BAR-P11

BAR-P11
PGDM 2011-13: Term-V

Business Analytics with R

Credits	3
Faculty Name	Subhajyoti Ray
Program	PGDM
Academic Year and Term	2012-13 Term V

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, and ANGOSS etc. This course will also introduce to R the open source and powerful analytical tool (like SAS) that is used by over 1 million analysts worldwide.

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 (QPI of 4.45 or more)
2. Not totally averse to some programming.

Session Plan

Session No	Topics/Activities	Reading
1	Over View of Analytics	DV – Chapters 1-3
2.	Analytics Methodology	Handouts
3.	Introduction to R – Data types and Operators, Missing values, Date Arithmetic	Handout, Demo
4.	Data Manipulation in R – Merge, Sort, Conditional Select etc.	Handout and PPT, Demo
5.	Data Manipulation in R – Merge, Sort, Conditional Select etc	Handout and PPT, Demo
6.	Refresh basic stat and Simple statistical tests in R	Handouts
7.	Graphs in R – simple plots	Handouts
8	Graphics in R - lattice (trellis) and other libraries	Handout/demo
9	Functions in R	Handout
10	Decision tree – structure and algorithms	Lab/ Handouts
11	Decision tree implementation and inference in R – Case of Bank Loan	Demo
12	Cluster analysis – Algorithms	Handouts
13	Cluster analysis – Store clustering	Handouts
14	Multiple linear regression – theory and application	Handouts
15	Logistic Regression – theory , diagnostics and application	Lecture notes
16	Logistic Regression – theory , diagnostics and application	PPTs
17	Logistic Regression – theory , diagnostics and application	PPTs
18.	Association Rules	PPT
19.	Group Presentations
20.	Guest Lecture

Evaluation

1. 2 Quizzes of 20% weight.
2. 1 group project of 20% weight.
3. 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. Davenport “Competing on Business Analytics” Reference text – for overview of analytics
2. Berry and Linoff – Data Mining Techniques – for easy understanding of some of the techniques
3. Several handouts and data sets will be made available during the course.

Academic Integrity

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

Created By: Debasis Mohanty on 05/28/2012 at 11:46 AM
Category: PGDM-II Doctype: Document

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