Never miss a new edition of The Variable, our weekly newsletter featuring a top-notch selection of editors’ picks, deep dives, community news, and more.
Are you planning to switch roles in the near future? On the lookout for your first data science or machine learning position? Regardless of your career stage, change and growth are likely on your mind — and we’re here to help.
At the core of this week’s Variable are articles that center the skills and knowledge base you should master to set yourself up for success. Grounded in the authors’ personal experiences, you’ll find hands-on advice and sharp insights that you can apply across a wide range of disciplines and settings.
I Transitioned from Data Science to AI Engineering: Here’s Everything You Need to Know
Part practical guide, part personal reflection, Sara Nobrega presents a compelling account of the skills, tools, and strategies that powered her successful switch to the competitive field of AI engineering.
Landing your First Machine Learning Job: Startup vs Big Tech vs Academia
No more cookie-cutter resumés and cover letters: Piero Paialunga stresses that you should tailor you job-search approach to the type of role and work environment you’re looking for.
5 Statistical Concepts You Need to Know Before Your Next Data Science Interview
For Haden Pelletier, nailing your job interview isn’t really about knowledge; it’s about the ability to explain what you know and how to apply abstract concepts in real-world situations.
This Week’s Must-Read Stories
Catch up on the articles our community has been buzzing about in recent days. Here’s a roundup of this week’s trending headlines:
The Best AI Books & Courses for Getting a Job, by Egor Howell
Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python, by Sarah Schürch
JAX: Is This Google’s NumPy killer?, by Thomas Reid
Other Recommended Reads
Explore some of our top-notch recent articles on other topics, including how to tackle LLMs’ security risks, synthetic-data generation, and more.
- How to Generate Synthetic Data: A Comprehensive Guide Using Bayesian Sampling and Univariate Distributions, by Erdogan Taskesen
- Evaluating LLMs for Inference, or Lessons from Teaching for Machine Learning, by Stephanie Kirmer
- The Secret Power of Data Science in Customer Support, by Yu Dong
Meet Our New Authors
Every week, we’re thrilled to welcome a fresh cohort of data science, machine learning, and AI experts. Don’t miss the work of some of our newest contributors:
- Mahe Jabeen Abdul devotes her debut TDS article to the challenges of landing on the right data-monitoring strategy.
- Toluwase Babalola presents a patient tutorial on implementing AI-powered webpage detection applications into production.
- Julian Mendel unpacks the promise of evolutionary coding agents, based on recent work out of Google DeepMind.
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, why not share it with us?
