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Duration: 32 Hours

The Analytics course introduces participants to fundamental concepts of statistics, and guides them all the way to building predictive models using multiple linear and logistic regressions. All the topics are explained with the help of hands-on practice with live case studies and data, enabling a deeper understanding of the underlying concepts of analytics.

Course Modules

  • Introduction to analytics
    • Need for analytics
    • Analytics use in different industries
    • Challenges in adoption of analytics
    • Overview of Course Contents
  • Data understanding
    • Data types (Nominal, Ordinal, Interval and Ratio)
  • Descriptive statistics
    • Tabular & Graphical Method
    • Summary statistics
  • Introduction to some statistical terminologies and inferences
    • Population, Sample and Random variables
    • Point and Interval Estimations
    • Probability
    • Discrete/Continuous Probability Distributions
  • Hypothesis Testing
    • Importance of formulating and validating the hypothesis
    • Formulation of hypothesis (Null and alternate)
    • Testing association and differences
    • Statistical significance and test statistic
    • Level of significance
  • Z-Test, T-Test, Chi-Square test, ANOVA
  • Parametric & Non-Parametric test
  • Correlation & Regression
  • Linear Regression
    • Case Study on Multiple Regression
  • Logistic Regression
    • Case Study on Logistic Regression
  • Cluster Analysis
    • Case Study on Cluster Analysis
  • Factor Analysis
    • Case Study on Factor Analysis