Course
Foundations of Inference in Python
AdvancedSkill Level
Updated 12/2025PythonProbability & Statistics4 hr14 videos48 Exercises4,050 XP3,559Statement of Accomplishment
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.Loved by learners at thousands of companies
Training 2 or more people?
Try DataCamp for BusinessCourse Description
Truly Understand Hypothesis Tests
What happens after you compute your averages and make your graphs? How do you go from descriptive statistics to confident decision-making? How can you apply hypothesis tests to solve real-world problems? In this four-hour course on the foundations of inference in Python, you’ll get hands-on experience in making sound conclusions based on data. You’ll learn all about sampling and discover how improper sampling can throw statistical inference off course.Analyze a Broad Range of Scenarios
You'll start by working with hypothesis tests for normality and correlation, as well as both parametric and non-parametric tests. You'll run these tests using SciPy, and interpret their output to use for decision making. Next, you'll measure the strength of an outcome using effect size and statistical power, all while avoiding spurious correlations by applying corrections.Finally, you'll use simulation, randomization, and meta-analysis to work with a broad range of data, including re-analyzing results from other researchers.Draw Solid Conclusions From Big Data
Following the course, you will be able to successfully take big data and use it to make principled decisions that leaders can rely on. You'll go well beyond graphs and summary statistics to produce reliable, repeatable, and explainable results.Prerequisites
Hypothesis Testing in Python1
Inferential Statistics and Sampling
In this chapter, we'll explore the relationship between samples and statistically justifiable conclusions. Choosing a sample is the basis of making sound statistical decisions, and we’ll explore how the choice of a sample affects the outcome of your inference.
2
Hypothesis Testing Toolkit
Learn all about applying normality tests, correlation tests, and parametric and non-parametric tests for sound inference. Hypothesis tests are tools, and choosing the right tool for the job is critical for statistical decision-making. While you may be familiar with some of these tests in introductory courses, you'll go deeper to enhance your inferential toolkit in this chapter.
3
Effect Size
In this chapter, you'll measure and interpret effect size in various situations, encounter the multiple comparisons problem, and explore the power of a test in depth. While p-values tell you if a significant effect is present, they don't tell you how strong that effect is. Effect size measures how strong an effect a treatment has. Master the factors underpinning effect size in this chapter.
4
Simulation, Randomization, and Meta-Analysis
You’ll expand your inferential statistics toolkit further with a look at bootstrapping, permutation tests, and methods of combining evidence from p-values. Bootstrapping will provide you with a first look at statistical simulation. In the lesson meta-analysis, you’ll learn all about combining results from multiple studies. You’ll end with a look at permutation tests, a powerful and flexible non-parametric statistical tool.
Foundations of Inference in Python
Course Complete
Earn Statement of Accomplishment
Add this credential to your LinkedIn profile, resume, or CVShare it on social media and in your performance reviewEnroll Now
FAQs
Is this course suitable for beginners?
his course is more targeted at intermediate level learners.
Will I receive a certificate at the end of the course?
Yes! Upon successful completion of the course, you would receive a Verified Certificate from DataCamp.
Who will benefit from this course?
This course is designed for anyone interested in applying their data knowledge to make sound conclusions and decisions. The skills taught in Foundations of Inference in Python are essential for roles such as data analyst, data scientist, business analyst, or a data engineer.
What tools will I use in the course?
This course uses Python and the SciPy library throughout to teach inferential statistics. By the end of the course, you will be able to effectively take big data and use it to make reliable and explainable results.
What topics will I cover in the course?
Foundations of Inference in Python covers topics such as sampling, hypothesis testing, effect size, simulation, bootstrapping, permutation tests, and meta-analysis. You’ll gain hands-on experience making sound conclusions based on data, that allow you to confidently produce repeatable results.
How long will it take to complete the course?
The entire course takes 4 hours to complete and includes 7 chapters. Each chapter builds on the concepts covered in the previous one, and dives in deeper to topics such as hypothesis testing, effect size and simulation.
Join over 19 million learners and start Foundations of Inference in Python today!
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.Grow your data skills with DataCamp for Mobile
Make progress on the go with our mobile courses and daily 5-minute coding challenges.