Duration: 2 hours

Participants: 10-15 FabLab users (makers, hobbyists, engineers, and students)

Prerequisites: Basic knowledge of Python programming and an understanding of NumPy, Pandas, and Matplotlib.

Objective: This workshop aims to provide participants with a quick, hands-on experience in applying AI data skills to analyze and visualize data generated from FabLab equipment and projects.

Agenda:

Introduction and Setup (15 minutes)

• Brief overview of the workshop and objectives
• Introduction to FabLab environment and typical data sources (3D printers, CNC machines, IoT devices)
• Setting up the working environment (Jupyter notebooks, necessary libraries)

Session 1: Data Collection and Preparation (30 minutes)

• Introduction to data collection methods from various FabLab equipment
  • Collecting sample data (provided) from IoT sensors and 3D printers
• Using Pandas to load and inspect data
  • Reading data from CSV files
  • Basic data cleaning and preparation (handling missing values, filtering)

    import pandas as pd

    # Load sample data
    data = pd.read_csv('sample_fablab_data.csv')

    # Inspect data
    print(data.head())

    # Clean data (example: fill missing values)
    data.fillna(method='ffill', inplace=True)
    

Session 2: Data Analysis with Pandas and NumPy (30 minutes)

• Using NumPy and Pandas for basic data analysis
  • Calculating summary statistics
  • Analyzing time-series data
• Hands-on activity: Participants will analyze the provided data to extract meaningful insights

    import numpy as np

    # Summary statistics
    print(data.describe())

    # Analyzing time-series data
    time_series = data['usage_time']
    print(np.mean(time_series))
    

Break (10 minutes)

Session 3: Data Visualization with Matplotlib (30 minutes)

• Introduction to data visualization with Matplotlib
  • Creating simple plots (line plot, bar chart)
  • Customizing plots (labels, titles, legends)
• Hands-on activity: Participants will create visualizations for their analyzed data

    import matplotlib.pyplot as plt

    # Line plot
    plt.plot(data['timestamp'], data['usage_time'])
    plt.xlabel('Time')
    plt.ylabel('Usage Time')
    plt.title('Usage Time Over Time')
    plt.show()

    # Bar chart
    plt.bar(data['device'], data['usage_time'])
    plt.xlabel('Device')
    plt.ylabel('Usage Time')
    plt.title('Usage Time by Device')
    plt.show()
    

Wrap-Up and Q&A (15 minutes)

• Summary of key takeaways
• Q&A session
• Providing additional resources for further learning

Materials Needed:

• Computers with Python, NumPy, Pandas, and Matplotlib installed
• Sample datasets for demonstration (pre-collected data from IoT sensors and 3D printers)

Outcome:

Participants will gain a quick and practical understanding of how to collect, analyze, and visualize data within a FabLab environment. They will learn how to apply AI data skills to extract insights and improve their projects, fostering a data-driven approach to innovation.