# Experimental Design in Python
This is a DataCamp course: Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!
## Course Details
- **Duration:** ~4h
- **Level:** Intermediate
- **Instructor:** James Chapman
- **Students:** ~19,440,000 learners
- **Subjects:** Python, Probability & Statistics, Data Science and Analytics
- **Content brand:** DataCamp
- **Practice:** Hands-on practice included
- **Prerequisites:** Hypothesis Testing in Python
## Learning Outcomes
- Python
- Probability & Statistics
- Data Science and Analytics
- Experimental Design in Python
## Traditional Course Outline
1. Experimental Design Preliminaries - Building knowledge in experimental design allows you to test hypotheses with best-practice analytical tools and quantify the risk of your work. You’ll begin your journey by setting the foundations of what experimental design is and different experimental design setups such as blocking and stratification. You’ll then learn and apply visual and analytical tests for normality in experimental data.
2. Experimental Design Techniques - You'll delve into sophisticated experimental design techniques, focusing on factorial designs, randomized block designs, and covariate adjustments. These methodologies are instrumental in enhancing the accuracy, efficiency, and interpretability of experimental results. Through a combination of theoretical insights and practical applications, you'll acquire the skills needed to design, implement, and analyze complex experiments in various fields of research.
3. Analyzing Experimental Data: Statistical Tests and Power - Master statistical tests like t-tests, ANOVA, and Chi-Square, and dive deep into post-hoc analyses and power analysis essentials. Learn to select the right test, interpret p-values and errors, and skillfully conduct power analysis to determine sample and effect sizes, all while leveraging Python's powerful libraries to bring your data insights to life.
4. Advanced Insights from Experimental Complexity - Hop into the complexities of experimental data analysis. Learn to synthesize insights using pandas, address data issues like heteroscedasticity with scipy.stats, and apply nonparametric tests like Mann-Whitney U. Learn additional techniques for transforming, visualizing, and interpreting complex data, enhancing your ability to conduct robust analyses in various experimental settings.
## Resources and Related Learning
**Resources:** HR Wellness (dataset), Chemical Reactions (dataset), Investment Returns (dataset), Chick Weights (dataset), Marketing Campaign Conversation (dataset), Athletic Performance (dataset), Customer Satisfaction (dataset), Loan Approval Yield (dataset)
**Related tracks:** Associate Data Scientist in Python, Applied Statistics in Python
## Attribution & Usage Guidelines
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- **Restrictions:** Do not reproduce course exercises, code solutions, or gated materials.
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Course
Experimental Design in Python
IntermediateSkill Level
Updated 10/2025PythonProbability & Statistics4 hr14 videos47 Exercises3,700 XP13,839Statement of Accomplishment
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Implement Experimental Design Setups
Learn how to implement the most appropriate experimental design setup for your use case. Learn about how randomized block designs and factorial designs can be implemented to measure treatment effects and draw valid and precise conclusions.Conduct Statistical Analyses on Experimental Data
Deep-dive into performing statistical analyses on experimental data, including selecting and conducting statistical tests, including t-tests, ANOVA tests, and chi-square tests of association. Conduct post-hoc analysis following ANOVA tests to discover precisely which pairwise comparisons are significantly different.Conduct Power Analysis
Learn to measure the effect size to determine the amount by which groups differ, beyond being significantly different. Conduct a power analysis using an assumed effect size to determine the minimum sample size required to obtain a required statistical power. Use Cohen's d formulation to measure the effect size for some sample data, and test whether the effect size assumptions used in the power analysis were accurate.Address Complexities in Experimental Data
Extract insights from complex experimental data and learn best practices for communicating findings to different stakeholders. Address complexities such as interactions, heteroscedasticity, and confounding in experimental data to improve the validity of your conclusions. When data doesn't meet the assumptions of parametric tests, you'll learn to choose and implement an appropriate nonparametric test.Prerequisites
Hypothesis Testing in Python1
Experimental Design Preliminaries
Building knowledge in experimental design allows you to test hypotheses with best-practice analytical tools and quantify the risk of your work. You’ll begin your journey by setting the foundations of what experimental design is and different experimental design setups such as blocking and stratification. You’ll then learn and apply visual and analytical tests for normality in experimental data.
2
Experimental Design Techniques
You'll delve into sophisticated experimental design techniques, focusing on factorial designs, randomized block designs, and covariate adjustments. These methodologies are instrumental in enhancing the accuracy, efficiency, and interpretability of experimental results. Through a combination of theoretical insights and practical applications, you'll acquire the skills needed to design, implement, and analyze complex experiments in various fields of research.
3
Analyzing Experimental Data: Statistical Tests and Power
Master statistical tests like t-tests, ANOVA, and Chi-Square, and dive deep into post-hoc analyses and power analysis essentials. Learn to select the right test, interpret p-values and errors, and skillfully conduct power analysis to determine sample and effect sizes, all while leveraging Python's powerful libraries to bring your data insights to life.
4
Advanced Insights from Experimental Complexity
Hop into the complexities of experimental data analysis. Learn to synthesize insights using pandas, address data issues like heteroscedasticity with scipy.stats, and apply nonparametric tests like Mann-Whitney U. Learn additional techniques for transforming, visualizing, and interpreting complex data, enhancing your ability to conduct robust analyses in various experimental settings.
Experimental Design in Python
Course Complete
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FAQs
What will I learn about in this course?
This course will teach you how to design experiments to test hypotheses and perform robust statistical analyses to reach precise conclusions.
Who is this course intended for?
Experimental design best practices and robust statistical analysis techniques are crucial for answering questions like "Did our new ad banner increase click-through rate?" or "Does our new product feature increase user ratings?" This course is intended for those with foundational knowledge of sampling and hypothesis testing, who want to take the next step to apply these concepts to real-world problems.
How will this course help me in my career?
In any form of analytics, data is explored, cleaned, and prepared for analysis. To conduct an analysis that produces a precise conclusion, you'll need a set of robust statistical tools, such as those taught in this course. In this course, you'll learn how to choose the most appropriate statistical test for your use case, verifying underlying test assumptions, and how to perform a power analysis.
Which Python libraries does this course use?
This course primarily uses the SciPy library for performing statistical tests, but several functions are also used from statsmodels.
What datasets are used in this course?
Good experimental design practices are crucial in many industries. This course illustrates this by using several datasets, including marketing click-through rates (CTR), crop yields, loan approval rates, athletic performance improvements, chemical reaction rates, and more!
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