Download appDownload App

Data Science Full Course

Inferential Statistics - Full Course

  • All levels
  • English

Course Description

Inferential Statistics - Full Course

Inferential Statistics - Full Course

What you’ll learn
  • Comprehensive Theory with Examples
  • Several Solved Exercises

Covering Topics

1
Introduction
2
What is the Purpose of Statistics
3
Population vs Sample
4
Inferential Approaches: Estimation
5
Inferential Approaches: Hypothesis Testing
6
Data Types(Qualitative and Quantitative), Samples
7
Trials, Experiments, Events, Independence and Likelihood
8
What is a Distribution?
9
Discrete: Binomial and Bernoulli
10
Discrete: Negative Binomial and Geometric
11
Discrete: Poisson
12
Continuous: Exponential
13
Continuous: Uniform
14
Continuous: Normal and Central Limit Theorem
15
Standardization Z-Score
16
T-Student Distribution
17
When to use Normal vs T-Student?
18
Continuous: Chi-Squared
19
F-Distribution and ANOVA
20
Probability Functions Revisited: Tables, PMF, PDF and CDF
21
What is an Hypothesis test, procedures and errors
22
Estimators and Main Techniques
23
Estimators: OLS
24
Estimators: MLE
25
Estimators: MME
26
Summary of Section I
27
Part II: Exercises: Hypothesis Testing: 1-Way ANOVA
28
Part II: Exercises: Hypothesis Testing: 2-Way ANOVA
29
Part II: Exercises: Confidence Intervals: How to Build a Confidence Interval
30
PART 2: Exercises: Find k, E(X) and V(X) from Functions
31
Course Summary

Curriculum

      In this Course you will learn the basics of Inferential Statistics, the basis for Machine Learning and Artificial Intelligence.

Frequently Asked Questions

This course is for everyone interested in developing their skills in Data Science. Nop background is required.