NumPy: Understand the fundamentals of NumPy, including array creation, manipulation, and basic operations. Learn to perform complex mathematical computations efficiently using NumPy arrays.
Pandas: Gain expertise in Pandas for data manipulation and analysis. Explore data structures like Series and DataFrames, and master techniques for cleaning, transforming, and analyzing data.
Matplotlib: Discover how to visualize data effectively using Matplotlib. Create a variety of plots, customize visualizations, and learn to present data insights clearly and compellingly.

This module is structured to provide a comprehensive understanding of Data Tools in AI. It includes several key components to facilitate learning and practical application:

• Initial Questionnaire: This section aims to gauge the user's existing knowledge of data tools in AI. It helps in tailoring the learning experience according to the user's proficiency level.
• Introduction to Data Tools: A brief presentation that explains what Data Tools are, their purpose, and the most commonly used tools in the field. This section sets the stage for the more detailed explorations to come.
• Three Main Sections: The core of the module is divided into three sections, each focusing on a specific tool: Numpy, Pandas and Matplotlib.
• Practical Exercises: After learning about each tool, this section provides exercises to test and reinforce the knowledge acquired throughout the module. Students can apply what they have learned and ensure they understand the material thoroughly.
• Workshops Design: This part of the module presents designs for several workshops that can be implemented in a FabLab environment. These workshops utilize the tools and hardware commonly found in FabLabs, providing a practical context for the skills learned.
• Final Knowledge Test: The module concludes with a test similar to the initial questionnaire. This final assessment allows students to answer questions related to the course content and evaluate their learning progress.

We hope this structured approach helps you effectively learn and apply data tools in AI, particularly in the innovative environment of a FabLab. Happy learning!