Experimental Design in Python
Implement experimental design setups and perform robust statistical analyses to make precise and valid conclusions!
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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.Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Applied Statistics in Python
Go To Track- 1
Experimental Design Preliminaries
FreeBuilding 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.
Setting up experiments50 xpNon-random assignment of subjects100 xpRandom assignment of subjects100 xpExperimental data setup50 xpBlocking experimental data100 xpStratifying an experiment100 xpWhich was stratified?50 xpNormal data50 xpVisual normality in an agricultural experiment100 xpAnalytical normality in an agricultural experiment100 xp - 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.
Factorial designs: principles and applications50 xpUnderstanding marketing campaign effectiveness100 xpHeatmap of campaign interactions100 xpFactorial designs and randomized block designs50 xpRandomized block design: controlling variance50 xpImplementing a randomized block design100 xpVisualizing productivity within blocks by incentive100 xpANOVA within blocks of employees100 xpCovariate adjustment in experimental design50 xpImportance of covariates50 xpCovariate adjustment with chick growth100 xp - 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.
Choosing the right statistical test50 xpChoosing the right test: petrochemicals100 xpChoosing the right test: human resources100 xpChoosing the right test: finance100 xpPost-hoc analysis following ANOVA50 xpAnxiety treatments ANOVA100 xpApplying Tukey's HSD100 xpApplying Bonferoni correction100 xpP-values, alpha, and errors50 xpAnalyzing toy durability100 xpVisualizing durability differences100 xpRole of significance levels50 xpPower analysis: sample and effect size50 xpEffect size purpose50 xpEstimating required sample size for energy study100 xp - 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.
Synthesizing insights from complex experiments50 xpVisualizing loan approval yield100 xpExploring customer satisfaction100 xpEffectively communicating experimental data50 xpAddressing complexities in experimental data50 xpCheck for heteroscedasticity in shelf life100 xpExploring and transforming shelf life data100 xpApplying nonparametric tests in experimental analysis50 xpVisualizing and testing preservation methods100 xpFurther analyzing food preservation techniques100 xpCongratulations!50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Applied Statistics in Python
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HR WellnessChemical ReactionsInvestment ReturnsChick WeightsMarketing Campaign ConversationAthletic PerformanceCustomer SatisfactionLoan Approval Yieldcollaborators
prerequisites
Hypothesis Testing in PythonJames Chapman
See MoreData Science & AI 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.
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