Data Visualization with Google Colab

• Basic knowledge of Python programming
• Familiarity with basic data concepts

Audience

• Data scientists
• Data professionals

Data Visualization with Google Colab is a course designed to introduce participants to the fundamentals of data visualization using powerful Python libraries like Matplotlib and Seaborn within the Google Colab environment.

This instructor-led, live training (online or onsite) is aimed at beginner-level data scientists who wish to learn how to create meaningful and visually appealing data visualizations.

By the end of this training, participants will be able to:

• Set up and navigate Google Colab for data visualization.
• Create various types of plots using Matplotlib.
• Utilize Seaborn for advanced visualization techniques.
• Customize plots for better presentation and clarity.
• Interpret and present data effectively using visual tools.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Google Colab for Visualization

• Overview of Google Colab
• Setting up Google Colab
• Navigating the Google Colab interface

Getting Started with Data Visualization

• Importance of data visualization
• Introduction to Python visualization libraries

Basic Plotting with Matplotlib

• Creating simple plots
  • Line plots
  • Bar charts
  • Pie charts
• Customizing plots
  • Titles, labels, and legends
  • Colors, styles, and themes

Advanced Plotting with Matplotlib

• Subplots and multiple plots
• Working with annotations
• Saving and exporting plots

Introduction to Seaborn

• Overview of Seaborn
• Creating statistical plots
  • Distribution plots
  • Regression plots
  • Categorical plots

Customizing Seaborn Plots

• Aesthetics and themes
• Advanced customizations
• Combining Seaborn with Matplotlib

Handling and Visualizing Real-world Datasets

• Importing datasets
• Cleaning and preparing data
• Visualizing complex data

Collaborative Visualization Projects

• Sharing and collaborating on notebooks
• Real-time collaboration features
• Best practices for collaborative projects

Tips and Best Practices

• Effective data visualization techniques
• Avoiding common visualization pitfalls
• Enhancing visual appeal and clarity

Summary and Next Steps