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.