SQL Optimization, Data Science Portfolios, and Other July Must-Reads

Feeling inspired to write your first TDS post? We’re always open to contributions from new authors.

Our most-read and -discussed articles from the past month suggest that neither extreme summer weather nor global sporting events can derail our readers from upping their skills and expanding their knowledge of emerging topics.

Our monthly highlights cover data science career paths, cutting-edge LLM workflows, and always-relevant topics around SQL and Python. They’re brought to you with our authors’ signature blend of accessibility and expertise, so in case you missed any of them, we hope you enjoy our July must-reads. (If hot temperatures are affecting your attention span—we know the feeling!—you’ll be glad to know that all but two of the articles below are under-10-minute reads.)

Monthly Highlights

• Mastering SQL Optimization: From Functional to Efficient Queries
  Who could ever refuse major time savings when running your queries? Yu Dong’s practical guide to SQL optimization made a splash by offering six advanced tips that have helped her reduce query running time by 50 hours daily at her recent job; they are especially relevant for data professionals working in Snowflake SQL.
• Full Guide to Building a Professional Portfolio with Python, Markdown, Git, and GitHub Pages
  Clear, quick, and full of helpful code snippets, Pierre-Etienne Toulemonde’s debut TDS article became a hit by walking readers through the process of building a top-notch professional portfolio that meets two key criteria: a free solution, and minimal configuration.
• Running Local LLMs is More Useful and Easier Than You Think
  As the use of LLMs spreads wider and deeper into our daily workflows, so does the need to run these powerful models locally. Guillaume Weingertner shared a concise, step-by-step guide that demonstrated how this is no longer a complex, resource-intensive process.

• Evolution of Data Science: New Age Skills for the Modern End-to-End Data Scientist
  “What has dramatically changed, however, are business expectations, the technology landscape and the expanding range of skills a data scientist is expected to have.” Col Jung took us on a journey through the history of data science and outlined the types of skills practitioners need to master to stay competitive today.
• Leading by Doing: Lessons Learned as a Data Science Manager and Why I’m Opting for a Return to an Individual Contributor Role
  Successful data science careers don’t depend on specific titles or org-chart positions; as Dasha Herrmannova, Ph.D. argues in a thoughtful reflection on her own recent career moves, the most essential ingredient in success is understanding your own priorities and finding a role that fits them, not the other way around.
• Document Parsing Using Large Language Models — With Code
  We were thrilled to welcome back Zoumana Keita’s work this month—especially when the article in question was a patient, easy-to-follow tutorial on a promising front for LLM adoption: document parsing (in this case, PDF files of scientific research papers).
• Implementing Neural Networks in TensorFlow (and PyTorch)
  Rounding out our monthly highlights is Shreya Rao’s latest addition to her Deep Learning Illustrated series: a practical-implementation guide for anyone who’d like to gain hands-on experience with the theoretical concepts Shreya introduced in earlier articles. Follow along to learn how to build neural networks in TensorFlow (with a bonus PyTorch section, too!).

Our latest cohort of new authors

Every month, we’re thrilled to see a fresh group of authors join TDS, each sharing their own unique voice, knowledge, and experience with our community. If you’re looking for new writers to explore and follow, just browse the work of our latest additions, including Jason Zhong, Don Robert Stimpson, Nicholas DiSalvo, Rudra Sinha, Harys Dalvi, Blake Norrish, Nathan Bos, Ph.D., Ashish Abraham, Jignesh Patel, Shreya Shukla, Vinícius Hector, Fima Furman, Kaizad Wadia, Tomas Jancovic (It’s AI Thomas), Laurin Heilmeyer, Li Yin, Kunal Kambo Puri, Mourjo Sen, Rahul Vir, Meghan Heintz, Dron Mongia, Mahsa Ebrahimian, Pierre-Etienne Toulemonde, Shashank Sharma, Anders Ohrn, Alex Davis, Badr Alabsi, PhD, Jubayer Hossain Ahad, Adesh Nalpet Adimurthy, Mariusz Kujawski, Arieda Muço, Sachin Khandewal, Cai Parry-Jones, Martin Jurran, Alicja Dobrzeniecka, Anna Gordun Peiro, Robert Etter, Christabelle Santos, Sachin Hosmani, and Jiayan Yin.

Thank you for supporting the work of our authors! We love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, don’t hesitate to share it with us.

Until the next Variable,

TDS Team

SQL Optimization, Data Science Portfolios, and Other July Must-Reads was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.