https://hub.imt-atlantique.fr/datascience-toolkit/session1/index.html Presentation & objectives By this case study, you will be able to: Set up Python environment and VS Code, using environment management tools like virtualenv or conda Load and explore a dataset using Python basics and Pandas, including basic DataFrame operations Implement error handling when loading and exploring a dataset Perform simple aggregations on a dataset Create basic visualizations with Matplotlib Implement simple linear regression for prediction Use Git for version control Very useful documentations can be found here: Hugo en-us https://hub.imt-atlantique.fr/datascience-toolkit/session1/practical_1/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/session1/practical_1/index.html Duration1h15 AI Banned Sales of a high-tech and multimedia shop Context We are in the context of a high-tech and multimedia shop located in the US with stores in several cities. The manager of this shop would like to prepare the promotional period and to well target advertisements for the products. He is interested in boosting sales by targeting the best moments to launch promotions. He contacted you as a data engineer to help him achieve this objective. https://hub.imt-atlantique.fr/datascience-toolkit/session1/practical_2/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/session1/practical_2/index.html 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. https://hub.imt-atlantique.fr/datascience-toolkit/session1/practical_3/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/session1/practical_3/index.html Duration2h30 AI Banned This guide will walk you through setting up a structured data science project for sales analysis, using Git for version control. We’ll focus on implementing the concepts introduced in our basic Git workflow guide. Prerequisites Important: Before starting this tutorial, make sure you’ve completed the Environment Setup and Git & GitLab How-to guides, which cover: GitLab account creation Repository initialization SSH key setup Git configuration Repository cloning This tutorial assumes you have already created and cloned the “sales-analysis” repository as instructed in the previous guide.