Data Science Foundation: Contents

  • Data Science overview
    • Analytics Delivery Framework
    • Analytics Problems and Approaches
  • Statistics Foundation
    • Hypothesis Testing
    • Correlation
    • Chi-square and ANOVA tests
    • Examples/Scenarios
  • Multiple Regression
    • Modeling Framework
    • Regression Concepts
    • ANOVA & R2
    • Multi-collinearity
    • Example - Cash Collection Forecasting
  • Logistic Regression
    • Model Development Framework
    • Logistic Regression and Model Performance Statistics
    • Interpreting Logistic Regression Output
    • Calculations of Concordance, KS, Gini
    • Example - MarketingResponse Model
  • Subjective Segmentation/Clustering
    • Segmentation Framework
    • K Means Algorithm - Explained
    • K Means clustering – Explained using London Olympic Athlete Data
  • Case Study project for candidates to work on