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Business Analytics

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Business Analytics

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ELECTIVE- BUSINESS ANALYTICS

Subjects:-

 Business Analytics- Paper 1st

 Big Data – Paper 2nd

 Curriculum of Business Analytics- Paper 1st         (IIBM-E1087)

S.No.

Topics

1

Product-Market Fit: Gap Analysis: –Gap Analysis, Carrying Out Gap Analysis, Steps in Gap Analysis, Conducting a Representative Survey for Gap Analysis, Case Studies.

2

Factor Analysis: -Applying Factor Analysis on Attributes for a Fast-food Eating Place, Case Studies.

3

Concepts of Cluster Analysis: -Similarity Measures, Applying Cluster Analysis to the Database for Measuring Awareness to Global Warming/Climate Change Phenomenon, Case Study.

4

Linear Discriminant Analysis: -Linear Discriminant Analysis Model, Case Studies.

5

Logistic Regression: -Theoretical Formulation of Logistic Regression, Mathematical Interpretation of Logistic Regression, Indicator for Model Fit, Case Studies, Testing the Reliability/Consistency of the Different Factors Measured, Applying Logistic Regression.

6

RFM Analysis: –Enhancing Response Rates with RFM Analysis, Case Studies.

7

Decision Tree Approach with CHAID: –How the Algorithm Works, Case Studies.

8

Structural equation Modelling: –Case Studies.

9

Conjoint Analysis: –Methodology for Conjoint Analysis, Case Studies.

Curriculum of  Big Data – Paper 2nd         (IIBM-E1088)  

S.No

Topics

1

What is Big Data and Why Is It Important: –A Flood of Mythic “Start-Up” Proportions, Big Data Is More Than Merely Big, Why Now?, A Convergence of Key Trends, Relatively Speaking, A Wider Variety of Data, The Expanding Universe of Unstructured data, Setting the Tone at the Top.

2

Industry examples of Big Data: –Digital Marketing and the Non-Line World, Don’t Abdicate Relationships, Is IT Losing Control of Web Analytics?, Database Marketers, Pioneers of Big Data, Big Data and the New School of Marketing, Consumers Have Changed. So Must Marketers, The Right Approach: Cross-Channel Lifecycle Marketing, Social and Affiliate Marketing, Empowering Marketing with Social Intelligence, Fraud and Big Data, Risk and Big Data, Credit Risk Management, Big Data and Algorithmic Trading, Crunching Through Complex Interrelated Data, Intraday Risk Analytics, a Constant Flow of Big Data, Calculating risk in Marketing, Other Industries Benefit from Financial Services’Risk experience, Big data and Advances in Health Care, “Disruptive Analytics”, A Holistic Value Proposition, Bl Is Not Data Science, Pioneering New Frontiers in Medicine, Advertising and Big data: From Papyrus to Seeing Somebody, Big Data Feeds the Modern-Day Donald Draper, Reach, Resonance, and Reaction, The Need to Act Quickly, Measurement Can Be Tricky, Content Delivery matters Too, Optimization and Marketing Mixed Modeling, Beard’s Take on the Three Big Data Vs in Advertising, Using Consumer Products as a Doorway.

3

Big Data Technology:-The Elephant in the Room: Hadoop’s parallel World, Old Vs New Approaches, Data Discovery: Work the Way People’s Minds Work, Open-Source Technology for Big Data Analytics, The Cloud and Big Data, Predictive Analytics Moves into the Limelight, Software as a Service BL, Mobile Business Intelligence is Going Mainstream, ease of Mobile Application Deployment, Crowdsourcing Analytics, Inter-and Trans-Firewall Analytics, R&D Approach Helps Adopt New Technology, Adding Big Data Technology into the Mix, Big Data Technology Terms, Data Size.

4

Information Management: –The Big Data Foundation, Big Data Computing Platforms, Big Data Computation, More on Big Data Storage, Big Data Computational Limitations, Big Data Emerging Technologies.

5

Business Analytics:-The Last Mile in Data Analysis, Geospatial-Intelligence Will Make Your Life Better, Listening: Is It Signal or Noise?, Consumption of Analytics, From Creation to Consumption, Visualizing: How to Make It Consumable?, Organizations Are Using Data Visualization as a Way to Take Immediate Action, Moving from Sampling to Using All the Data, Thinking Outside the Box, 360° Modeling, Need for Speed, Let’s Get Scrappy, What Technology Is Available?, Moving from Beyond the Tools to Analytic Applications.

6

The People Part of the Equation:Rise of the Data Scientist, Learning over Knowing, Agility, Scale and Convergence, Multidisciplinary Talent, Innovation, Cost Effectiveness, Using Deep Math, Science, and Computer Science, The 90/10 Rule and Critical Thinking, Analytic Talent and Executive Buy-in, Developing Decision Sciences Talent, Holistic View of Analytics, Creating Talent for Decision Sciences, Creating a Culture that Nurtures Decision Sciences talent, Setting Up the Right Organizational Structure for Institutionalizing Analytics.

7

Data Privacy and Ethics: –The Privacy Landscape, The Great Data Grab Isn’t New, Preferences, Personalization and Relationships, Rights and Responsibility, Playing in a Global Sandbox, Conscientious and Conscious Responsibility, Privacy May Be the Wrong Focus, Can Data Be Anonymized?, Balancing for Counterintelligence, Now What?.