Excited to Share My Latest Achievement! 🎉

I am thrilled to announce that I have successfully completed the "Hypothesis Testing in Python" course, marking a significant milestone on my journey towards earning the Associate Data Scientist in Python certification.

What I Learned 🐍📊

Throughout this course, I delved into various hypothesis testing techniques essential for any aspiring data scientist. Here's a sneak peek into what I covered:

Hypothesis Testing Fundamentals

• Assumptions in Hypothesis Testing: Understanding the core assumptions like normality, homogeneity of variance, and independence is crucial for applying the right tests.
• Testing Sample Size: Learning how sample size influences the reliability of test results.

Two-Sample and ANOVA Tests

• Two-Sample Tests: Techniques for comparing means between two independent groups.
• ANOVA (Analysis of Variance): Methods to compare means across multiple groups.

Proportion Tests

• One-Sample Proportion Tests: Comparing a sample proportion to a known or hypothesized population proportion.
• Two-Sample Proportion Tests: Assessing differences between proportions of two independent samples.

Non-Parametric Tests

• Wilcoxon Signed-Rank Test: A non-parametric alternative to the paired t-test for comparing two related samples.
• Wilcoxon-Mann-Whitney Test: Comparing differences between two independent samples without assuming normal distribution.
• Kruskal-Wallis Test: A non-parametric version of ANOVA for comparing more than two groups.

Sampling Techniques

In addition to hypothesis testing, the course covered various sampling techniques that are essential for collecting and analyzing data effectively:

• Convenience Sampling
• Simple Random Sampling
• Systematic Sampling
• Cluster Sampling
• Bootstrap Distributions

A Special Thanks

I am incredibly grateful to DataCamp and everyone who supported me on this journey. Your encouragement and resources have been invaluable.

For a more detailed explanation of these concepts, check out my related Medium article. If you're interested in seeing more exercises and examples, visit my Kaggle Notebook.

Show Your Support

Thank you for taking the time to explore my work. If you found it valuable, please consider showing your support through an upvote or leaving a comment/feedback to help improve the notebook.

Happy Learning!

Feel free to connect with me on LinkedIn and follow my journey as I continue to explore the world of data science. Your support and feedback are always welcome!

4o