Winning Space Race with Data Science

This is the presentation of the capstone project in the IBM Data Science Professional Certificate.

Note that this presentation is much more detailed and technical than regular high-level and abstracted presentations for executive teams.

I assume the role of a Data Scientist working for a startup intending to compete with SpaceX, and in the process follow the Data Science methodology involving data collection, data wrangling, exploratory data analysis, data visualization, model development, model evaluation, and reporting results to stakeholders.

In this capstone, we will predict if the Falcon 9 first stage will land successfully, SpaceX advertises Falcon 9 rocket launches on its website, with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because SpaceX can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against SpaceX for a rocket launch.

Executive Summary

Introduction

Methodology

Data collection API notebook

Web scraping notebook

Data wrangling notebook

EDA with Visualization notebook

EDA with SQL notebook

Launch Sites Locations Analysis with Folium notebook

Interactive Dashboard with Ploty Dash

Machine Learning Prediction notebook

Insights Drawn from EDA

Launch Sites Proximities Analysis

Build a Dashboard with Plotly Dash

Predictive Analysis (Classification)

Conclusions

Appendix

For notebooks, datasets and scripts, follow this GitHub repository link: Applied Data Science Capstone