🎉 🥳🥂 Data Science Roundup Top 20 Posts of 2019 [DSR #211]

Well that’s a wrap! Another fun year curating the Data Science Roundup. As always, thanks for welcoming me into your inbox every weekend, it’s a real privilege and one I do not take lightly.

Looking back on 2019, the content being produced by the data community continues to be more mature and thoughtful than ever. Your favorite links of the year weren’t listicles and simplistic how-to content, they were thought-provoking articles by some of the smartest minds in the space. I’m glad that’s what you’re interested in, because it’s what I’m interested in (and seeking out!) as well.

Happy New Year and best wishes in 2020!

- Tristan

❤️ Want to support this project? Forward this email to three friends!a

🚀 Forwarded this from a friend? Sign up to the Data Science Roundup here.

This year's best data science articles

1. Miscellaneous unsolicited (and possibly biased) career advice

2. Why I’m Leaving Data

3. Data science is different now

4. 10 Reads for Data Scientists Getting Started with Business Models

5. We’re All Using Airflow Wrong and How to Fix It

6. Advice for New Data Scientists

7. Gartner Reveals Five Major Trends Shaping the Evolution of Analytics and Business Intelligence

8. How Much Do Data Scientists Make?

9. A wave of acquisitions in business intelligence

10. Resources for Analytics Engineers

11. How should I structure my data team? A look inside HubSpot, Away, M.M. LaFleur, and more

12. Becoming a Level 3.0 Data Scientist

13. Open Sourcing Amundsen: A Data Discovery And Metadata Platform

14. Does my startup data team need a data engineer?

15. Engineering Career Development at Etsy

16. The Data Tooling Market in 2019

17. We’re still in the steam-powered days of machine learning

18. Top 10 Coding Mistakes Made by Data Scientists

19. Modeling conversion rates and saving millions of dollars using Kaplan-Meier and gamma distributions

20. For Data Warehouse Performance, One Big Table or Star Schema?

Thanks to our sponsors!

dbt: Your Entire Analytics Engineering Workflow

Analytics engineering is the data transformation work that happens between loading data into your warehouse and analyzing it. dbt allows anyone comfortable with SQL to own that workflow.

getdbt.com • Share

Stitch: Simple, Powerful ETL Built for Developers

Developers shouldn’t have to write ETL scripts. Consolidate your data in minutes. No API maintenance, scripting, cron jobs, or JSON wrangling required.

www.stitchdata.com • Share

By Tristan Handy

The internet's most useful data science articles. Curated with ❤️ by Tristan Handy.

Tweet Share

If you don't want these updates anymore, please unsubscribe here.

If you were forwarded this newsletter and you like it, you can subscribe here.

Powered by Revue

915 Spring Garden St., Suite 500, Philadelphia, PA 19123