2. Basic data manipulation

Duration2h30 x 3 AI Banned

Introduction

This practical session introduces fundamental concepts of data manipulation and visualization using Python through a real-world sales analysis case study. You’ll work with authentic sales transaction data to develop essential data science skills using Pandas, NumPy, Matplotlib, and Seaborn.

Through hands-on exercises, you will learn to:

  • Data Loading & Exploration: Import CSV data and explore dataset structure, dimensions, and data types
  • Data Cleaning & Preprocessing: Handle missing values, duplicates, and convert data types with error handling
  • Feature Engineering: Extract temporal components, parse geographic information, and create product categories using regular expressions
  • Statistical Analysis: Perform data aggregation, grouping operations, and calculate descriptive statistics
  • Data Visualization: Create professional charts following visualization best practices, including bar plots, correlation heatmaps, and box plots

This session uses a real sales dataset containing transaction information (Order ID, Product, Quantity, Price, Date, Address) and emphasizes practical application of data science techniques to transform raw data into actionable business insights.

Getting Started

You have access to a JupyterHub environment that already contains all the necessary data, notebooks, and required Python libraries. Simply access the environment and start working immediately!

Access JupyterHub

Local Installation

If you prefer to work locally, please refer to the activity Setup your first Data Science project first to ensure you have all the necessary Python packages installed.

You can download the notebook for this practical session using the link below:

Download Notebook (Right-click → Save as…)