Course Syllabus
Introduction to Business Analytics
1.1) Evolution and Scope of Business Analytics
1.2) Data Storage and Data Base
1.3) Big Data and the Cloud
1.4) Analytics on Spreadsheet – Excel and Excel Solver
Data Exploration and Reduction
2.1) The scope of Data Mining
2.2) Data Exploration and Reduction
2.3) Classification Techniques
2.4) Cluster Analysis using Excel and XL Miner
Descriptive Analytics
3.1) Data Visualization and Exploration
3.2) Descriptive Statistical Measures
3.3) Sampling and Estimation
3.4) Statistical Inference
Predictive Analytics
4.1) Linear Regression
4.2) Multiple Regression
4.3) Logistic Regression
4.4) Trees and Neural Networks
4.5) Forecasting Techniques
Prescriptive Analytics – Decision Analysis
5.1) Making Decision Under Uncertainty
5.2) Making Decision Under Risk
5.3) Decision Trees and Sensitivity Analysis
5.4) Utility Theory
5.5) Monte Carlo Simulation
Prescriptive Analytics – Optimization
6.1) Linear Programming in Business Decisions
6.2) Multiple Objectives Optimization
6.3) Binary Optimization Problem
6.4) Integer Optimization Problem
1.1) Evolution and Scope of Business Analytics
1.2) Data Storage and Data Base
1.3) Big Data and the Cloud
1.4) Analytics on Spreadsheet – Excel and Excel Solver
Data Exploration and Reduction
2.1) The scope of Data Mining
2.2) Data Exploration and Reduction
2.3) Classification Techniques
2.4) Cluster Analysis using Excel and XL Miner
Descriptive Analytics
3.1) Data Visualization and Exploration
3.2) Descriptive Statistical Measures
3.3) Sampling and Estimation
3.4) Statistical Inference
Predictive Analytics
4.1) Linear Regression
4.2) Multiple Regression
4.3) Logistic Regression
4.4) Trees and Neural Networks
4.5) Forecasting Techniques
Prescriptive Analytics – Decision Analysis
5.1) Making Decision Under Uncertainty
5.2) Making Decision Under Risk
5.3) Decision Trees and Sensitivity Analysis
5.4) Utility Theory
5.5) Monte Carlo Simulation
Prescriptive Analytics – Optimization
6.1) Linear Programming in Business Decisions
6.2) Multiple Objectives Optimization
6.3) Binary Optimization Problem
6.4) Integer Optimization Problem
Frequently Asked Questions
Q1 : What is Business Analytics?
A1 : Business analytics is a field that drives practical, data-driven changes in a business. It is a practical application of statistical analysis that focuses on providing actionable recommendations. Analysts in this field focus on how to apply the insights they derive from data.
Q2 : What is decision tree?
A2 : A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning. source : https://en.wikipedia.org/wiki/Decision_tree
A1 : Business analytics is a field that drives practical, data-driven changes in a business. It is a practical application of statistical analysis that focuses on providing actionable recommendations. Analysts in this field focus on how to apply the insights they derive from data.
Q2 : What is decision tree?
A2 : A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning. source : https://en.wikipedia.org/wiki/Decision_tree