Testing Statistical Hypotheses in Data science with Python 3

Parametric and nonparametric hypotheses testing using Python 3 advanced statistical libraries with real world data

Course Description
This course is designed to bridge the gap between understanding statistical hypothesis testing and applying it effectively using Python. It focuses on leveraging Python's capabilities to perform hypothesis testing on real-world datasets, offering students practical experience that can be directly applied in professional and academic settings.

Prerequisites
A strong foundation in the theory of hypothesis testing is essential. This includes familiarity with concepts such as null and alternative hypotheses, significance levels, test statistics, and p-values. If you’re comfortable with these concepts, you’re ready to dive into applying them programmatically.

What You Will Learn
Throughout the course, we explore a variety of statistical hypothesis tests, both parametric and non-parametric, including:

• One-sample tests for means: : Testing whether the mean of a population (e.g., average daily calorie intake) equals a specified value.

• Two-sample tests for means: Comparing the means of two independent groups (e.g., average blood pressure of patients on two different medications).

• One-sample test for proportions: Testing whether the proportion of a population (e.g., the percentage of people who prefer a certain product) equals a specified value.

• Two-sample test for proportions: Comparing the proportions of two independent groups (e.g., the percentage of smokers in two different cities).

• Paired tests: Testing differences in paired data (e.g., before-and-after scores of a treatment group).

• ANOVA (Analysis of Variance): Comparing the means of more than two groups (e.g., effectiveness of three different diets).

• Chi-square tests: Testing for independence between categorical variables (e.g., gender and preference for a product).

• Non-parametric tests: Mann-Whitney U, Kruskal-Wallis, and others for datasets that do not meet parametric test assumptions.

You’ll learn how to formulate hypotheses, calculate test statistics, identify rejection regions, and draw meaningful conclusions—all using Python.

Why Take This Course?

• Hands-On Learning: Every concept is illustrated with examples data relevant to health, business, education, engineering, etc.

• Practical Tools: You'll use Python Jupyter notebooks to write code. Where needed, the hypotheses are clearly well written using LaTeX to clearly document statistical hypotheses.

• Expert Instruction: The course is taught by a Data Scientist and Statistician with over 20 years of experience applying statistical methods in engineering, health, and business contexts.

• Comprehensive Content: This course focuses exclusively on hypothesis testing, ensuring depth and mastery of the topic.

Who Should Take This Course?
This course is ideal for:

• Health researchers performing clinical studies.

• Data Scientists and Analysts who draw conclusions from data by carrying out hypotheses testing.

• Statisticians applying advanced testing methods.

• Engineers validating process performance.

If your work involves testing hypotheses and interpreting data, this course will equip you with the skills to confidently analyze statistical problems using Python.