Data Science & Python

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• Pandas Data Wrangling 23
• Visualization 20
• Statistics Basics 21
• Machine Learning Basics 20
• Notebooks and Reproducibility 17

Prompts

• Frame a Problem for Machine Learning

  Decide whether ML is needed and what to predict.

  Create Data Science & Python / Machine Learning Basics

• Train-Test Split and Leakage Audit

  Make sure evaluation scores are honest.

  Create Data Science & Python / Machine Learning Basics

• Evaluation Metrics Chooser

  Pick metrics that match the real costs.

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• Baseline Model Plan Before Complexity

  Build the simplest model that could work.

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• Feature Engineering Brainstorm

  Create features from raw data thoughtfully.

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• Overfitting Diagnosis Walkthrough

  Recognize and fix a model that memorizes.

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• Explain a Model's Predictions to Stakeholders

  Communicate model behavior honestly.

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• Clustering Analysis Plan

  Group similar records and judge the result.

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• Model Documentation Card

  Document what a model is for and where it fails.

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• Time Series Forecasting Baselines

  Beat the naive forecast honestly.

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• Class Imbalance Handling Guide

  Deal with rare events without fooling yourself.

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• Recommendation or Ranking Prototype Plan

  Plan a simple recommender and its evaluation.

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• Cross-Validation Design for My Data

  Choose a validation scheme that matches reality.

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• Hyperparameter Tuning Plan

  Tune sensibly without overfitting the validation set.

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• Probability Calibration Check

  See whether predicted probabilities can be trusted.

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• Model Error Analysis Session

  Learn from the mistakes of a model.

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• Data Drift Monitoring Plan

  Detect when data or performance changes.

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• Text Classification Baseline

  Build a simple, honest baseline for text.

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• Model Fairness Check (Defensive Audit)

  Check a model for uneven performance across groups.

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• Model Deployment Readiness Checklist

  Check before a model goes live.

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• Notebook Cleanup and Structure Review

  Turn an exploratory notebook into a readable one.

  Create Data Science & Python / Notebooks and Reproducibility

• Reproducible Environment Setup

  Pin versions so results can be rerun later.

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• Project Folder Structure for Analysis Work

  Organize files so others can follow.

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• Code Review Checklist for Analysis Scripts

  Review data code for correctness, not just style.

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• Parameterized Reports from Notebooks

  Run the same analysis for many inputs.

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• Explain My Analysis to a Colleague in a Handover

  Document decisions, not just steps.

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• Logging and Checks Inside Data Scripts

  Make scripts tell you when something is wrong.

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• Python Skill Gap Assessment for Analysts

  Find what to learn next from real tasks.

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• SQL to Pandas Translation Guide

  Move queries between SQL and pandas correctly.

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• Experiment Tracking Log for Analysis Work

  Record what you tried and what happened.

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• Testing Data Code With Pytest

  Write tests for data transformations.

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• From Notebook to Package

  Move reusable code out of notebooks.

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• Data Versioning and Provenance Plan

  Know which data produced which result.

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• Configuration and Secrets in Analysis Projects

  Keep settings and credentials out of code.

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• Data Dictionary Template and Builder

  Document columns so others can use them.

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• Reproducibility Audit of an Analysis

  Check whether someone else can rerun your work.

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• Jupyter Habits That Prevent Hidden State Bugs

  Keep notebooks honest.

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• Messy CSV Cleaning Plan Before Any Code

  Inspect a messy file and plan the cleaning steps.

  Create Data Science & Python / Pandas Data Wrangling

• Pandas Merge and Join Troubleshooter

  Fix wrong row counts after merging tables.

  Create Data Science & Python / Pandas Data Wrangling

• Datetime and Time Zone Cleaning Guide

  Parse mixed timestamps without silent errors.

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• Groupby and Aggregation Recipe Builder

  Choose the right aggregation for a business question.

  Create Data Science & Python / Pandas Data Wrangling

• Missing Data Strategy Interview

  Decide how to handle nulls case by case.

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• Slow Pandas Code Performance Review

  Find and fix the slowest steps.

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• Reshaping Wide and Long Tables

  Pivot, melt, and stack without confusion.

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• Text Column Cleaning and Standardization

  Normalize names, categories, and free text.

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• Duplicate Detection and Record Linkage Plan

  Find duplicates safely without deleting good rows.

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• Data Quality Report From a DataFrame

  Produce a repeatable profile of a dataset.

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• Window Functions and Rolling Calculations

  Moving averages, ranks, and running totals.

  Create Data Science & Python / Pandas Data Wrangling

• Unit Tests for Data Transformations

  Test a cleaning function with small known cases.

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• Reading Many Files into One Clean Table

  Combine monthly exports with different layouts.

  Create Data Science & Python / Pandas Data Wrangling

• Explain This Pandas Code Line by Line

  Understand inherited code before changing it.

  Create Data Science & Python / Pandas Data Wrangling

• Choosing Data Types to Save Memory

  Right-size columns safely.

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• Pipeline from Raw Export to Analysis-Ready Table

  Design a reproducible cleaning pipeline.

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• Merge Validation With Indicator Columns

  Prove a join did what you intended.

  Create Data Science & Python / Pandas Data Wrangling

• Large CSV Processing in Chunks

  Handle files that do not fit in memory.

  Create Data Science & Python / Pandas Data Wrangling

• Nested JSON and API Data to Tidy Tables

  Flatten messy nested data reliably.

  Create Data Science & Python / Pandas Data Wrangling

• Time Series Resampling and Gap Handling

  Align irregular data to regular intervals.

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• Fuzzy Matching Names and Addresses

  Link records that are almost the same.

  Create Data Science & Python / Pandas Data Wrangling

• Cohort Table Construction in Pandas

  Build retention-style cohort tables.

  Create Data Science & Python / Pandas Data Wrangling

• Parsing Numbers, Currencies and Dates in Messy Columns

  Clean values stored as text.

  Create Data Science & Python / Pandas Data Wrangling

• Which Statistical Test Should I Use

  Pick a test from the data and question.

  Create Data Science & Python / Statistics Basics

• Confidence Intervals Explained With My Numbers

  Understand what an interval does and does not say.

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• A/B Test Plan and Sample Size

  Plan a test before collecting data.

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• Interpret My Regression Output

  Read coefficients, intervals, and diagnostics.

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• Probability Puzzles Tutor

  Learn probability through questions, not lectures.

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• Sampling and Bias Review

  Check whether a sample can answer the question.

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• Correlation versus Causation Checklist

  Test a causal claim before believing it.

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• Outlier Handling Decision Guide

  Decide to keep, fix, or remove extreme values.

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• Bootstrap and Simulation Walkthrough

  Estimate uncertainty with resampling.

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• Statistical Claims in the News: Fact Check

  Evaluate a headline that cites a study.

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• Descriptive Statistics Summary for Non-Technical Readers

  Turn a table of numbers into plain findings.

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• Survey Data Analysis Plan

  Plan the analysis before looking at results.

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• Bayesian Thinking for Everyday Decisions

  Update beliefs with evidence step by step.

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• Statistics Report Review Rubric

  Check an analysis write-up for errors and overreach.

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• Multiple Comparisons and False Positives Explainer

  Understand why many tests produce spurious wins.

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• Power Analysis Walkthrough

  Work out how much data you need.

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• Non-Parametric Test Chooser

  Decide when to avoid normality assumptions.

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• Chi-Square and Categorical Data Walkthrough

  Test relationships between categories.

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• Effect Sizes and Practical Significance

  Report how big a difference is, not just if it exists.

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• Regression Diagnostics Checklist

  Check a regression model's assumptions.

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• Explaining P-Values to Managers

  Communicate statistical results without jargon.

  Create Data Science & Python / Statistics Basics

• Choose the Right Chart for My Question

  Match chart type to the question, not the data.

  Create Data Science & Python / Visualization

• Chart Makeover: Before and After

  Fix a cluttered or misleading chart.

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• Accessible Color Palette Builder for Charts

  Colors that work for everyone.

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• Dashboard Layout Design Brief

  Plan a dashboard around decisions.

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• Exploratory Plot Sequence for a New Dataset

  See the data before modelling it.

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• Annotated Time Series Chart

  Show change over time with context.

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• Small Multiples for Comparing Groups

  Compare many groups without a tangle of lines.

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• Distribution Plots That Tell the Truth

  Histograms, box plots, and violins used correctly.

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• Chart Review Rubric

  Score a chart before it is published.

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• Maps and Geographic Charts Without Distortion

  Choropleths, symbol maps, and their traps.

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• Presenting Uncertainty in Charts

  Show error bars and intervals so people understand them.

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• Dashboard Metrics and Chart Specification Table

  Specify every tile so it can be built and tested.

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• Chart Alt Text and Data Table Alternatives

  Make charts usable without sight.

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• Data Visualization Critique Role-Play

  Practice defending a chart to a skeptical viewer.

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• Publication-Quality Matplotlib Figure

  Prepare a clean figure for print or a report.

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• Interactive Chart Specification

  Specify an interactive chart before building.

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• Waterfall and Bridge Chart for Changes

  Explain how a number moved from A to B.

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• Correlation Heatmap Done Right

  Avoid misleading heatmaps.

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• Slope and Dumbbell Charts for Before-After Comparisons

  Show change between two points clearly.

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• Dashboard Review for Decision Usefulness

  Test whether a dashboard changes decisions.

  Create Data Science & Python / Visualization

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