Industrial Economics Or

Business Statistics

Course Structure

Code

SEM I Course

Credits

Code

SEM II Course

Credits

10101

Applied Statistics for Business

4

10201

Business Economics

4

10102

Operations Research

4

10202

Research Methodology

4

10103

Fundamentals program in R Programming

3

10203

Predictive Modelling using IBM SPSS Modeler

4

10104

Information Management – RDBMS concepts

4

10204

Recent trends in Data Sciences /Project

4

10105

Python – Basics

2

10106

Data Visualization with Cognos Analytics

1

Total Credits

18

Total Credits

16

Unit

No.

Topics

1

Concept of statistics, population, sample, parameter and statistic, examples of use of statistic, data sources, representation of data, types of statistical analyses, sampling methods, types of variables, measures of central tendency, statistical estimation: point and interval, co-variance, coefficient of correlation, formulae.

2

Permutations and combinations, Probability concepts, types of probabilities, collectively exhaustive event set, joint probability, Bayes Theorem, probability distribution for a discreet random variable, probabilistic view on variance, covariance.

3

Distributions: Bernoulli’s trail, binomial distribution, Poisson distribution, Hypergeometric distribution, student-t distribution, Chi-square distribution, F- distribution, Normal distribution, explanation of derivation of population parameter through samples and central limit theorem, Z score.

4

Hypothesis and testing, single parameter and two-parameter testing, single sided and two-sided testing, p-value, tests and test statistic and logic behind it, problems on hypothesis testing, diagnostic tests: goodness of fit, t-test, f-test and chi-sq test, contingency table, degree of freedom, analysis of variances.

Unit No.

Topics

1

Mathematical Optimization for Business Problems

Mathematical Programming is a powerful technique used to model and solve optimization problems. This training provides the necessary fundamentals of mathematical programming and useful tips for good modeling practice in order to construct simple optimization models. In this training, students will explore several aspects of mathematical programing and more about constructing optimization models using IBM Decision Optimization technology, including:

3. 1. • Basic terminology: operations research, mathematical optimization, and mathematical programming

      • Basic elements of optimization models: data, decision variables, objective functions, and constraints

      • Different types of solution: feasible, optimal, infeasible, and unbounded

      • Mathematical programming techniques for optimization: Linear Programming, Integer Programming, Mixed Integer Programming, and Quadratic Programming

      • Algorithms used for solving continuous linear programming problems: simplex, dual simplex, and barrier

      • Important mathematical programming concepts: sparsity, uncertainty, periodicity, network structure, convexity, piecewise linear and nonlinear

These concepts are illustrated by concrete examples, including a production problem and different network models.

2

Mathematical Optimization for Business Problems

Mathematical Programming is a powerful technique used to model and solve optimization problems. This training provides the necessary fundamentals of mathematical programming and useful tips for good modeling practice in order to construct simple optimization models. In this training, students will explore several aspects of mathematical programing and more about constructing optimization models using IBM Decision Optimization technology, including:

3. 1. • Basic terminology: operations research, mathematical optimization, and mathematical programming

      • Basic elements of optimization models: data, decision variables, objective functions, and constraints

      • Different types of solution: feasible, optimal, infeasible, and unbounded

      • Mathematical programming techniques for optimization: Linear Programming, Integer Programming, Mixed Integer Programming, and Quadratic Programming

      • Algorithms used for solving continuous linear programming problems: simplex, dual simplex, and barrier

      • Important mathematical programming concepts: sparsity, uncertainty, periodicity, network structure, convexity, piecewise linear and nonlinear

These concepts are illustrated by concrete examples, including a production problem and different network models.

3

3. 1. • Introduction to Linear programming

      • A production problem : Part 1 – Writing the model

      • A production problem : Part 2 – Finding a solution

      • A production problem : Part 3 – From feasibility to unboundedness

      • Algorithms for solving linear programs : Part 1 – The Simplex and Dual Simplex Algorithm

Algorithms for solving linear programs : Part 2 – The Simplex and Barrier methods

4

Linear Programming

5

Network Models

3. 1. • Introduction to Network Models

      • The Transportation problem

      • The Transshipment problem

      • The Assignment problem

      • The shortest path problem

Critical path analysis

6

Beyond simple LP

3. 1. • Nonlinearity and Convexity

      • Piecewise linear programming

      • Integer programming

      • The branch and bound method

Quadratic Programming

7

Modelling Practice

3. 1. • • Modelling the real world

        • The importance of Sparsity

Tips for better models

Unit No.

Topics

1

Module 2.1 Module 1 – R basics

3. 1. • • Math, Variables, and Strings

        • Vectors and Factors

Vector operations

2

Module 2.2 Data structures in R

• Arrays & Matrices

• Lists

Dataframes

3

Module 2.3 R programming fundamentals

• Conditions and loops

• Functions in R

• Objects and Classes

Debugging

4

Module 2.4 – Working with data in R

3. 1. • • Reading CSV and Excel Files

        • Reading text files

Writing and saving data objects to file in R

5

Module 2.5 – Strings and Dates in R

3. 1. • • String operations in R

        • Regular Expressions

Dates in R

6

Module 2.6 – Basic Visualization Tools

3. 1. • • Bar Charts

        • Histograms

Pie Charts

7

Module 2.7 – Basic Visualization Tools Continued

3. 1. • • Scatter Plots

Line Plots and Regression

8

Module 2.8 – Specialized Visualization Tools

3. 1. • • Word Clouds

        • Radar Charts

        • Waffle Charts

Box Plots

9

Module 2.9 – How to create Maps

Creating Maps in R

Unit

No.

Topics

1

Introduction to RDBMS – Data Models – Database users – System Structure – Data base administrator – Basic concepts

2

Entity – Relationship model, Basic concepts – Mapping constraints – Keys – ER Diagram – Work Entity sets – Design of an ER Database Schema – reduction of ER Schema to tables

3

SQL – Set operations – Aggregate functions – Null values – Nested subqueries – views – Modification of databases joined Relations – DDL – other SQL features

4

Data Warehousing theory- concept of cubes and aggregation of data in characteristics, Key figures and data granularity

5

Comparison of OLTP and OLAP

6

ETL- Extraction, Transformation and Loading of data from various sources to DW

7

Slicing, dicing and cross applications reporting and complex data analysis

8

Business Applications of BI- Business Intelligence

9

Data Mining Engine consisting of a set of functional modules for tasks such as characterization, association and correlation analysis, classification, prediction, cluster analysis, outlier analysis, and evolution analysis.

10

Pattern Evaluation-The search toward interesting patterns and knowledge presentation and visualization techniques for displaying recognized patterns

11

Data Mining Applications- Financial data, Retail Industry, Telecom Industry, Biological Data etc.

Unit

No.

Topics

1

Introduction to Python,

• Understanding Operators, Variables and Data Types,

• Conditional Statements,

• Looping Constructs, Functions,

• Data Structure, Lists, Dictionaries

• Understanding Standard Libraries in Python, Reading a CSV File in Python

2

Data Frames and basic operations with Data Frames, Indexing a Data Frame

• Libraries in Python –

• NumPy,

• SciPy, Matplotlib, Scikit-learn,

• Web development frameworks: Django/Flask

Unit

No.

Topics

1

FoundationinBusinessAnalyticsusingCognosInsight

• Introduction to Business Analytics in Digital Economy

• Analytics in Modern Enterprises

• Ways of importing data to work with IBM Cognos Insight

• Restructuring of data

• Create different visualizations relevant to Business uses

• Constructing a new workspace in Enterprise Business

• Different ways of formatting & entering data in reporting

• Sharing data & publish a workspace across business stake holders

• Business case studies & problem solving

Usage of Reporting in Marketing , HR , Operations Management

2

CognitiveanalyticsusingWatson

Analyzing data has no value if you cannot tell a story of insight out of it. This is where tools like Watson Analytics can take your analysis to the next level. Watson Analytics offers you the benefits of advanced analytics without the complexity.

• Use Excel to get you started and put you in the right analytical mindset.

• Learn, in a few hours, how to perform data exploration, predictive analytics and effortless dashboard and infographic creation.

Get answers and new insights to make confident decisions in minutes—all on your own!

3

Overview of Watson Analytics

• • Identify Watson Analytics components and the user interface (Overview)

Data

• • Identify data structure limitations and requirements

  • Upload data from flat files

  • Shape data before uploading

  • Upload data from Twitter

4

Work with data connections

• • Create a secure connection

  • Create a secure gateway

Refine data

• • Join data sets

  • Add a calculation to the data set

  • Add a data group to the data set

  • Add a hierarchy

5

Discover patterns, relationships, and predictive insights

• • Change column headings

  • Sort data

  • Perform analysis using natural language questions

  • Edit targets in a predictive analysis

  • Navigate the spiral in a predictive analysis

  • View decision rules and the decision tree

Provide added value to the analysis

• • Share the analysis through email

6

Assemble a display

• • Change visualization types in a display

  • Modify properties of a display

  • Filter visualizations in a display

Control access

• • Share assets and set access permissions

References – Create and Use expert Storybooks

Author an expert storybook, Use an expert storybook

Unit

No.

Topics

1

Introduction to Managerial-Economics: Nature and Scope; Approaches; Relevance to Social Economy: Concept of Scarcity; Concept of Division of Labor and Exchange; Factors of production, Utility, Market, Exchange; Market Mechanism

2

Market – Demand Side: Law of Demand; Exceptions, Elasticity Concepts; Downward Sloping Curve/ Shifts of Demand Curve; Notion of Competition/ Industry Demand Curve.

Market – Supply Side: Law of Supply; Exceptions, Elasticity Concepts

3

Equilibrium and Pricing: Price Determination

4

Concepts of Cost; Fixed/ Variable/ Average Costs; Marginal Cost, Opportunity Costs; Social Costs

5

Production and Returns: Variable Proportions; Returns to Scale: Increasing; decreasing; constant returns, Input-Output; Value Added (Production Based Interpretation, ‘Factor-payment’ Interpretation; Labour and other factor payments)

6

Market Structures: Competition, Monopoly, Imperfect Competition, Monopolistic Competition, Oligopoly: Pricing decisions under different market structures

Unit

No.

Topics

1

Perspective and approaches to research: Quantitative, Positivist and Qualitative Etic and Emic perspectives, Phenomenology Ethnography, Grounded Theory Research in Business

2

Research Process: Overview and steps involved in research

3

Research Problem and Proposal: Selecting defining and developing the problem and formulating the hypothesis

4

Research Design and Planning: Meaning of research design, kinds of research design, experimental and non-experimental designs, operational design and planning

5

Primary Data Collection Methods:

a.) Survey methods, Questionnaire design, Instruments for respondent communication, Experimentation

b.)Observation methods, Participant Observation, Interviews, Behavioral event interview, long interview, Focus group discussion, Case study method Sampling: Sampling design, criteria for selecting a sample, types of sampling, sample size

6

Secondary data and literature research, problems in secondary data

7

Scaling: Importance, scaling, basic types of scales

8

Data Processing and Preliminary Analysis: Editing, tabulating, coding, classification, analysis, interpretation, statistics in research, use of computers in research

9

Sampling Distribution and Hypothesis Testing

10

Report writing and presentation

Unit

No.

Topics

The Predictive Analytics Modeler will learn the essential analytics models to collect and analyze data efficiently. This will require skills in predictive analytics models, such as data mining, data collection and integration, nodes, and statistical analysis. The Predictive Analytics Modeler will use tools for market research and data mining in order to predict problems and improve outcomes.

1

Introduction to Data Mining

• CRISP-DM Methodology

• Introduction to SPSS Modeler – predictive data mining workbench

• SPSS Modeler Interface

2

The Data Mining Process

• • Business Understanding

  • Data Understanding

Data Preparation

3

Modeling Techniques

• • Introduction to Common Modeling Techniques

  • Cluster Analysis (Unsupervised Learning)

  • Classification & Prediction (Supervised Learning)

  • Classification – Training & Testing

  • Sampling Data in Classification

  • Predictive Modeling Algorithms in SPSS Modeler

• • Automated Selection of Algorithms

4

Model Evaluation

• Metrics for Performance Evaluation

• Accuracy as Performance Evaluation tool

• Overcoming Limitations of Accuracy Measure

• ROC Curves

5

Predictive Analytics Modeler

• Integrating data (methods, options, merging, and sampling)

• Deriving and reclassifying fields (CLEM)

• Looking for relationships (matrix, distribution, means, histogram, statistics and plot)

• Functions (conversion, string, and statistical)

• Data transformation

• Statistical. graphical and sample nodes

• Automated data mining and modeling

• Predictive models and customer segmentation

Unit

No.

Topics

No. of

lectures

1

Hortonworks Data Platform (HDP), Apache Ambari, Hadoop and the Hadoop Distributed File System, MapReduce and Yarn, Apache Spark, Storing and Quering data, ZooKeeper, Slider, and Knox, Loading data with Sqooq

2

Dataplane Service, Stream Computing, Data Science essentials, Drew Conway’s Venn Diagram – and that of others, The Scientific Process applied to Data Science, the steps in running a Data Science project

3

Languages used for Data Science (Python, R, Scala, Julia, …), Survey of Data Science Notebooks, Markdown language with notebooks, Resources for Data Science, including GitHub, Jupyter Notebook, Essential packages: NumPy, SciPy, Pandas, Scikit-learn, NLTK, BeautifulSoup.

4

Data visualizations: matplotlib, …, PixieDust, Using Jupyter “Magic” commands

5

Using Big SQL to access HDFS data, Creating Big SQL schemas and tables, Querying Big SQL tables, Managing the Big SQL Server, Configuring Big SQL security,

6

Data federation with Big SQL, IBM Watson Studio, Analyzing data with Watson Studio Prerequisites Skills

Unit

No.

No. of

lectures

Understanding tourism

·        Meaning & concepts

·        Types of tourism

·        Motives of tourism

Management of Tourism Services

·        environmental analysis and current Market Trends

·        Understanding tourist needs and Catering to the same

Marketing of Tourism Packages

·        Development of service package

·        7 P’s of tourism services

·        Special issues and challenges for the managers

Environmentally Responsible Tourism

& Tourism Regulations