Analytics 101: Overview of Techniques and Application

Business Knowledge Series course

This course introduces the basic concepts in Analytics and the techniques that can be used to address research and business questions. It discusses the applications of predictive modeling methods such as decision trees and regression. Participants will learn about segmentation analysis and market basket analysis. Time series analysis is introduced, and methods for modeling and forecasting are presented. The course also covers survival/time-to-event analysis, incremental response or net-lift modeling, and other analytics techniques and applications. Hands-on examples are provided to reinforce analytical ideas and interpretation.

Learn how to

• identify the appropriate analytics technique/s to answer research or business questions
• interpret analytics results to aid in decision-making
• use predictive modeling methods for churn prediction, propensity scoring, etc.
• apply time series analysis for modeling and forecasting time-related data
• derive insights from segmentation/cluster analysis and profiling
• implement basic market basket analysis
• utilize survival analysis to model time-to-event data
• use incremental response modeling to optimize marketing efforts
• apply other analytics techniques such as neural networks and customer lifetime value

Who should attend

Anyone who wants to learn the basic concepts and techniques in Analytics that can be applied in business and research.

Formats available	Duration
Classroom:	2 days

There are no prerequisites for this course.

Introduction to Analytics

• Aspects of Business Analytics
• Introduction to Predictive Modeling
• Concepts in Pattern Discovery
• The Idea Behind Forecasting
• Other Areas of Analytics

Data Exploration and Preparation

• Exploratory Data Analysis
• Outlier Detection and Treatment
• Managing Missing Values
• Non-Numeric Variables

Essentials of Predictive Modeling

• Data and Models
• Predicting New Cases
• Variable Selection
• Optimizing Complexity through Data Partitioning
• Assessment Statistics

Decision Trees and Random Forests

• Cultivating Decision Trees
• Optimizing the Complexity of Decision Trees
• Random Forests

Regression Analysis

• The Regression Equation
• Selecting Regression Inputs
• Optimizing Regression Complexity
• Interpreting Regression Models

Segmentation Analysis

• Principles of Cluster Analysis
• The K-Means Clustering Algorithm
• Profiling Segments

Market Basket Analysis

• Association Analysis
• Sequence Analysis

Time Series Analysis and Forecasting

• Fundamentals of Time Series Forecasting
• Time Series Models
• Event Models
• Hierarchical Forecasting

Survival or Time-to-Event Analysis

• Survival Analysis Concepts
• Exploratory Data Analysis
• Time-to-Event Modeling

Incremental Response Modeling

• The Idea of Incremental Response
• Variable Pre-screening
• Models for Incremental Response and Revenue
• Revenue and Cost Settings

Other Analytical Techniques and Applications

• Optimization and Prescriptive Analytics
• Customer Lifetime Value
• Neural Network Models
• Text Mining and Sentiment Analysis
• Credit Risk Scorecards