Data Science Full Course

  Comprehensive Theory with Examples

  Several Solved Exercises

 Introduction

 What is the Purpose of Statistics

 Population vs Sample

 Inferential Approaches: Estimation

 Inferential Approaches: Hypothesis Testing

 Data Types(Qualitative and Quantitative), Samples

 Trials, Experiments, Events, Independence and Likelihood

 What is a Distribution?

 Discrete: Binomial and Bernoulli

 Discrete: Negative Binomial and Geometric

 Discrete: Poisson

 Continuous: Exponential

 Continuous: Uniform

 Continuous: Normal and Central Limit Theorem

 Standardization Z-Score

 T-Student Distribution

 When to use Normal vs T-Student?

 Continuous: Chi-Squared

 F-Distribution and ANOVA

 Probability Functions Revisited: Tables, PMF, PDF and CDF

 What is an Hypothesis test, procedures and errors

 Estimators and Main Techniques

 Estimators: OLS

 Estimators: MLE

 Estimators: MME

 Summary of Section I

 Part II: Exercises: Hypothesis Testing: 1-Way ANOVA

 Part II: Exercises: Hypothesis Testing: 2-Way ANOVA

 Part II: Exercises: Confidence Intervals: How to Build a Confidence Interval

 PART 2: Exercises: Find k, E(X) and V(X) from Functions

 Course Summary