Getting Started with Google Colab for Data Science and AI

Overview

Learn essential workflows for creating and documenting data science projects in Google Colab, covering best practices for notebook organization and documentation in AI applications.

Syllabus

• Introduction to Google Colab
Overview of the Google Colab environment
Setting up a Google account and accessing Colab
Navigating the user interface
• Basic Operations in Google Colab
Creating and managing notebooks
Sharing and collaborating on notebooks
Downloading and importing data
• Python and Libraries for Data Science
Introduction to Python in Colab
Installing and importing Python libraries
Overview of essential data science libraries: NumPy, Pandas, Matplotlib, Seaborn
• Data Manipulation and Analysis
Working with data frames in Pandas
Data cleaning and preprocessing
Exploratory data analysis techniques
• Visualization in Colab
Creating visualizations with Matplotlib and Seaborn
Customizing plots and graphics
Interactive visualizations with Plotly
• Machine Learning in Colab
Introduction to machine learning workflows
Using Scikit-Learn for basic machine learning models
Training and evaluating models
• Organizing and Documenting Notebooks
Best practices for notebook structure
Using Markdown for documentation
Adding comments and explanations to code
• Integrating with Google Cloud and External Services
Introduction to Google Cloud Storage integration
Using Google Drive with Colab for data storage
Accessing external APIs and services
• Advanced Features and Tips
Utilizing hardware accelerators (TPU/GPU)
Managing dependencies with pip and virtual environments
Debugging and troubleshooting common issues
• Final Project
Planning a data science project in Colab
Implementing project workflows
Documenting and presenting project findings

Subjects

Data Science