiple
Regression: Use of Software in Multiple Regression, Building Multiple Regression Models, Different
types of models, Multicollinearity
10
3 Forecasting Methods: Subjective Delphic, Nominal grouping and Jury of Opinion; Quantitative – Input-
Output Model, Time Series Method, Moving Average, Exponential Smoothing, Linear Trend Line,
Method of Least Square, Measuring error – MAD, MAPD, CE, MSE, MSPE.
11
4 Estimation Theory and Hypothesis Testing: Sampling theory; Formulation of Hypotheses; Application of Z-test, t-test, F-test and Chi-Square-test in testing of the hypothesis. Techniques of association of Attributes & Testing.
12
Practical content
Text Books
1
Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
Reference Books
1.
2.
3.
4.
5.
6.
7.
Quantitative Techniques in Management by Vohra, Tata McGraw-Hill, Latest edition.
Statistics for Management by Richard I. Levin and David S. Rubin (Pearson Education).
Statistics for Management, T N Srivastava and Shailaja Rego, TMH.
Business Analytics by James Evans, Pearson Education.
Turban, Sharda,Decision Support and Business Intelligence Systems, Delen, Pearson, 9th edition, 2014
Cathy O’Neil and Rachel Schutt. Doing Data Science, Straight Talk From The Frontline. O’Reilly. 2014.
Business Analytics: The Science of Data Driven Decision Making By U Dinesh Kumar, Wiley Publication
GANPAT UNIVERSITY
FACULTY OF MANAGEMENT STUDIES
Programme MBA Branch/Spec. Business Analytics
Semester I Version 2.0.0.0
Effective from Academic Year 2020-21 Effective for the batch Admitted in June 2020
Subject code IA03FCP Subject Name Fundamentals of Computer Programming and Networking
Teaching scheme Examination scheme (Marks)
(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total
L TU P TW
Credit 2 0 1 0 3 Theory 30 20 50
Hours 1 0 1 0 1 Practical 30 20 50
Objective:
To provide wide opportunities of Logic Development using Algorithm, Flowchart and Computer Program.
Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
To make aware the students about basics of computer, languages, peripheral devices and network.
Course Outcome:
CO-1: After completing this course, students should be able to:
✓ Students gain an overall understanding of Logic Development ✓ Students able to design the solution of problem ✓ Understand logic development tools for Algorithm, Flowchart and Program ✓ To gain and implement of Algorithm, Flowchart and Program ✓ To acquire logic skills for computer based problems & solutions
Co-2: Student can get awareness about basic of computer, devices and network fundamentals. Theory syllabus
Unit Content Hrs
1 Introduction to Design of problem Concept of problem solving, Problem definition, Program design, Debugging, Types of errors in programming, Documentation and Comments
3
2 Fundamentals of Algorithm & Flowchart Algorithms, Flowchart, Pseudocode, Structured programming - top-down and bottom-up.
4
3 Introduction to C programming Structure of a C Program, Why C language, Writing, Compiling and Executing program, Data types, Variables, Identifiers and keywords, Literals, Strings.
4
4 Understand the basic statements Types of Operators, Input and Output Statements, Selection Statement, Control Statement, Jumping Statement.
4
5 Introduction to Computer: Information Technology, Hardware and processor: History of Computer, Definition of computer, Block Diagram of computer, Characteristics of computer. Data and Information, Features of Information, System Hardware, Processor Architecture, Computer Arithmetic, Instruction Set Architecture
3
6 Types of Languages: Low level v/s High level languages, Introduction of Machine Language, Assembly Language. Language Processor: Compilers, Interpreter, Assemblers, Difference between Compiler-Assembler-Interpreter, Types of Software: System Software, Application Software
2
7 Peripheral Device: FDD, Hard disk drive, Tape Drives, CD-DVD Drives, USB, Cache memory, Pen Drive Port Introduction: USB, Serial, Parallel and PS2 08,
Input Devices: Key Board, Mouse, Touch screen, Scanner, OMR, MICR, OCR Output Devices: VDU, Printer, Communication Devices: MODEM, NIC
4
8 Introduction to Computer Network Need of Computer Network, Advantages of Computer Network, Uses of Computer Network, Network Models, Categories of Networks and Internetworks, Network Topologies (Bus, Star, Ring, Star Bus, Star Ring and Physical Mesh)
Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
Brain Friendly Guide Head First C by David Griffiths & Dawn Griffiths, Oreilly Publication, First Edition.
Reference Books:
1. Brain Friendly Guide Head First Software Development by Dan Pilone & Russ Miles, Oreilly 2. Publication, First Edition. 3. Information Technology and Concepts, 2nd Edition By. Dr. Madhulika Jain, BPB publication 4. Fundamental Of computer Organization By Albert Zomaya 5. B.A. Forouzan:DataCommunicationandNetworking,TataMcGrawHill.
Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
GANPAT UNIVERSITY
FACULTY OF MANAGEMENT STUDIES
Programme MBA Branch/Spec. Business Analytics
Semester I Version 1.0.0.1
Effective from Academic Year 2020-21 Effective for the batch Admitted in June 2020
Subject code IA05MAM Subject Name MARKETING MANAGEMENT AND ANALYTICS
Teaching scheme Examination scheme (Marks)
(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total
L TU P TW
Credit 4 0 0 0 4 Theory 60 40 100
Hours 4 0 0 0 4 Practical
Pre-requisites:
Objectives: This course develops the student’s basic analytical skills, conceptual abilities, and substantive
knowledge in marketing through exercise in decision making in a variety of real-life marketing situations. It is
intended to be foundation for those who plan to do further work in marketing in the second year. It is also designed
to serve as a terminal course for those not intending to specialize in marketing.
CourseOutcome:
CO-1: Students will understand scope of marketing and various concepts of marketing management.
Co-2. It helps students to learn and understand Various bases of segmentation, identifying target market and
positioning through various case studies and role plays.
Co-3. It will help students to learn and understand how companies draft their marketing mix, branding and pricing
strategies. Students will be able to analyse product life cycle various organizations through case study method.
Co-4. Students will be able to understand various concepts of promotion mix such as advertising, personal selling,
direct marketing. They will also be able to analyse the strategies by corporate for competitive advantage.
CO-5: The students will be able to learn various innovative concepts to develop marketing strategies for
challengers, followers and niches. Students will be able to Design and manage global marketing strategies.
CO-6: The students will be able to learn various emerging trends such as rural marketing, customer relationship
management and services marketing. Role play method can be used to make students understand the concept in
an easy way.
Theory syllabus
Unit Content Hrs
1 Nature and Scope of Marketing, Marketing Management-Concepts & Philosophy, Environmental Scanning, Marketing Research and Forecasting, Buying Behavior-Consumer & Industrial, Difference Between Consumer Markets and Industrial Markets, Nature of Demand in Industrial Markets.
12
2 Market Segmentation, Targeting and Positioning, Segmenting: Bases and Process, Target Market
Selection, Positioning-Nature and Importance
10
3 Product Decisions: New Product Development, Product Mix, Branding and Packaging Decisions,
Product Life Cycle & Strategies, Product Differentiation Strategies.
Pricing Decisions-Objectives and Determination, Methods of Setting Price and pricing strategies
14
Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
4 Promotion: Integrated Marketing Communication; Mass Communication-Advertising, Sales
Promotion, Events & Public Relations; Personal Communication, Personal Selling and Direct
Marketing, Digital Communication-Online, Social Media and Mobile.
Place: Channels of Distribution-Levels and Types of Channels, Functions and Management of
Channel members, Channel Selection and Motivation, Management of Physical Distribution;
Wholesaling and Retailing.
14
5 Developing Marketing Strategy for Market leader, Challenger, Follower and Nicher; Global
Kotler, Philip, "Marketing Management: Analysis, Planning, Implementations and Control", Pearson
Education, New Delhi, Latest Edition.
Saxena Rajan, "Marketing Management", Tata McGraw Hill, New Delhi , Latest Edition.
Stanton William J., "Fundamentals of Marketing", McGraw Hill, Latest Edition.
Kotler, Philip and Armstrong, Graw. "Principles of Marketing", Pearson Education, New Delhi 2004.
Neelamegham, S., "Indian Cases in Marketing", Vikas Pub. New Delhi.
Bull, Victor P., "Marketing Management: A Strategic Planning Approach", McGraw Hill, New York.
Czinkota, M.R., "Marketing Management", Pearson Education Asia, New Delhi 2004.
Michael, J. E., Bruce, J. W. and Williom, J. S.,“Marketing Management”, Tata McGrawHill, New Delhi, 13th Edition, 2004.
Louis E. Boone and David L. Kurtz,“Contemporary Marketing”. Harcourt Collye Publishers, 2001.
Douglas, J. Darymple & Leonard J. Parsons,“Marketing Management: Text and Cases”, Seventh Edition, John Wiley and
Sons, 2002.
Pride, William, M., and O.C. Ferrell,“Marketing: Concepts and Strategies”, Biztantra, New Delhi, 2005.
Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
GANPAT UNIVERSITY
FACULTY OF MANAGEMENT STUDIES
Programme MBA Branch/Spec. Business Analytics
Semester I Version 1.0.0.0
Effective from Academic Year 2020-21 Effective for the batch Admitted in June 2020
Subject code IA06PFA-I Subject Name Programming for Analytics-1 (SAS Subject)
Teaching scheme Examination scheme (Marks)
(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total
L TU P TW
Credit 4 0 0 0 4 Theory 60 40 100
Hours 4 0 0 0 4 Practical
Pre-requisites:
Objective:
Course Outcome:
Theory syllabus
Unit Content Hrs
1 Essentials • the SAS programming process • using SAS programming tools • understanding SAS syntax
2 Accessing Data • understanding SAS data • accessing data through libraries • importing data into SAS
3 Exploring and Validating Data • exploring data • filtering rows • formatting columns • sorting data and removing duplicates
4 Preparing Data • reading and filtering data • computing new columns • conditional processing
5 Analyzing and Reporting on Data • enhancing reports with titles, footnotes, and labels • creating frequency reports • creating summary statistics reports
6 Exporting Results • exporting data • exporting reports
Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
Using SQL in SAS • using Structured Query Language in SAS • joining tables using SQL in SAS
Controlling DATA Step Processing • setting up for this course • understanding DATA step processing • directing DATA step output
Summarizing Data
• creating an accumulating column • processing data in groups
Manipulating Data with Functions • understanding SAS functions and CALL routines • using numeric and date functions • using character functions • using special functions to convert column type
Creating Custom Formats • creating and using custom formats • creating custom
MacDonald, Mathew- Excel: The Missing Manual. Sebastopol:O'reilly.2nd edition, 2010
Monahan, George E. Management Decision Making: SpreadSheet, Modelling, Analysis.,
London:CambridgeUniversity.8th edition,2000
Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
SEMSETER- II
GANPAT UNIVERSITY
FACULTY OF MANAGEMENT STUDIES
Programme MBA Branch/Spec. Business Analytics
Semester Version 1.0.0.0
Effective from Academic Year 2020-21 Effective for the batch Admitted in June 2020
Subject code IIA01PFA-II Subject Name PROGRAMMING FOR ANALYTICS (SAS Subject)
Teaching scheme Examination scheme (Marks)
(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total
L TU P TW
Credit 3 Theory 60 40 100
Hours 3 Practical
Pre-requisites:
Objective:
Course Outcome:
Theory syllabus
Unit Content Hrs
Introduction • Why SAS macro?
• Setting up for this course.
SAS Macro Facility • Program flow.
• Creating and using macro variables.
Storing and Processing Text • Macro functions.
• Using SQL to create macro variables.
• Using the DATA step to create macro variables.
• Indirect references to macro variables.
Working with Macro Programs • Defining and calling a macro.
• Macro variable scope.
• Conditional processing.
• Iterative processing.
Developing Macro Applications • Storing macros.
• Generating data-dependent code.
• Validating parameters and documenting macros.
Essentials • Setting up for this course.
• Overview of SAS Foundation.
• Course logistics.
• Course data files.
• Introducing the Structured Query Language.
• Overview of the SQL procedure.
• Exploring tables.
• Specifying columns.
PROC SQL Fundamentals • Subsetting data.
Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
• Presenting data.
• Summarizing data.
• Creating and managing tables.
• Using DICTIONARY tables.
SQL Joins • Introduction to SQL joins.
• Inner joins.
• Outer joins.
• Complex SQL joins.
Subqueries • Noncorrelated subqueries.
• Correlated subqueries.
• In-line views.
• Creating views with the SQL procedure.
• Subqueries in the SELECT clause.
• Remerging summary statistics.
Set Operators • Introduction to set operators.
• The INTERSECT operator.
• The EXCEPT operator.
• The UNION operator.
• The OUTER UNION operator.
Using and Creating Macro Variables in SQL • Interfacing PROC SQL with the macro language.
• Creating data-driven macro variables with a query.
• Using macro variables in SQL.
Accessing DBMS Data with SAS/ACCESS • Overview of SAS/ACCESS technology.
• SQL pass-through facility
Practical content
Text Books
1
Reference Books
SAS Material
GANPAT UNIVERSITY
FACULTY OF MANAGEMENT STUDIES
Programme MBA Branch/Spec. Business Analytics
Semester Version 2.0.0.0
Effective from Academic Year 2020-21 Effective for the batch Admitted in June 2020
Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
Subject code IIA02RMM Subject Name RESEARCH METHODOLOGY IN MANAGEMENT
Teaching scheme Examination scheme (Marks)
(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total
L TU P TW
Credit 4 4 Theory 60 40 100
Hours 4 4 Practical
Pre-requisites:
Objective: The objective of this course to make the students to understand and formulate managerial situations in a theoretic framework in a decision making. It focuses on developing skills in structuring and analyzing problems and to inculcate the attitude of developing an executable solution to the problem with the help of some advanced statistical techniques.
Course Outcome: Co-1: To acquaint the students with basic statistics. CO-2: To learn concept of business research and its application. CO-3: Focuses on developing skills in structuring the research. CO-4: To understand statistical techniques for developing executable solution
Theory syllabus
Unit Content Hrs
1 Concept of business research and its applications in the various functions of management, Types of research-Basic and Applied Research, Ontology and Epistemology of business research, Quantitative V. Qualitative research, Types of business problems encountered by the research, Problems and precautions to the researcher in India, Characteristics of good research, ethics in research, Research problem definition and developing its approach, Value of Research Questions, Development of Research Questions and Hypotheses, Steps involved in research process.
12
2 Research design: Exploratory Research Design - Secondary data and Qualitative research, Descriptive Research Design, Causal research design; Research design Comparison; Data Collection; Measurement and Scaling; Scale Evaluation; Questionnaire Design; Research Methods: Structured Interview/Self-completion questionnaire, Structured Observation V. Ethnography; Sampling Methods – Probabilistic & Non Probabilistic Sampling; Sample Design & Procedures Error: Sampling and Non-Sampling Error; Sources of Error
12
3 Parametric Tests: Estimation: Confidence interval and sample size determination. Hypothesis testing: process, type I and type II error, power of test. Data analysis: Univariate, Bivariate and Multivariate Test for means: Z-test, student’s t-test: one sample test; two independent sample test and two dependent sample test (paired sample test). Test for proportions: one sample and two sample test.
12
4 Non-parametric test: Chi-square Test: Test of association, Goodness of fit, Strength of association, Analysis of Variance (ANOVA): One-way ANOVA, Two-way ANOVA (with SPSS); Multivariate Analysis of Variance (MANOVA) (with SPSS). Correlation: Bivariate and multiple; Simple regression; Multiple regression (with SPSS) Exploratory Factor analysis (with SPSS); Other Non-parametric tests (with SPSS): Run test; Binomial test; Sign test; Wilcoxon matched-pairs test; Mann-Whitney rank-sum test and Fridman one-way ANOVA.
12
5 Research proposal and report preparation: format, Types and layout of research report, Precautions in preparing the research report, Guideline for tables, figures and graphs, Bibliography and Annexure in report, Drawing conclusions, Giving suggestions and recommendations to the concerned persons.
12
Practical content
Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
Text Books
1
Reference Books
1 2 3 4 5 6 7 8 9 10 11
Quantitative Techniques in ManagementbyVohra, Tata McGraw-Hill, Latest edition. Quantitative Techniques by Kothari, Vikas Publication, 1996, 3rded. Business Statistics for Contemporary Decision Making by Ken Black (Fourth or later edition) Wiley Student Edition. Statistics for Management by Richard I. Levin and David S. Rubin (Pearson Education). Statistics for Management, T N Srivastava and ShailajaRego, TMH. Complete Business Statistics, Amir D Aczel and JayavelSounderpandian, TMH. Business Statistics by J. K. Sharma (2nd Edition or later edition) Pearson Mathematics and Statistics for Management, K. B. Akhilesh& S. B. Balasubrahmanyam, Vikas Publishing. Statistical Method by Gupta, S.C., Himalaya Publication. Business Statistics by R.S.Bharadwaj, Excel Books. Comprehensive Statistical Methods by P.N. Arora, S. Chand.
GANPAT UNIVERSITY
FACULTY OF MANAGEMENT STUDIES
Programme MBA Branch/Spec. Business Analytics
Semester Version 1.0.0.0
Effective from Academic Year 2020-21 Effective for the batch Admitted in June 2020
Subject code IIA03VBA Subject Name VISUAL BUSINESS ANALYTICS (SAS Subject)
Teaching scheme Examination scheme (Marks)
(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total
L TU P TW
Credit 3 Theory 60 40 100
Hours 3 Practical
Pre-requisites:
Objective:
Course Outcome:
Theory syllabus
Unit Content Hrs
Getting Started with SAS Visual Analytics • Exploring SAS Visual Analytics concepts.
• Using the SAS Visual Analytics home page.
• Discussing the course environment and scenario.
Administering the Environment and Managing Data • Exploring SAS Visual Data Builder.
• Exploring SAS Visual Analytics Administrator.
Using SAS Visual Analytics Explorer • Examining Visual Analytics Explorer.
• Selecting data and defining data item properties.
• Creating visualizations.
• Enhancing visualizations with analytics.
• Interacting with visualizations and explorations.
Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
Designing Reports with SAS Visual Analytics • Examining the SAS Visual Analytics Designer interface.
• Creating a simple report.
• Creating data items and working with graphs.
• Working with filters and report sections.
• Establishing interactions, links, and alerts.
• Working with gauges and display rules.
• Working with tables.
• Working with other objects.
Using SAS Visual Analytics Custom Graph Builder • Creating custom graphs.
• Using custom graph objects in a report.
Case Study: Creating Analyses and Reports with SAS Visual Analytics: MegaCorp case study.
Practical content
Text Books
1
Reference Books
Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
GANPAT UNIVERSITY
FACULTY OF MANAGEMENT STUDIES
Programme MBA Branch/Spec. Business Analytics
Semester Version 1.0.0.0
Effective from Academic Year 2020-21 Effective for the batch Admitted in June 2020
Subject code IIA04DIM Subject Name DATA INTEGRATION FOR MANAGERS (SAS Subject)
Teaching scheme Examination scheme (Marks)
(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total
L TU P TW
Credit 4 Theory 60 40 100
Hours 4 Practical
Pre-requisites:
Objective:
Course Outcome:
Theory syllabus
Unit Content Hrs
Introduction to the platform for SAS Business Analytics and SAS Data Integration Studio • exploring the platform for SAS Business Analytics
• working with SAS Data Integration Studio
• introduction to change management
Introduction to the platform for SAS Business Analytics and SAS Data Integration Studio • exploring the platform for SAS Business Analytics
• working with SAS Data Integration Studio
• introduction to change management
Introduction to Course Data and Course Scenario • explaining the course data
• explaining how to define target data
• introduction to the course scenario Creating Metadata for Source Data
• setting up the environment
• registering source data Creating Metadata for Target Tables
• registering target tables
• importing metadata Creating Metadata for Jobs
• creating jobs
• exploring functionality of Job Editor
• submitting jobs to create target tables
• specify how to document jobs
• recording job performance statistics
• chaining job flows
Working with Transformations • working with the extract transformation
• working with the data validation transformation
• working with the apply lookup standardization transformation
• working with the sort transformation
• working with the append transformation
• working with an analysis transformation
• working with the transpose transformation
• working with the transformation generator wizard
• working with the user-written code transformation
Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
• working with the loop transformations
• using XML writer and file transfer
• working with new SAS Data Integration Studio transformations
• working with status handling Defining Table Relationships
• reviewing the data model
• defining integrity constraints
• defining keys and indexes
• exploring various load techniques
Working with Slowly Changing Dimensions • defining slowly changing dimensions
• working with SCD Type 2 loader transformation
• working with the lookup transformation Working with Transformations
• working with the transformation generator wizard
Implementing Data Quality Techniques (Self-Study) • creating and applying match codes
• building and applying standardization schemes
• apply the DataFlux IS Job and DataFlux IS Service transformations
Deploying Jobs • specify how to deploy jobs for batch scheduling
• specify how to deploy jobs as stored processes
• specify how to deploy jobs for web services Maintaining and Administering SAS Data Integration Studio
• establishing project repositories for change management
• working with impact analysis
• moving metadata
• enabling status handling
• setting up the SAS Data Quality Server software
Practical content
Text Books
1
Reference Books
Note: Version 1.0.0.0 (First Digit= New syllabus/Revision in Full Syllabus, Second Digit=Revision in
Teaching Scheme,Third Digit=Revision in Exam Scheme, Forth Digit= Content Revision)
L=Lecture, TU=Tutorial, P= Practical/Lab., TW= Term work, DT= Direct Teaching, Lab.= Laboratory
work
CE= Continuous Evaluation, SEE= Semester End Examination
GANPAT UNIVERSITY
FACULTY OF MANAGEMENT STUDIES
Programme MBA Branch/Spec. Business Analytics
Semester Version 1.0.0.0
Effective from Academic Year 2020-21 Effective for the batch Admitted in June 2020
Subject code IIA05DMS Subject Name DATA BASE MANAAGEMENT SYSTEMS
Teaching scheme Examination scheme (Marks)
(Per week) Lecture(DT) Practical(Lab.) Total CE SEE Total
L TU P TW
Credit 4 4 Theory 60 40 100
Hours 4 4 Practical
Pre-requisites:
Objective: This course attempts to introduce the students to database management systems, with an emphasis on how
to organize, maintain and retrieve - efficiently, and effectively - information from a DBMS.
Course Outcome:
Upon successful completion of this course, students should be able to:
CO-1: Describe the fundamental elements of relational database management systems
CO-2: Explain the basic concepts of relational data model, entity-relationship model, relational database design,
relational algebra and SQL.
CO-3: Design ER-models to represent simple database application scenarios
CO-4: Convert the ER-model to relational tables, populate relational database and formulate SQL queries on data.
CO-5: Improve the database design by normalization.
CO-6: Security and storage of Data in the system
CO-7: Familiar with basic database storage structures and access techniques: file and page organizations, indexing
methods including B tree, and hashing
Theory syllabus
Unit Content Hrs
1 Introductory concepts of DBMS and Modelling :
Introduction and applications of DBMS, Purpose of data base, Data, Independence, Database System
architecture- levels, Mappings, Database, users and DBA
Relational Model :
Structure of relational databases, Domains, Relations, Relational algebra – fundamental operators and