Testing Statistical Hypotheses in Data science with Python 3

Name: Testing Statistical Hypotheses in Data science with Python 3
Rating: 4.3 (48 reviews)

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

Created byLuc Zio

Last updated 12/2024

English

What you'll learn

Learn how to use Python libraries to perform a wide variety of parametric and non-parametric tests.
Calculate and interpret critical test statistics (e.g., t-values, F-values, chi-square values) to assess relationships and differences in datasets.
Use Python to compute p-values, interpret their significance in the context of statistical tests, and make decisions
Gain experience interpreting the results of hypothesis tests, and making data-driven conclusions applicable to fields like business, healthcare, etc..

Course content

5 sections • 30 lectures • 4h 10m total length

Welcome to class: Test of statistical hypotheses in Data science with Python 32:59
This lecture introduces the student about the requirements for the course
Installing Python Anaconda distribution on your PC8:57
In this lecture, we learn how to download and install Anaconda distribution for Python 3
Why is the Anaconda distribution of Python recommended for this course?
Testing hypotheses in Data Science with Python 3 class structure6:39
This lecture explains the structure of the course and talks about accessing and downloading files attached to each lecture.

Testing if the data is normally distributed in Python 313:33
In this lecture, we are using Normaltest in Python to test the hypothesis that the data is normally distributed
How to determine that data is normally distributed in Python
Test of hypothesis about a correlation coefficient in Python12:51
In this lecture, we are testing a hypothesis about a correlation coefficient using Python
Understanding correlations libraries used in Python for testing
One sample t-test using Python11:03
In this lecture, we demonstrate how to carry out a one sample t-test using Python.
Testing hypothesis about a population mean using ttest_1samp
One sample Z test about the population the mean in Python9:51
In this lecture, you will learn about how to carry out a one sample Z-test about the population mean in Python.
Understanding Python Z-test arguments
One sample Z test about a population proportion p9:53
In this lecture, we are testing a hypothesis about the population proportion when the sample size is large using Python
One sample test about the population variance using Python7:54
In this lecture, we are testing a one sample hypothesis about the population variance.
Two samples test about the population mean using t-test in Pyhton12:35
In this lecture, we learn how to test a two independent sample about the population when the sample sizes are small. You will be able to carry out similar tests using real world data with Python 3
Two-samples test about the population mean using the Z test in Python13:07
In this lecture, we are testing a hypothesis about the population mean when the sample size is large using the Z-test in Python
Two sample test about the population proportion using Python7:35
In this lecture, we are testing the equality of populations proportions from two sample using Python
Computing the left and right tailed P-values of the Student t-test in Python6:28
In this lecture, we learn how to compute the left and right tailed P-values of the Student t-test in Python
Conducting paired t-test in Python for related samples8:47
In this lecture, we will conduct a paired t-test when the samples are paired or related.
Test of equality of variance using Barlett's test6:42
In this lecture, we will test the equality of variance using Barlett's test
Performing a One way Analysis of Variance (ANOVA) in Python17:19
In this lecture, you will learn how to carry out a one way ANOVA test in Python 3

Chi square Goodness of fit test using Python8:56
In this lecture, we show how to perform Chi-square goodness of test statistic using Python
Chi-squared test of independence using Python5:26
In this lecture, we are doing a Chi square test of independence using Python
Man Whitney-Wilcoxon test about two populations mean in Python7:06
In this lecture, we are using the nonparametric Man Whitney test for testing the hypothesis of equality of two populations means.
Computing the Fisher exact test in Python12:45
In this lecture, you will learn how to carry out the Fisher's exact test in Python 3
Test of equality of population of variances using Levene's test in Python8:08
In this lecture, we use the Levene test of two sample equality of population variances using Python
Computing Chochran Q test in Python7:47
In this lecture, you will learn how to carryout a Cochran Q test in Python 3
Wilcoxon nonparametric test about the population mean in Python4:14
Computing Kolmogorov-Smirnov test in Python5:31
Wilcoxon nonparametric test about the population mean Part 26:56
After this lecture, you will know how to carry out a Wilcoxon signed ranked test in Python 3
Computing the Friedman test in Python8:46
In this lecture, we are using the Friedman test in Python to test if the distribution of scores are same across repeated measurement.
Computing the Friedmand test in Python (Part2)3:31
In this lecture, we are a conducting a Friedman test in which we found that the distribution of scores are not the same across repeated measurement.
Using Post hoc nonparametric tests in Python Scikit library10:47
In this lecture, you will learn how to carry out a Post-hoc test to determine which pairs are significantly different after the Friedman test was found to be significant.

Requirements

Having successfully completed a college-level Statistics course that extensively covers the theoretical foundations of hypothesis testing.
Basic knowledge of nonparametrics data analysis concepts
Knowledge of ANOVA concepts
Knowledge of the Python programming language
Install the Anaconda distribution for Python 3
Use of Anaconda Jupyter notebook

Description

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.

Who this course is for:

Professionals who regularly analyze data and make decisions based on statistical tests in domains such as business, healthcare, and technology
Engineers and scientists involved in R&D or quality assurance, requiring knowledge of statistical methods to test hypotheses and validate experimental data.
Decision-makers in marketing, finance, or operations who need to evaluate customer behavior, market trends, or operational performance through data analysis.
Data scientists who need to make decisions using sound statistical hypotheses
Statisticians who want to test statistical hypotheses using Python
Individuals with statistics and Python programming experience looking to enhance their data science skills by mastering statistical hypothesis testing techniques.

Testing Statistical Hypotheses in Data science with Python 3

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Course content

Introduction about the class3 lectures • 19min

Parametric tests of hypotheses using Python13 lectures • 2hr 18min

Hands on Project about testing Hypotheses in Python 31 lecture • 3min

Nonparametric tests of hypotheses using Python12 lectures • 1hr 30min

Conclusion for Testing Statistical Hypotheses in Data science with Python 31 lecture • 2min

Requirements

Description

Who this course is for: