BA EM-15

BA EM-15
1yr MBA-Executive 2015-16T-IV

Business Analytics

Credits	2
Faculty Name	Subhajyoti Ray
Program	MBA(Executive)
Academic Year and Term	2015-16 Term IV

Course Description

In today’s fast changing business environment organizations face significant challenges in making decisions that become fruitful. Organizations are increasingly realizing that fact based decisions can come to their rescue by significantly reducing the chance of incorrect decision. This is largely being achieved by basing decision on insights drawn from statistical analysis of data.

This course takes you into the field of business analytics, which has been defined as the extensive use of data, statistical and quantitative analysis, exploratory and predictive models, and fact-based management to drive decisions and actions. Analytics projects rely heavily on the knowledge and use of some tools like SAS, R, Weka, and ANGOSS etc. This course will also introduce to R the open source and powerful analytical tool (like SAS) which we will use throughout the course.

Leaning Outcomes

At the end of the course you should
1. Be able use R for statistical analysis
2. Be able to define a business issue as an analytical problem.
3. Be able to choose from various analytical techniques in a given situation
4. Be able to understand, interpret and recommend actions based on analytical output

Pre Requisites
1. Good performance in statistics core paper
2. Not totally averse to some programming.

Session Plan

Topics/Activities	Reading
Over View of Analytics	PA – ER ( chapters 1-3)
Analytics Methodology	Handouts
Introduction to R – Data types and Operators, Missing values, Date Arithmetic	Handout, Demo
Data Manipulation in R – Merge, Sort, Conditional Select etc.	Handout and PPT, Demo
Data Manipulation in R – Merge, Sort, Conditional Select etc	Handout and PPT, Demo
Refresh basic stat and Simple statistical tests in R	Handouts
Graphs in R – simple plots	Handouts
Multiple linear regression – theory and application	Handouts
Logistic Regression – theory , diagnostics and application	Lecture notes
Logistic Regression – theory , diagnostics and application	PPTs
Logistic Regression – theory , diagnostics and application	PPTs
Decision tree implementation and inference in R	Handouts
Cluster analysis	Handouts
Association Rules	Handouts

Evaluation

1. 2 Quizzes of 30% weight each.
2. End-term 40%

No Makeup Exams. Marks for missed components will be equated to the minimum of the other attended components (in percentage terms).

Reading
1. Text : Predictive Analytics – Eric Siegel, Wiley (PA – ER)
2. R in Action
3. Davenport “Competing on Business Analytics”Reference text – for overview of analytics
4. Berry and Linoff – Data Mining Techniques – for easy understanding of some of the techniques
5. Several handouts and data sets will be made available.

Academic Integrity

Malpractice in any form will be dealt with as per manual of policies

Created By: Alora Kar on 10/16/2015 at 12:20 PM
Category: MBA EX-15-T-IV Doctype: Document

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