sql-spacex

SQL-powered analysis of SpaceX launch data using SQLite in a Python environment. Explore payloads, landing outcomes, and booster performance through SQL queries and pandas.

📊 Interactive dashboard built in Power BI showcasing payload mass, landing outcome, booster version comparison with Launch site.

🚀 SpaceX Launch SQL Analysis

This project demonstrates SQL-powered exploration of SpaceX launch data using a local SQLite database and Jupyter Notebook. It combines SQL querying with Python's pandas and SQLite to uncover insights into SpaceX’s missions, including launch sites, booster performance, payloads, and landing outcomes.

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📊 Overview

Attribute	Details
Dataset	SpaceX Launch Dataset (IBM Capstone)
Source	CSV Link
Tools	SQL (via ipython-sql), SQLite, pandas
Environment	Jupyter Notebook
Focus	SQL Queries & Data Analysis

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✅ Objectives

• Connect to a SQLite database and load launch data
• Clean and prepare the dataset using pandas
• Create SQL tables and run queries inside Jupyter using %sql
• Perform exploratory analysis using SQL:
  • Launch sites
  • Booster versions
  • Payload statistics
  • Landing outcomes
  • Mission trends over time

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🔍 Key Analysis Tasks

Task	Description
📌 Task 1	List all distinct launch sites
📌 Task 2	Filter launches starting with "CCA"
📌 Task 3	Total payload mass by NASA (CRS)
📌 Task 4	Average payload for F9 v1.1
📌 Task 5	First successful ground pad landing date
📌 Task 6	Boosters with 4000–6000kg payloads & drone ship landings
📌 Task 7	Count of missions by outcome
📌 Task 8	Boosters carrying the maximum payload
📌 Task 9	2015 landing outcome by month
📌 Task 10	Landing outcomes between specific dates

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🧠 Skills Demonstrated

• 🗃️ SQL query writing & filtering
• 📊 Aggregations, sorting, and conditional grouping
• 🧩 CASE WHEN logic for labeling and categorization
• 📆 Date parsing and filtering by time ranges
• 📓 Jupyter Notebook integration for SQL workflows using %load_ext sql and %sql magic
• 🧹 Basic data cleaning and preprocessing with pandas
• 🔗 SQL database setup with Python using sqlite3 and pandas .to_sql() method for seamless transition from CSV to SQL

👤 Author

Rahul Arya
🎓 B.Sc. Physics | 📜 IBM & Stanford ML Certified | 💡 Data Science Enthusiast