Course Objectives

To impart basic knowledge on regulatory authorities and agencies governing the manufacture and sale of pharmaceuticals.

Learning Outcomes

To recognize, understand and apply the language, theory and models of the field of business analytics

Course Syllabus

Module:I   Overview of Business Analytics:

• Definition, Evolution, Architecture, Benefits, Future.
• Business, Analytics as Solution for Business Challenges.
• Effective Predictive Analytics, Integrating Analytics in Business Processes, Unstructured Data Analytics, Balanced Scorecard, Dashboards, KPI based on Dashboard and Scorecard,
• LOFT effect, Data Quality, Master Data Management, Data Profiling.
• Why are Business Analytics important

Module 2: Descriptive Analytics, Predictive Analytics and Prescriptive Analytics:

• Introduction to Descriptive Analytics, Visualizing and Exploring Data, Descriptive Statistics, Sampling and Estimation, Introduction to Probability Distributions
• Introduction to Predictive Analytics, Predictive Modeling (Logic-driven models and data driven models)
• Introduction to Prescriptive Analytics, Prescriptive Modeling, Non-linear Optimization

Module 3: Data Issues:

• Organization/sources of data, Importance of data quality, Dealing with missing or incomplete data, Data Classification
• Data Warehouse: Definition, Features, Applications, Types of data warehouse,
• Architecture: Business Analysis framework, 3-tier data warehouse framework.
• Data Warehouse Models: Virtual Warehouse, Data Mart and Enterprise warehouse.
• Metadata: Meaning and Categories, Role of metadata, Metadata respiratory, Challenges for metadata management, Data Cube
• Online Analytical Processing Server (OLAP): Types, OLAP operations, OLAP Vs Operational Database (OLTP).
• SCHEMA: Star Schema, Snowflake schema, Fact Constellation schema

Module 4: Data Mining and Testing: Definition, Concepts, Applications and Methods.

Module 5: Security: Security requirements, User Access, Data classification, User Classification, Data Movement, And Impact of security on design.

Module 6: Decision Modelling and Forecasting:

• Optimization: Using excel to solve business problems Eg: Marketing Mix, Portfolio optimization etc.
• Linear Programming: Introduction, Types of Linear programming problems/Models, Linear programming Model elements, Model formulation procedure, Computer based solutions for linear programming using Simplex method
• Duality and Sensitivity Analysis: What is Duality?, Duality and Sensitivity analysis problems
• Integer Programming: Introduction, Solving IP problems/Models
• Forecasting: Introduction, Types of Variation in Time series data, Simple Regression Model, Multiple Regression Models
• Simulation: Introduction, Types of Simulation
• Decision Theory: Introduction, Decision theory model elements, types of decision environments, decision theory formulation, decision making under uncertainty and risk, Decision trees.

Module 7: Fundamentals of R Language:

• Introduction, Basic Statistical Analysis using R, Process of Business Analytics,
• BA Process-Walk through with R,
• Multiple regression- Theory and Walk through with R,
• Clustering and Segmentation- Theory and Walk through with R

Text Books Recommended

• Fundaments of Business Analytics by RN Prasad and SeemaAcharya, Wiley India Publication
• Win With Advanced Business Analytics by Jean Paul Isson and Jesse S. Harroitt, Wiley Publication, 2013
• Successful Business Intelligence: Secrets to Making BI a Killer App by CindiHowson, Tata McGraw Hill Edition 2012
• Analytics at Work by Thomas H. Davenport, Jeanne G. Harris and Robert Morison, Harvard Business Press.

Session Plan

Session 1

Definition, Evolution, Architecture, Benefits, Future. Business, Analytics as Solution for Business Challenges.

PDF-Evans_Analytics2e_ppt_01

Session 2

Effective Predictive Analytics, Integrating Analytics in Business Processes, Unstructured Data Analytics, Balanced Scorecard, Dashboards, KPI based on Dashboard and Scorecard

PPT-introductiontobusinessanalyticspart1-160404203118

Session 3

LOFT effect, Data Quality, Master Data Management, Data Profiling. Why are Business Analytics important

PPT-Data quality

Session 4

Introduction to Descriptive Analytics, Visualizing and Exploring Data, Descriptive Statistics, Sampling and Estimation, Introduction to Probability Distributions

Descriptive Statisics

Session 5

Introduction to Predictive Analytics, Predictive Modeling (Logic-driven models and data driven models)

Data-Driven_Modelling_Concepts_Approaches_and_Expe

Session 6

Introduction to Prescriptive Analytics, Prescriptive Modeling, Non-linear Optimization

Prescriptiveanalytics-Literaturereviewandresearchchallenges

Session 7

Organization/sources of data, Importance of data quality, Dealing with missing or incomplete data, Data Classification Data Warehouse: Definition, Features, Applications, Types of data warehouse

Descriptive Classification

Session 8

Architecture: Business Analysis framework, 3-tier data warehouse framework. Data Warehouse Models: Virtual Warehouse, Data Mart and Enterprise warehouse.

https://www.slideshare.net/IT-BA-Certification/ba-toolstechniques

Session 9

Metadata: Meaning and Categories, Role of metadata, Metadata respiratory, Challenges for metadata management, Data Cube

https://www.slideshare.net/Dataversity/slides-metadata-management-for-the-governance-minded

Session 10

Online Analytical Processing Server (OLAP): Types, OLAP operations, OLAP Vs Operational Database (OLTP) SCHEMA: Star Schema, Snowflake schema, Fact Constellation schema

https://www.slideshare.net/WalidElbadawy/olap-on-line-analytical-processing

Session 11

Data Mining and Testing: Definition, Concepts, Applications and Methods.

https://www.slideshare.net/RaZoR141092/the-8-step-data-mining-process

Session 12

Security: Security requirements, User Access, Data classification, User Classification, Data Movement, And Impact of security on design.

https://www.slideshare.net/RaZoR141092/the-8-step-data-mining-process

Session 13

Optimization: Using excel to solve business problems Eg: Marketing Mix, Portfolio optimization etc.

https://www.slideshare.net/vanecekpavel/marketing-mix-optimization-5918003

Session 14

Linear Programming: Introduction, Types of Linear programming problems/Models, Linear programming Model elements, Model formulation procedure, Computer based solutions for linear programming using Simplex method

https://www.slideshare.net/nagendraamatya/linear-programming

Session 15

Duality and Sensitivity Analysis: What is Duality?, Duality and Sensitivity analysis problems

https://www.slideshare.net/KiranJadhav23/sensitivity-analysis-linear-programming-copy

Session 16

Integer Programming: Introduction, Solving IP problems/Models Forecasting: Introduction, Types of Variation in Time series data, Simple Regression Model, Multiple Regression Models

https://www.slideshare.net/JosephKonnully/linear-programming-ppt

Session 17

Simulation: Introduction, Types of Simulation.

https://www.slideshare.net/saneemnasim/simulation-modelling-31263694

Session 18

Decision Theory: Introduction, Decision theory model elements, types of decision environments, decision theory formulation, decision making under uncertainty and risk, Decision trees.

https://www.slideshare.net/iamkuldeep/decision-theory-66766212

Session 19

Introduction, Basic Statistical Analysis using R, Process of Business Analytics

https://www.slideshare.net/PrincyFrancisM/statistical-analysis-119122733

Session 20

BA Process-Walk through with R

https://www.slideshare.net/Vikash_Mishra/rpa-ba-138015469

Session 21

Multiple regression- Theory and Walk through with R

https://www.slideshare.net/crlmgn/multiple-regression-presentation

Session 22

Clustering and Segmentation- Theory and Walk through with R

https://www.slideshare.net/21_venkat/cluster-analysis-17406372

Case Studies

Case Studies