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Python for Data Science

Learn Python from scratch with a focus on data work: core syntax, functions, control flow, then the scientific stack — NumPy arrays, pandas DataFrames, and visualization with Matplotlib and Seaborn. Every lesson runs right in your browser.

Start course2 modules · 10 lessons

01Python Fundamentals

Notebooks, variables, data types, functions, conditionals, and loops — the core language you'll use everywhere.

  1. Getting Started with Python & NotebooksMeet Python and the notebook workflow — run your first code, write comments, and use Python as a powerful calculator.
  2. Variables & Data TypesMaster Python's core data types — numbers, strings, booleans, lists, tuples, dictionaries, and sets — the building blocks of every dataset you'll ever touch.
  3. FunctionsPackage logic into reusable functions — parameters, return values, defaults, scope, docstrings, and a first look at lambdas.
  4. Conditionals & BooleansTeach your code to make decisions — comparison operators, and/or/not, if/elif/else chains, truthiness, and the pitfalls that bite beginners.
  5. Loops & IterationRepeat work over entire datasets with for and while loops, enumerate and zip, list comprehensions, and the accumulator pattern.

02Python for Data

The scientific Python stack: fast arrays with NumPy, tabular data with pandas, and plotting with Matplotlib and Seaborn.

  1. NumPy: Fast ArraysMeet the array — NumPy's fast, vectorized alternative to Python lists — and learn creation, indexing, broadcasting, aggregation, and random numbers.
  2. pandas: DataFrames & SeriesLearn pandas' two core structures — Series and DataFrame — and the first-look toolkit every analysis starts with, from head() and info() to derived columns and value_counts().
  3. Filtering, Sorting & GroupingAsk real questions of your data — boolean masks, combined conditions, isin and between, loc vs iloc, sort_values, groupby aggregation, and pivot tables.
  4. Matplotlib: Your First ChartsDraw line, scatter, bar, and histogram charts with Matplotlib, style them with colors and markers, and compose multi-panel figures with plt.subplots.
  5. Seaborn: Statistical VisualizationGet publication-quality statistical charts in one line — scatterplots with hue, boxplots, violin plots, correlation heatmaps, and pairplots with seaborn.