Space Missions Data Analysis

Project Overview

This project analyzes historical space mission launch data to explore how humanity has progressed in space exploration over decades. The analysis focuses on mission outcomes, launch trends, company performance, and cost efficiency using real-world data that includes missing values and inconsistent formats. The goal of this project is to demonstrate end-to-end data analysis skills, from raw data cleaning to advanced analysis and visualization.

Dataset Information

The dataset contains records of global space missions with the following attributes: Launch company Launch location and country Launch date and time Rocket and rocket status Mission status (Success, Failure, Partial Failure, Prelaunch Failure) Launch cost (with missing values)

Data Cleaning & Preparation

Data cleaning was performed using Excel and Power Query, including: Handling missing and blank launch cost values Standardizing mission status categories Cleaning and restructuring launch location fields Preparing the dataset for SQL analysis and Power BI visualization

SQL Analysis

A structured set of SQL queries was created to answer analytical questions such as: Overall mission success rate Company and rocket performance comparison Mission outcomes before vs after the year 2000 Time-based launch trends Cost efficiency analysis Identification of the most reliable rockets The SQL section demonstrates aggregation, filtering, CASE logic, time-based analysis, and ranking.

Power BI Dashboard

An interactive Power BI dashboard was built with a space-themed dark design, including: Mission success and failure analysis Launch trends over time Top companies and rockets performance Global launch locations map KPIs and slicers for dynamic exploration Tools & Technologies Excel Power Query SQL Power BI

Key Insights

Mission success rates improved significantly after the year 2000 Certain companies and rockets consistently outperform others Launch activity increased dramatically in recent decades Missing cost data represents a real-world data challenge

Author

Hany Attia Data Analyst