Skip to main content
HomePythonExperimental Design in Python

Experimental Design in Python

Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!

Start Course for Free
4 Hours14 Videos47 Exercises

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
GroupTraining 2 or more people?Try DataCamp For Business

Loved by learners at thousands of companies

Course Description

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.
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more
Try DataCamp for BusinessFor a bespoke solution book a demo.
  1. 1

    Experimental Design Preliminaries

    Free

    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.

    Play Chapter Now
    Setting up experiments
    50 xp
    Non-random assignment of subjects
    100 xp
    Random assignment of subjects
    100 xp
    Experimental data setup
    50 xp
    Blocking experimental data
    100 xp
    Stratifying an experiment
    100 xp
    Which was stratified?
    50 xp
    Normal data
    50 xp
    Visual normality in an agricultural experiment
    100 xp
    Analytical normality in an agricultural experiment
    100 xp
  2. 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.

    Play Chapter Now
    Factorial designs: principles and applications
    50 xp
    Understanding marketing campaign effectiveness
    100 xp
    Heatmap of campaign interactions
    100 xp
    Factorial designs and randomized block designs
    50 xp
    Randomized block design: controlling variance
    50 xp
    Implementing a randomized block design
    100 xp
    Visualizing productivity within blocks by incentive
    100 xp
    ANOVA within blocks of employees
    100 xp
    Covariate adjustment in experimental design
    50 xp
    Importance of covariates
    50 xp
    Covariate adjustment with chick growth
    100 xp
  3. 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.

    Play Chapter Now
    Choosing the right statistical test
    50 xp
    Choosing the right test: petrochemicals
    100 xp
    Choosing the right test: human resources
    100 xp
    Choosing the right test: finance
    100 xp
    Post-hoc analysis following ANOVA
    50 xp
    Anxiety treatments ANOVA
    100 xp
    Applying Tukey's HSD
    100 xp
    Applying Bonferoni correction
    100 xp
    P-values, alpha, and errors
    50 xp
    Analyzing toy durability
    100 xp
    Visualizing durability differences
    100 xp
    Role of significance levels
    50 xp
    Power analysis: sample and effect size
    50 xp
    Effect size purpose
    50 xp
    Estimating required sample size for energy study
    100 xp
  4. 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.

    Play Chapter Now
    Synthesizing insights from complex experiments
    50 xp
    Visualizing loan approval yield
    100 xp
    Exploring customer satisfaction
    100 xp
    Effectively communicating experimental data
    50 xp
    Addressing complexities in experimental data
    50 xp
    Check for heteroscedasticity in shelf life
    100 xp
    Exploring and transforming shelf life data
    100 xp
    Applying nonparametric tests in experimental analysis
    50 xp
    Visualizing and testing preservation methods
    100 xp
    Further analyzing food preservation techniques
    100 xp
    Congratulations!
    50 xp
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

Datasets

HR WellnessChemical ReactionsInvestment ReturnsChick WeightsMarketing Campaign ConversationAthletic PerformanceCustomer SatisfactionLoan Approval Yield

Collaborators

Collaborator's avatar
Dr. Chester Ismay

Prerequisites

Hypothesis Testing in Python
James Chapman HeadshotJames Chapman

Curriculum Manager, DataCamp

James is a Curriculum Manager at DataCamp, where he collaborates with experts from industry and academia to create courses on AI, data science, and analytics. He has led nine DataCamp courses on diverse topics in Python, R, AI developer tooling, and Google Sheets. He has a Master's degree in Physics and Astronomy from Durham University, where he specialized in high-redshift quasar detection. In his spare time, he enjoys restoring retro toys and electronics.

Follow James on LinkedIn
See More

What do other learners have to say?

    FAQs

    Join over 13 million learners and start Experimental Design in Python today!

    Create Your Free Account

    GoogleLinkedInFacebook

    or

    By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.