Introduction to Jupyter Notebooks and Basic Data Analysis¶
Learning Objectives¶
By the end of this lesson, students should be able to:
- Understand what Jupyter Notebooks are and how to use them effectively.
- Load and inspect data from CSV and JSON files in a Jupyter Notebook.
- Perform basic data analysis using Python libraries such as pandas and matplotlib.
- 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.