Giter VIP home page Giter VIP logo

nasa-planetary-budget-analysis-with-sql's Introduction

Exploring NASA's Planetary Exploration Budgets Through SQL Analysis.

Executive Summary:

An analysis of the historical budget allocations for space missions, adjusted for inflation, categorized by destination and fiscal year using SQL. The analysis aims to uncover trends in funding across different space exploration destinations over time, providing valuable insights into NASA's budgetary priorities and strategic focus.

Public Dataset:

https://www.planetary.org/space-policy/planetary-exploration-budget-dataset

Methodology:

The analysis involved the following steps:

  1. Data Retrieval: Data was sourced from the mission_budgets, inflation, and mission_details tables within the NASA database.
  2. Data Joining: We joined the tables based on common keys to provide a comprehensive view of mission costs, adjusted for inflation.
  3. Data Grouping: We grouped the data by fiscal year and destination to calculate the total adjusted cost for each category.
  4. Data Analysis: We ordered the results and visualized to facilitate trend analysis and interpretation. The bar plots were generated using Python’s “Matplotlib” library.

Results:

The analysis yielded comprehensive insights into NASA's planetary exploration budgets, revealing trends in funding allocation over time. Key findings include:

  1. Total Cost: The total adjusted cost of all planetary missions over time amounted to $41,406 million USD.
  2. Inflation Adjustment: After adjusting for inflation, the total adjusted cost was calculated to be $41,447 million USD.
  3. Most Expensive Mission: The Europa Clipper emerged as NASA's most expensive mission, with an adjusted total cost of $5,283.81 million USD.
  4. Annual Spending: Yearly expenditure showcased fluctuations, reflecting changes in mission priorities and external factors.
  5. Expenditure by Destination: Analysis of spending by destination highlighted NASA's diverse exploration efforts, with significant investments in the Moon, Mars, and outer planets.
  6. Changes Over Time: Trends over time revealed shifts in NASA's exploration focus, from a lunar-centric approach to a more diversified portfolio encompassing various celestial bodies.

Overall Interpretations:

Interpretations of the findings revealed:

  1. Lunar Dominance: Significant funding during the Apollo era highlights the importance of lunar exploration.
  2. Mars Exploration: Consistent investment in Mars missions reflects sustained interest and exploration efforts on the red planet.
  3. Outer Planets Focus: Intermittent peaks in investment indicate flagship missions to outer planets like Jupiter and Saturn.
  4. Venus Exploration: Lesser funding for Venus missions suggests a lower priority compared to Mars and outer planets.
  5. Trend Shifts: Evolving trends highlight NASA's dynamic approach to planetary exploration, adapting to technological advancements and scientific discoveries.

Conclusion

The report reveals a clear pattern of space exploration funding, with a shift from a lunar-centric approach in the early years to a broader focus on various solar system bodies. The sustained interest in Mars and periodic investments in outer planet missions reflect strategic priorities in space exploration. The data underscores the importance of adjusting for inflation to accurately assess historical budget trends.

nasa-planetary-budget-analysis-with-sql's People

Contributors

nawailkhan avatar

Stargazers

 avatar  avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.