Analyze your data in Python

on YouTube ↗01h 53m

" summary

Step‑by‑step Python workflow: Jupyter Notebook with pandas + Jupyter + Plotly to load fake Excel/CSV data, inspect dtypes, drop or impute NaNs, and detect outliers via boxplots and a custom IQR trimming function (examples remove values <4 or >59, earlier rough cutoff 117). Covers descriptive stats (mean/median, histograms), Pearson/Spearman correlations, t‑tests/paired t, ANOVA/MANOVA, Wilcoxon/Mann‑Whitney/Friedman nonparametrics, basic linear regression and R², interprets results by p-value < 0.05 and shows most examples non‑significant; notebooks and code are shared.

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" chapters (12)

1. 0:00Introduction
2. 3:25Data preprocessing
3. 13:12Renaming columns and subsets
4. 21:00Missing values and outliers
5. 30:41Removing extreme values
6. 42:36Descriptive statistics
7. 55:50Inferential statistics
8. 1:06:55T-tests independent/dependent
9. 1:17:26ANOVA and alternatives
10. 1:27:19Non-parametric dependent tests
11. 1:33:03Correlation and regression
12. 1:43:50Summary

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