HomePythonPerforming Experiments in Python

Performing Experiments in Python

4 hours16 videos53 exercises

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Course Description

Data is all around us and can help us to understand many things. Making a pretty graph is great, but how can we tell the difference between a few outliers on a graph and a real, reliable effect? Is a trend that we see on a graph a reliable result or just random chance playing tricks? In this course, you will learn how to interrogate datasets in a rigorous way, giving clear answers to your questions. You will learn a range of statistical tests, how to apply them, how to understand their results, and how to deal with their shortcomings. Along the way, you will explore Olympic athlete data and the differences between populations of continents.

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1. 1

The Basics of Statistical Hypothesis Testing

Free

In this chapter, you will learn how to explore your data and ask meaningful questions. Then, you will discover how to answer these question by using your first statistical hypothesis tests: the t-test, the Chi-Square test, the Fisher exact test, and the Pearson correlation test.

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Welcome to the course!
50 xp
Getting started with plotnine
100 xp
Boxplots
100 xp
Density plots
100 xp
Student's t-test
50 xp
100 xp
One-sample t-test
100 xp
Two-sample t-test
100 xp
Testing proportion and correlation
50 xp
Chi-square test
100 xp
Fisher's exact test
100 xp
Pearson correlation
100 xp
2. 2

Design Considerations in Experimental Design

In this chapter, you will learn how to examine and multiple factors at once, controlling for the effect of confounding variables and examining interactions between variables. You will learn how to use randomization and blocking to build robust tests and how to use the powerful ANOVA method.

3. 3

Sample size, Power analysis, and Effect size

In this chapter, you will focus on ways to avoid drawing false conclusions, whether false positives (type I errors) or false negatives (type II errors). Central to avoiding false negatives is understanding the interplay between sample size, power analysis, and effect size.

4. 4

Testing Normality: Parametric and Non-parametric Tests

In this final chapter, you will examine the assumptions underlying statistical tests and learn about how that influences your experimental design. This will include learning whether a variable follows a normal distribution and when you should use non-parametric statistical tests like the Wilcoxon rank-sum test and the Spearman correlation test.

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datasets

Olympic datasetUN dataset

collaborators

Luke Hayden

Lead Data Scientist at Kobalt Music Group

Luke is a Lead Data Scientist at Kobalt Music Group, where he applies data science to the complexities of the music industry. Before moving into industry, he worked in academic research and holds a PhD in Zoology from the National University of Ireland, Galway. His greatest professional achievement is that one time he found a really good excuse to use a polar area graph.
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