Data Analysis & Visualization
pandas, NumPy, and the daily skill of turning real (messy) data into an answer — not ML theory
6 phases. 28 lessons. 28 labs. 1 capstone. The daily-practice depth this platform's other courses only touch in passing — hand-parsing painful data before pandas earns its keep, pandas mastery on pandas 3.0's current Copy-on-Write behavior, NumPy underneath it, matplotlib/seaborn visualization, and real exploratory data analysis. Built entirely on real, live-fetched datasets: actual Chipotle orders, NOAA weather stations, NYC taxi trips, and two real Inside Airbnb city snapshots.
- Lessons: —
- Labs: —
- Projects: —
- Level: Beginner
Curriculum
- Working with Data in Python — Hand-parsing real CSV data, hand-written aggregation bugs, meeting the DataFrame, fixed-width files, nested JSON.
- pandas Mastery — Filtering and Copy-on-Write, sorting, groupby split-apply-combine, merging, missing data, time series — on real Chipotle, NYC taxi, and Airbnb data.
- NumPy for Data — Fixed-type arrays, vectorized operations measured at a real 60x speedup, broadcasting, views vs. copies.
- Data Visualization — The object-oriented matplotlib pattern, combining seaborn with it, choosing a chart type from data shape, catching a misleading axis.
- Exploratory Data Analysis — EDA as question-generation, investigating real outliers individually, correlation vs. confounding variables, checking findings across data slices.
- Capstone — A real, never-seen 7,189-listing New Orleans Airbnb snapshot — explored, cleaned with documented reasoning, analyzed, and reported.
Skills You Will Learn
- pandas
- NumPy
- Data Cleaning
- Data Visualization
- Exploratory Data Analysis
- Statistical Reasoning
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