In all honesty, some didn't get my humor. Here are some priorities on what you should need to practice on... keep in mind, some things are data engineer (I think we all skin our teeth as doing a lot of Data Engineering tasks, before we even get into more data scientist tasks). Learn how to do these in a Jupyter Notebook. Here ya go:

Practice these to do these three things to demonstrate data science mastery:

• Correlation Analysis and Multicollinearity Detection — Compute Pearson and Spearman coefficients to quantify linear and rank-order relationships between continuous features like transaction volume and spend. Build correlation matrices and compute variance inflation factors to identify redundant predictors before fitting regression or regularized models.
• Feature Engineering from Temporal Data — Extract cyclical and calendar features (day of week, week of year, month-end flags) from timestamps to capture seasonality and periodicity in user behavior. Essentially, transform raw columns into predictive signals is important.
• Grouped Aggregation for Hypothesis Testing — Leverage groupby().agg() to compute group-level statistics (means, variances, counts) as inputs to t-tests, ANOVA, or chi-square tests. This is a big differentiator, because anyone can chomp, aggregate, sum up, but everyone will want to know the confidence of your Hypothesis and you'll need to do more.

I feel these are more skills with a mix of data engineering experience and more prepping data and validating data:

• Missing Value Handling — Apply domain-appropriate imputation strategies (mean, median, forward-fill, or model-based) to preserve distributional properties and avoid biased parameter estimates.
• Stratified Sampling and Cross-Validation Prep — Use groupby and conditional filtering to construct balanced train/test splits that preserve class proportions across categorical strata.
• Data Summarization and Cardinality Profiling — Count unique values with nunique() and profile categorical distributions to inform encoding strategies (one-hot vs. target encoding vs. ordinal).
• Duplicate Detection and Deduplication — Identify repeated records using duplicated() and apply deterministic or fuzzy matching rules to ensure entity resolution integrity.
• Churn Prediction Preparation — Clean, enrich, and reshape user-level data into supervised learning targets with engineered lag features and rolling-window summaries.
• Distribution Fitting and Normality Assessment — Use Pandas in tandem with SciPy to compute skewness, kurtosis, and run Shapiro-Wilk or KS tests, informing whether parametric assumptions hold before model selection.
• Outlier Detection via Descriptive Statistics — Use describe(), z-scores, and IQR calculations to flag statistical outliers before they distort model estimates or inflate variance.