achuthasubhash/Complete-Life-Cycle-of-a-Data-Science-Project
A Link-Heavy Roadmap Through the Data Science Lifecycle
A curated link index built to help aspiring data scientists navigate the project lifecycle by mapping hundreds of external tools and datasets to each stage from business understanding onward.
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What it does
This repository is a curated reference index for the data science lifecycle, beginning with business understanding and drilling deep into data collection. The README acts as a massive bookmark file, linking out to hundreds of external tools, articles, and dataset repositories rather than shipping original code. It categorizes resources by task—web scraping libraries, SQL and NoSQL databases, public dataset portals, and data quality tools—intended as a roadmap for newcomers.
The interesting bit
The author treats the README as a living textbook, attempting to catalog virtually every mainstream Python scraping tool and cloud database in one place. It is admirably comprehensive, though the format is closer to a personal wiki than a polished guide.
Key highlights
- Attempts to cover the full pipeline arc, starting from business understanding and drilling deep into data collection strategies.
- Data collection section is exhaustively subdivided: structured vs. unstructured data, web scraping (
BeautifulSoup,Scrapy,Selenium,AutoScraper), social-media scrapers (Twint,snscrape,Instaloader), and third-party APIs. - Maintains an extensive list of public dataset sources, from Google Dataset Search and Papers With Code to government census portals.
- Includes data quality and ETL references (
cleanlab,ydata-quality,awesome-etl) rather than stopping at raw acquisition.
Caveats
- The README is a dense, minimally structured link collection; finding a specific tool requires patience and generous use of Ctrl+F.
- Contains no original code, notebooks, or executable examples—this is purely a curated reading list, not a framework.
- Formatting is uneven, with nested lists, bare URLs, and abrupt section breaks that make scanning difficult.
Verdict
Worth a bookmark if you’re a student or career switcher building your first mental model of the data science toolchain. Experienced practitioners who already know their way around Scrapy and PyMongo will find it redundant.
Frequently asked
- What is achuthasubhash/Complete-Life-Cycle-of-a-Data-Science-Project?
- A curated link index built to help aspiring data scientists navigate the project lifecycle by mapping hundreds of external tools and datasets to each stage from business understanding onward.
- Is Complete-Life-Cycle-of-a-Data-Science-Project open source?
- Yes — achuthasubhash/Complete-Life-Cycle-of-a-Data-Science-Project is open source, released under the MIT license.
- How popular is Complete-Life-Cycle-of-a-Data-Science-Project?
- achuthasubhash/Complete-Life-Cycle-of-a-Data-Science-Project has 647 stars on GitHub.
- Where can I find Complete-Life-Cycle-of-a-Data-Science-Project?
- achuthasubhash/Complete-Life-Cycle-of-a-Data-Science-Project is on GitHub at https://github.com/achuthasubhash/Complete-Life-Cycle-of-a-Data-Science-Project.