IBM IBSC 2026-04-08 | Module 9 Live Session | Capstone Part 1, GitHub, Data Collection & EDA

Live session for Module 9 of the IBM Data Science Professional Certificate, IBSC 2026-04-08 cohort. This week begins the final capstone project, where learners take on the role of a data scientist working for SpaceY, a fictional competitor to SpaceX. The goal of the capstone is to analyze SpaceX launch data and build toward a model that predicts whether a Falcon 9 first-stage booster will land successfully and be reusable. Module 9 focuses on the first half of the capstone workflow: understanding the business problem, collecting data, wrangling and cleaning it, organizing the project in GitHub, and beginning exploratory data analysis. These steps prepare us for Module 10, where we will move into machine learning classification, dashboarding, Folium maps, and the final slide deck submission. In this session, we cover: • Capstone project overview and business scenario • SpaceY vs SpaceX project framing • Why booster reuse matters for launch cost estimation • Capstone submission expectations • Required GitHub repository setup • How to organize notebooks, outputs, slides, and reports • Why the final slide deck should include conclusions, not just screenshots • Recommended README / executive summary structure • Examples of strong past capstone submissions • Uploading notebooks and project folders to GitHub • Module 9 and Module 10 deliverables • Working through the SpaceX API notebook • Current SpaceX API issues and how to document broken API behavior • Using cached/static data when the live API is unavailable • Web scraping SpaceX launch data from Wikipedia • Using requests, headers, BeautifulSoup, and Pandas • Extracting launch tables from HTML • Data wrangling, missing values, and basic data quality checks • Preparing the cleaned data for exploratory analysis • How Module 9 connects to the final machine learning and dashboard work in Module 10 The big idea: The capstone is not just about running notebooks. A strong data science project should clearly explain the business problem, document the data sources, organize the work in a public GitHub repository, and synthesize results into meaningful conclusions. By the end of Module 9, learners should have the foundation of their capstone project complete: data collected, cleaned, explored, documented, and ready for modeling and visualization in Module 10. For the final project, learners should complete all eight capstone notebooks across Modules 9 and 10, upload them to a public GitHub repository, and submit a final slide deck as a PDF through the IBM course platform. A short README or written executive summary is also highly recommended for portfolio completeness. All notebooks and live session materials for this bootcamp are available here: https://github.com/ABoothInTheWild/ib... #datascience #python #capstone #github #webscraping #beautifulsoup #pandas #spacex #machinelearning #eda #datacleaning #datawrangling #portfolio #ibm #bootcamp