Overview
 Knowledge of hypotheses testing in statistics and ANOVA concepts
 Basic knowledge of nonparametric data analysis concepts
 Introductory Python programming language
 Anaconda distribution for Python 3
 Use of Anaconda Jupyter notebook
While there are many courses in Python, Machine Learning and other Data sciencerelated topics, they tend to be covering several topics in a piecemeal fashion and often superficially. In other words, those courses are not laserfocused on a given topic that will provide instant mastery. This course is EXCLUSIVELY about testing parametric and nonparametric Statistical Hypotheses in Python 3.
It is highly recommended for Students, Data scientists, Analysts, Programmers and Statisticians who will be using Python as the main tool for data analysis and therefore need to understand HOW Python 3 powerful scientific libraries can be effectively used to tests hypotheses that they were used to performing using R, SAS, SPSS, Matlab or other tools.
The course has several strengths that should not be ignored.
 It is handson, uses realworld data and focuses on testing statistical hypotheses using Python 3.
 It is taught by an Adjunct Professor of Statistics who taught statistics for twelve years
 It is taught by a Data Scientist with a Statistics background and over twenty years of professional experience.
 it is extensive and cover all aspects of testing statistical hypotheses using Python
 It uses Jupyter notebook and markdowns to clearly document the codes and make them professional
 The course uses latex to write the statistical hypotheses to help users understand what is being tested/
In this course, you will learn how to test various statistical hypotheses using Python 3. The course covers the most relevant tests about the population parameters for one, two and many samples. In addition, the course covers ANOVA (Analysis of Variance) and many nonparametric tests. This course is handson with realworld datasets to help the students understand how to carry on the various tests.
 Anyone interested in learning how to test statistical hypotheses using Python
 Data scientists who need to make decisions using sound statistical hypotheses
 Statisticians who want to test statistical hypotheses using Python
 Anyone with the analytical skills who want to use Python as a tool of choice
Course Features
 Lectures 30
 Quizzes 0
 Duration 50 hours
 Skill level All levels
 Language English
 Students 0
 Assessments Yes
Curriculum

Section 1: Getting started with testing statistical hypotheses with Python 3
This section explains what we need to know about the class and the prerequisites

Section 2: Parametric tests of hypotheses using Python
Testing hypotheses in Python based on parametric assumptions
 Lecture 4: Testing if the data is normally distributed in Python 3
 Lecture 5: Test of hypothesis about a correlation coefficient in Python
 Lecture 6: One sample ttest using Python
 Lecture 7: One sample Z test about the population the mean in Python
 Lecture 8: One sample Z test about a population proportion p
 Lecture 9: One sample test about the population variance using Python
 Lecture 10: Two samples test about the population mean using ttest in Python
 Lecture 11: Twosamples test about the population mean using the Z test in Python
 Lecture 12: Two sample test about the population proportion using Python
 Lecture 13: Computing the left and right tailed Pvalues of the Student ttest in Python
 Lecture 14: Conducting a paired ttest in Python for related samples
 Lecture 15: Test of equality of variance using Barlett’s test in Python
 Lecture 16: Chisquared test of independence using Python
 Lecture 17: Chi square Goodness of fit test using Python
 Lecture 18: Performing a One way Analysis of Variance (ANOVA) in Python

Section 3: Hands on Project about testing Hypotheses in Python 3
Project files for the course Testing hypotheses using Python

SECTION 4: Nonparametric tests of hypotheses using Python
This section is about nonparametric tests of hypotheses using Python.
 Lecture 20: MannWhitneyWilcoxon test about two populations mean in Python
 Lecture 21: Computing the Fisher’s exact test in Python
 Lecture 22: Test of equality of two populations variances using Levene’s test in Python
 Lecture 23: Computing the Cochran Q test in Python
 Lecture 24: Wilcoxon nonparametric test about the population mean in Python – Part 1
 Lecture 25: Wilcoxon nonparametric test about the population mean in Python – Part 2
 Lecture 26: Computing KolmogorovSmirnov test in Python
 Lecture 27: Computing the Friedman test in Python (Part1)
 Lecture 28: Computing the Friedman test in Python (Part2)
 Lecture 29: Using Post hoc nonparametric tests in Python Scikit library

Section 5: Conclusion for Testing Statistical Hypotheses in Data science with Python
Concluding remarks for the course