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Want to showcase your data analysis skills to recruiters or clients? In this video, I’ll show you exactly how to document your data analysis report on GitHub—from creating a professional repository to writing a clear and structured README file. 🔹 What you’ll learn in this video: Why documenting your project is important How to structure your GitHub repository Tips for writing a professional README file How to make your portfolio stand out to recruiters 📌 This tutorial is perfect for aspiring data analysts, data scientists, or anyone building a GitHub portfolio. If you find this helpful, don’t forget to like, share, and subscribe for more data analysis tips and tutorials.
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- Statistical methods: applying t-tests, chi-squared tests, analysis of variance (ANOVA), hypothesis testing, regression metrics, gini index, entropy, p-value, receiver operating characteristic/area under the curve (ROC/AUC), akaike information criterion/bayesian information criterion (AIC/BIC), and confusion matrix. Probability and modeling: explaining distributions, skewness, kurtosis, heteroskedasticity, probability density function (PDF), probability mass function (PMF), cumulative distribution function (CDF), missingness, oversampling, and stratification. Linear algebra and calculus: understanding rank, eigenvalues, matrix operations, distance metrics, partial derivatives, chain rule, and logarithms. Temporal models: comparing time series, survival analysis, and causal inference.


