Business Analytics Syllabus @ Vigor Council
Business
Analytics Syllabus
Learning Objectives
n Familiarize students with basics of predictive
and prescriptive analytics in order to solve some business problems using
different types of data
n Students should be able to solve business
problems, analyze datasets using various relevant statistical software
packages, and interpret and effectively communicate the results
Learning Outcomes
n Understand fundamental concepts of machine
learning
n Build basic models Statistical Softwares
n Interpret results
n Compare results of different models to select
the best fit
n Drive business decisions using model output
Unit 1
1.
Introduction
to Business Analytics and Prescriptive Analytics
1.1.
Introduction
to Business Analytics
1.2.
Role of
Analytics for Data Driven Decision Making
1.3.
Types of Business
Analytics
1.3.1.
Descriptive
Analytics
1.3.2.
Predictive
Analytics
1.3.3.
Prescriptive
Analytics
1.4.
Introduction
to Big Data Analytics
1.4.1.
Understanding
5Vs
1.4.2.
Key
components of Big Data Analytics
1.4.3.
Application
areas of Big Data Analytic
1.5.
Web and
Social Media Analytics
1.6.
Overview
of Machine Learning Algorithms
1.7.
Introduction
to relevant statistical software packages and carrying out descriptive analysis
through it
Unit 2
2.
Predictive
Analytics 1
2.1.1.
Estimation
of Parameters
2.1.2.
Validation
of simple linear regression model
2.1.3.
Coefficient
of determination
2.1.4.
Significant
Tests
2.1.5.
Residual
Analysis
2.1.6.
Confident
and Predication Intervals
2.2.
Multiple
Linear Regression
2.2.1.
Interpretation
of Partial regression coefficients
2.2.2.
Working
with categorical variables
2.2.3.
Multi-collinearity
and VIF
2.2.4.
Outlier
Analysis
2.2.5.
Auto-correlation
2.2.6.
Transformation
of variables
2.2.7.
Variable
Selection in Regression Model Building
Unit 3
3.
Predictive
Analytics 2
3.1.
Logistic
and Multinomial Regression
3.1.1.
Logistic
Function
3.1.2.
Estimation
of probability using logistic regression
3.1.3.
Omnibus
Test
3.1.4.
Wald Test
3.1.5.
Hosmer
Lemshow Test
3.1.6.
Pseudo R
Square
3.2.
Model
Performance
3.2.1.
Classification
Table
3.2.1.1.
Sensitivity
3.2.1.2.
Specificity
3.2.1.3.
Accuracy
Paradox
3.2.1.4.
Precision
3.2.1.5.
F Score
3.2.2.
Gini
Coefficient
3.2.3.
ROC
3.2.4.
AUC
3.2.5.
Model for
determining the optimal cutoff probability
Unit 4
4.
Machine
Learning Models
4.1.
Decision
Trees
4.1.1.
Introduction
4.1.2.
Chi-Square
Automatic Interaction Detection
4.1.3.
Bonferroni
Correction
4.1.4.
Classification and Regression Tree
4.1.5.
Gini
Impurity Index
4.1.6.
Entropy
4.1.7.
Cost based
splitting criteria
4.1.8.
Ensemble
Methods
4.1.9.
Random
Forest
4.2.
Clustering
4.2.1.
Introduction
4.2.2.
Distance
and Dissimilarity measures used in clustering
4.2.3.
Quality
and Optimal Number of clusters
4.2.4.
Clustering
Algorithms
4.2.5.
K-means
clustering
4.2.6.
Hierarchical
Clustering
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