Data Visualization in Python using Matplotlib and Jupyter

Data is only as powerful as your ability to communicate it. This workshop will show you how to use Python to turn raw numbers into visual stories that are easy to explore, understand, and share.

In this fully hands-on session, you’ll learn how to use Matplotlib and Jupyter Notebooks to create charts, graphs, and visuals that make your data come alive. We’ll start with the basics—line plots, bar charts, scatter plots—and gradually move to subplots, styling, and interactivity.

You’ll also learn how to combine visualizations with Markdown and code in Jupyter Notebooks to create polished reports or dashboards that can be shared with collaborators. No design skills required—just a willingness to experiment and a curiosity to see your data in a new light.

Agenda: Data Visualization with Python (6 hours)

• Introduction & Setup
  Why visualize data? The role of Matplotlib in the Python ecosystem
  Setting up Jupyter, installing Matplotlib with uv, and creating your first notebook

• Basic Plotting with Matplotlib
  Line plots, bar charts, pie charts, scatter plots
  Titles, labels, legends, gridlines — making charts readable
  Saving your plots to PNG or SVG

• Exploring Data Visually
  Loading a dataset with Pandas
  Plotting directly from a DataFrame
  Choosing the right chart for the data

• Short Break

• Layout & Styling
  Using subplots to compare data
  Adjusting figure sizes, colors, and styles
  Customizing ticks, axes, and labels for clarity

• Hands-On: Visualizing a Dataset
  Work with a sample dataset (e.g. climate data, sales reports, or sports stats)
  Participants will create a short report with multiple visualizations in one notebook

• Lunch Break

• Using Jupyter for Data Narratives
  Combining visuals, Markdown, and code into an interactive notebook
  Exporting to HTML or PDF
  Sharing notebooks via GitHub or Google Colab

• ChatGPT for Visual Exploration
  Learn how to use ChatGPT to generate plot code, troubleshoot layout issues, and ask “what chart should I use?”

Q&A + Wrap Up (30 minutes)

Prerequisites

• Familiarity with Python and Pandas basics
• Python installed and uv set up (instructions here)
• Laptop running Linux or macOS (Windows supported via browser-based JupyterLab)
• You must have permission to install software
• A personal Github account
  • Before the workshop, send me your ID so I can add you to the
    repo
    for access. This is necessary because we will be using gitter
    for the group chat during the workshop, and for sharing links.
  • Some familiarity to git is useful, but not required. If you’re
    interested here is a good
    intro to both Git and Github

Target Audience

• Beginners who want to visualize their data with Python
• Analysts, journalists, and researchers looking to create clean and informative charts
• Developers and hobbyists exploring Jupyter for presentations or data storytelling
• Anyone tired of doing all their plots in Excel

Slides

WIP

Exercises

WIP

About the Trainer

Vinay Keerthi is a self-taught developer who has spent the past decade building tools and systems across the software stack—from internal developer tools to large-scale data systems. He has worked at companies like Flipkart, Visa, and ChainSafe and has taught programming to developers, analysts, and tech-curious folks alike.

A firm believer in empowering beginners, Vinay uses ChatGPT and other modern tools to make learning programming more accessible and less intimidating.

Contact details

For inquiries and bulk bookings, contact Hasgeek on +91-7676332020 or email info@hasgeek.com