Description

Description

Python Data Analysis with JupyterLab is a hands-on, practical course that introduces learners to the fundamentals of data analysis using Python, JupyterLab, NumPy, pandas, and matplotlib. Designed for those with basic Python experience, this course empowers you to manipulate, analyze, and visualize data in an interactive notebook environment widely used in both industry and academia.

Starting with the basics of JupyterLab, you’ll learn how to write rich, readable notebooks using Markdown and Magic Commands. From there, the course progresses through numerical computing with NumPy, data manipulation with pandas, and finally, data visualization with matplotlib—all through real-world examples and guided exercises.

Training Objectives

• Set up and navigate JupyterLab for interactive data analysis
• Use Markdown and Magic Commands for creating readable well-documented notebooks
• Efficiently manipulate arrays and perform computations using NumPy
• Clean filter and transform structured data with pandas
• Explore and visualize datasets using matplotlib
• Work with Series and DataFrames including slicing grouping and aggregating data
• Build meaningful data-driven Python projects in a collaborative environment

Course Outline

• 1. Getting Started with JupyterLab<br />
• Creating a virtual environment (Exercise)<
• Launching and navigating JupyterLab (Exercise)<
• Notebook modes and cell types<
• Experimenting with notebooks (Exercise)<
• 2. Enhancing Notebooks with Markdown and Magic<br />
• Using Markdown for clean documentation (Exercise)<
• Magic commands and how to use them effectively (Exercise)<
• Getting help and working efficiently in notebooks<
• 3. Numerical Computing with NumPy<br />
• Why NumPy? Demonstrating its efficiency (Exercise)<
• Creating and manipulating arrays<
• Multi-dimensional arrays and slicing (Exercise)<
• Boolean masking and random number generation<
• Exploring more NumPy features<
• 4. Data Analysis with pandas<br />
• Getting started with Series and DataFrames<
• Handling missing values (np.nan)<
• Accessing and modifying Series and DataFrame elements (Exercise)<
• Boolean indexing, alignment, and comparison<
• Applying functions and element-wise operations<
• Creating DataFrames from various sources (CSV, Series)<
• Exploring and transforming data (Exercise)<
• Pivoting, filtering, and property handling<
• 5. Data Visualization with matplotlib<br />
• Plotting Series and DataFrames (Exercise)<
• Customizing plots<
• Exploring different plot types