Learn the fundamentals of NumPy arrays, creation, and basic operations.
Tutorial series
Scientific Python
11 tutorials — follow in order for the best learning path.
- Getting Started with NumPy
- NumPy Array Operations
Master essential NumPy array operations: element-wise operations, broadcasting, aggregations, and array manipulation.
- Getting Started with pandas
Learn the fundamentals of pandas DataFrames, data loading, and basic data manipulation for analysis.
- Data Cleaning with pandas
Master essential data cleaning techniques in pandas including handling missing values, removing duplicates, string cleaning, and data type conversions.
- Building a Data Cleaning Pipeline
Chain pandas operations into a reusable data cleaning pipeline. Covers method chaining, pipe(), custom transformers, and production-ready ETL patterns.
- Plotting Data with Matplotlib
Learn how to create stunning visualizations in Python with Matplotlib, from basic line plots to custom styling.
- scikit-learn Intro: Your First ML Model
Build your first machine learning model with scikit-learn. Covers estimator pattern, fit/predict API, train-test splits, and a complete classification example.
- Scientific Computing with SciPy
Learn how to use SciPy for optimization, interpolation, integration, and statistics.
- pandas Intro: DataFrames and Series
Get started with pandas DataFrames and Series. Learn how to create, index, filter, and transform tabular data in Python with practical examples.
- Matplotlib Basics: Plots and Charts
Create line plots, scatter charts, bar graphs, and histograms with Matplotlib. Learn the pyplot interface, figure management, and basic customization.
- Data Visualization with Seaborn
Create statistical charts with Seaborn: scatter plots, bar charts, histograms, box plots, and heatmaps. Built on matplotlib with sensible defaults.