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BA-EMBA-19
MBA(Exe.) 2019-20: Term-IV

Business Analytics
Credits3
Faculty NameSoumyajyoti Datta
OfficeOld Building Top Floor, XIMB Campus
Contactsoumyajyoti@ximb.edu.in
ProgrammeEMBA(BM)
Term, Academic yearT-IV,2019-20
ConsultationPrior appointment over e-mail

Course Description

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 many 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. and hands on experience to the attendees on Business Analytics using Data Mining and Machine Learning principles. Besides, the attendees will be exposed to R as a computing aid for undertaking the statistical analysis.

Pre-requisites

Sound knowledge about statistics.


Leaning Outcomes
At the end of the course you should
1. Be able to conceptualize a business issue as an analytical problem.
2. Be able to explore the various statistical techniques for data analysis
3. Be able to understand, interpret and recommend actions based on analytical output
4. Be able use R for statistical analysis

Session Plan

SessionTopics/Activities
1Introduction to Business Analytics
2-3Exploring the R capabilities in data analysis
4-5 Introduction to data and data pre-processing
6Exploratory Data Analysis, Visualization and Dimensionality Reduction –
I. Summary Statistics
II. Visualizations
III. Principal Component Analysis
7Simulation using R
8-9Statistical Analysis –
I. Random Variables & Probability Distributions
II. Hypothesis Testing
10-13Simple and Multiple Linear Regression -Concepts applications
Variable selection
Shrinkage models
14Logit, Probit and Tobit Models
15ANOVA and ANCOVA
16-17Supervised Learning – Classification: Concepts and Applications
I. k-NN Algorithm
II. Decision trees
III. Neural Networks
IV. Support Vector Machines
18Unsupervised Learning – Clustering: Concepts and Applications-performance assessment
19Performance Evaluation:
Assessing Performance of Models
Overfitting v/s Underfitting
20Basics of heuristics and Doubt Clearing

Evaluation

1. Class Participation -10%
2. 2 Quizzes -15% each.
3. Project- 30%
4. End-term of -30%
No Makeup Exams for the missed components of evaluation.

Academic Integrity

Malpractice in any form will be dealt strictly as per the programme policy. Deadlines are sacrosanct.

Created By: Alora Kar on 03/12/2020 at 12:52 PM
Category: MBA(Exe.)2019-20 T-IV Doctype: Document

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