Python Data Analysis with JupyterLab Training (PYT252)

This Python course provides a thorough introduction to essential data science tools, including JupyterLab, NumPy, and pandas, designed to help you manipulate, analyze, and visualize data effectively. Designed for Python programmers looking to strengthen their data science skills, this course offers a comprehensive exploration of these powerful Python libraries through hands-on exercises and practical examples.

The course begins with JupyterLab, where you'll learn to create a virtual environment and get started with Jupyter Notebooks. You will explore different notebook modes, work with Markdown for documentation, and use magic commands to optimize your workflow. The module includes exercises to help you become comfortable with JupyterLab's environment and features, such as getting help and experimenting with notebook functionality.

Next, you'll dive into NumPy, a fundamental package for scientific computing with Python. You will learn about the efficiency of NumPy arrays, their multi-dimensional capabilities, and how to perform element-wise operations. This module includes exercises on manipulating array elements, retrieving data from arrays, and using Boolean arrays. You'll also explore random number generation and gain a deeper understanding of how NumPy can handle large datasets efficiently.

The pandas module introduces you to one of the most popular data manipulation libraries in Python. You'll start with the basics of pandas and learn about Series, handling missing data with np.nan, and accessing elements in a Series. The course covers element-wise operations, the apply() method, and practical applications of Series. You'll then move on to DataFrames, learning how to create, explore, and manipulate them using various techniques. Exercises will guide you through retrieving and modifying data, pivoting DataFrames, and plotting data using matplotlib. You'll also learn to be cautious with properties and master advanced plotting techniques.

By the end of this course, you'll have a strong foundation in using JupyterLab, NumPy, and pandas for data analysis and visualization. You'll be well-equipped to handle data science tasks, manipulate large datasets, and present your findings effectively through compelling visualizations.

1. JupyterLab
   1. Exercise: Creating a Virtual Environment
   2. Exercise: Getting Started with JupyterLab
   3. Jupyter Notebook Modes
   4. Exercise: More Experimenting with Jupyter Notebooks
   5. Markdown
   6. Exercise: Playing with Markdown
   7. Magic Commands
   8. Exercise: Playing with Magic Commands
   9. Getting Help
2. NumPy
   1. Exercise: Demonstrating Efficiency of NumPy
   2. NumPy Arrays
   3. Exercise: Multiplying Array Elements
   4. Multi-dimensional Arrays
   5. Exercise: Retrieving Data from an Array
   6. More on Arrays
   7. Using Boolean Arrays to Get New Arrays
   8. Random Number Generation
   9. Exploring NumPy Further
3. pandas
   1. Getting Started with pandas
   2. Introduction to Series
   3. np.nan
   4. Accessing Elements in a Series
   5. Exercise: Retrieving Data from a Series
   6. Series Alignment
   7. Exercise: Using Boolean Series to Get New Series
   8. Comparing One Series with Another
   9. Element-wise Operations and the apply() Method
   10. Series: A More Practical Example
   11. Introduction to DataFrames
   12. Creating a DataFrame using Existing Series as Rows
   13. Creating a DataFrame using Existing Series as Columns
   14. Creating a DataFrame from a CSV
   15. Exploring a DataFrame
   16. Exercise: Practice Exploring a DataFrame
   17. Changing Values
   18. Getting Rows
   19. Combining Row and Column Selection
   20. Boolean Selection
   21. Pivoting DataFrames
   22. Be careful using properties!
   23. Exercise: Series and DataFrames
   24. Plotting with matplotlib
   25. Exercise: Plotting a DataFrame
   26. Other Kinds of Plots

Each student receives a comprehensive set of materials, including course notes and all class examples.

Experience in the following is required for this Python class: