Data Science & Analytics with Python · Data Science & Analytics with Python

Data Science with Python

This track covers the core data science workflow in Python: data manipulation with NumPy and pandas, exploratory analysis, and visualization with Matplotlib and Seaborn. It also introduces reproducible notebooks and best practices for sharing results. Ideal for analysts, researchers, and developers seeking to turn raw data into actionable insights using Python.

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Lesson outline

  1. 25
    Build Bar & Pie Charts

    Learn how to build bar charts and pie charts in Python for data science. Step-by-step tutorial with hands-on exercises.

  2. 26
    Create Histograms & Box Plots

    Learn to create histograms and box plots in Python with this hands-on tutorial from the Data Science with Python track. Step-by-step instructions, troubleshooting tips, and comparisons to help you visualize data distributions effectively.

  3. 27
    Subplots & Figure Layout

    Master subplots and figure layout in Matplotlib with Python. Learn to create and arrange multiple plots, customize grids, and troubleshoot common issues.

  4. 28
    Add Labels, Legends, and Annotations

    In this lesson, you'll master adding labels, legends, and annotations to Python plots. Perfect for data scientists using Matplotlib and Seaborn, this tutorial covers step-by-step instructions, practical examples, and troubleshooting tips to make your visualizations clearer and more informative.

  5. 29
    Seaborn Pairplots Explained

    Explore data with Seaborn pairplots: learn how to create, read, and customize pairplots for quick exploratory analysis in Python.

  6. 30
    Visualize Distributions with Seaborn

    Visualize distributions with Seaborn — Data Science with Python. Learn how to plot histograms, KDE plots, and more to understand your data.

  7. 31
    Categorical Plots in Seaborn

    Learn to create categorical plots with Seaborn in this hands-on Data Science with Python tutorial. Master bar, box, violin, and swarm plots for clear comparisons and insights.

  8. 32
    Correlation Heatmaps in Python

    Learn to create correlation heatmaps with Python using pandas and seaborn to reveal relationships in your data.

  9. 33
    Save Publication-Ready Figures

    Learn to save and export publication-ready figures in Python with Matplotlib and Seaborn—set DPI, formats (PNG, PDF, SVG), and styling for journals and reports. Hands-on steps, troubleshooting, and next lessons included.

  10. 34
    Set Up Jupyter Notebook

    Set up Jupyter Notebook for analysis in this practical Data Science with Python lesson. Learn to launch, configure, and use notebooks for reproducible data work, with hands-on steps, troubleshooting, and next-steps guidance.

  11. 35
    Run Code Cells in Notebooks

    Learn to run code cells and markdown in Jupyter notebooks with this hands-on Python tutorial. Discover the core concepts, step-by-step execution, and troubleshooting tips to boost your data science workflow.

  12. 36
    Use Magic Commands

    Learn to use Jupyter magic commands for efficiency in data science, including line and cell magics, common shortcuts, and troubleshooting.

    Data Science with Python — step-by-step tutorials

    What you will find here

    This track walks through data science with python in order — each lesson is server-rendered HTML you can read without JavaScript. Follow the outline, then practice in the browser IDE when a lesson links to runnable code.

    Tutorials vs quizzes and code samples

    Tutorials teach in sequence. For quick checks use quizzes. For copy-paste snippets see code samples. For deeper reading browse articles.