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In the modern era of exorbitantly soaring quantity of raw data as well as processed information, challenges faced by decision makers including management professionals primarily consist of visualizing, analyzing and opting for the best or nearly best decisions which may help the organization in accomplishing myriad challenges. Business analytics is the scientific process of transforming data into insights for making better decisions used for data-driven or fact-based decision making, which is often seen as more objective than other alternatives for decision making. This course is aimed at providing the exposure as well as the understanding of the various processes and technique for data analysis. The attendees will have hands on experience on Business Analytics using data mining and statistical models. Besides, the attendees will be exposed to R as a computing aid for undertaking the statistical analysis.

2. Student Learning Outcomes (typically 3-5 bullet points)

At the end of the course you should
1. Be able to conceptualize a business issue as an analytical problem.
2. Be able to understand the significance of data and its processing
3. Be able to implement appropriate statistical models
4. Be able to interpret results and recommend actions based on output
5. Be able use R for analysis

3. Required Textbooks

· Business Analytics- The Science of Data Driven Decision Making by U Dinesh Kumar, Wiley Publishing.

4. Tentative Session Plan

Session	Topics/Activities	Readings
1	Introduction to Business Analytics	Ch-1
2-3	Exploring R capabilities in data analysis	Class Notes
4-5	Introduction to data and data pre-processing	Class Notes
6-7	Exploratory Data Analysis, Visualization and Dimensionality Reduction – I. Summary Statistics II. Visualizations III. Principal Component Analysis	Ch-2 and Class Notes
8	Simulation using R	Class Notes
9-12	Simple and Multiple Linear Regression -Concepts & applications
13	Logistic regression	Ch-11
14-16	Supervised Learning – Classification: Concepts and Applications I. Decision trees and Random forest II. Neural Networks	Ch-12 and Class Notes
17-18	Unsupervised Learning – Clustering: K-means and hierarchical Concepts and Applications	Ch-14
19	Performance Evaluation of Models	Class Notes
20	Innovative Applications	Class Notes

5. Evaluation

Class Participation-10

Assignments-10

Project-25

Quiz-25

End Term-30

6. Academic Integrity

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

Created By: Alora Kar on 08/21/2020 at 02:28 PM
Category: BM 19-21 T-V Doctype: Document