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Introduction to Jupyter Notebooks and Basic Data Analysis

Learning Objectives

By the end of this lesson, students should be able to:

  1. Understand what Jupyter Notebooks are and how to use them effectively.
  2. Load and inspect data from CSV and JSON files in a Jupyter Notebook.
  3. Perform basic data analysis using Python libraries such as pandas and matplotlib.
  4. Apply their knowledge to analyze data from Donkey Car projects.

Required Materials and Preparation

  • Access to a computer with Python, Jupyter Notebooks, pandas, and matplotlib installed.
  • Sample data sets from Donkey Car projects in both CSV and JSON formats.
  • Previous knowledge in Python programming.

Lesson Breakdown

Lesson 1: Introduction to Jupyter Notebooks (2 hours)

1.1 Lecture: What is a Jupyter Notebook? (30 mins)

  • Definition and purpose
  • Features and benefits of using Jupyter Notebooks

1.2 Hands-on Activity: Getting Started with Jupyter Notebook (90 mins)

  • Launching a Jupyter Notebook
  • Familiarizing with the interface
  • Creating, editing, and executing cells
  • Markdown syntax and use
  • Saving and sharing Jupyter Notebooks

Lesson 2: Data Loading and Inspection in Jupyter Notebooks (2 hours)

2.1 Lecture: Basics of pandas (30 mins)

  • Overview of pandas
  • Creating dataframes
  • Basic dataframe operations

2.2 Hands-on Activity: Loading and Inspecting Data (90 mins)

  • Reading data from CSV and JSON files with pandas
  • Inspecting data: checking the dimensions, viewing the first/last few rows, data types
  • Data summary statistics: using describe()

Lesson 3: Basic Data Analysis in Jupyter Notebooks (3 hours)

3.1 Lecture: Data Analysis with pandas (30 mins)

  • Filtering and selecting data
  • Grouping and aggregation
  • Basic plotting with pandas

3.2 Hands-on Activity: Basic Data Analysis (150 mins)

  • Practical exercises for data selection, filtering, and aggregation
  • Creating basic plots to visualize data insights
  • Exploring the data to answer exploratory questions

Lesson 4: Data Analysis of Donkey Car Project Data (3 hours)

4.1 Recap: Overview of the Donkey Car project (30 mins)

  • Overview of the Donkey Car project and the associated datasets

4.2 Hands-on Activity: Donkey Car Data Analysis (150 mins)

  • Loading and inspecting Donkey Car project datasets
  • Performing exploratory data analysis: answering specific questions, making plots, extracting insights
  • Discussion: Sharing insights, potential improvements for the Donkey Car project based on the data

Evaluation

Students' understanding will be evaluated through their participation in the hands-on activities and the insights they generate from the Donkey Car project's data analysis. An end-of-unit quiz will also be provided to assess their theoretical understanding and practical skills in Jupyter Notebooks and data analysis.

Extension Activities

  • Advanced Data Analysis: Introduce students to more advanced data analysis techniques such as correlation analysis, data normalization, and pivot tables.
  • Data Visualization: Teach students about more complex visualizations using libraries such as seaborn or plotly.
  • Machine Learning Introduction: Provide a brief overview of how the data they have analyzed could be used to train a machine learning model.