Training Course: Advanced Data

Introduction

This course focuses on advanced analytical techniques that are routinely used in industry and academic research. Advanced analytics is a tool that can help you get more out of your data.   Predictive analytics can use these clean sets and existing insights to extrapolate and make predictions and projections about future activity, trends, and consumer behaviors.

Course Objectives of Advanced Data

• Estimation of basic models in R/ Python
• Approaches to dealing with missing data.
• Data reduction techniques

Course Outlines of Advanced Data

Day 1

Principal components analysis

• Rescaling principal components
• Choosing the number of components
• Component scores 

Day 2

Factor analysis

•  Factor extraction and common factor analysis
•  Factor rotation and factor scores

Item response theory models

• Latent trait models and item response function
• Logistic and normal IRT models and interpreting the IRT score scale

Day 3

Structural equation modeling and path models

• Path diagrams
• Structural equations& designing SEMs•
• Confirmatory factor analysis
• Latent class models

Day 4

Clustering and cluster analysis, and association rules

• Classification in social sciences
• Hierarchical clustering; k-means clustering;
• Model-based clustering
• Visualization of clustering results

Day 5

Missing data

• Missing data generation and mechanisms
• Multiple imputations
• Pattern mixture models
• Data missing not at random
• Missing data in longitudinal studies