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The controversy around AI’s impact on tech careers has been polarizing—to place it very, mildly.
The utopians are pointing towards a future where data scientists and programmers can concentrate on management, strategy, and deep considering, as an alternative of on boring, repetitive tasks. The pessimists, meanwhile, are dreading a future by which there are not any more data scientists and programmers.
This week, we invite you to explore the space between these positions and the opportunities that arise amid uncertainty. The articles we’ve chosen suggest that we will harness AI’s power to turn into higher and simpler at our jobs—while foregrounding the qualities that make humans irreplaceable.
Turn into a Higher Data Scientist with These Prompt Engineering Suggestions and Tricks
“I see prompt engineering as a superpower,” says Sara Nobrega—one that allows smarter work and substantial time savings for junior and seasoned data professionals alike. In the primary a part of her latest series, Sara unpacks the advantages of prompt engineering in the course of the EDA (exploratory data evaluation) process.
Rethinking Data Science Interviews within the Age of AI
Yu Dong makes a compelling case for an AI-informed hiring process, and explains how candidates can use latest tools to showcase their skills.
Your Personal Analytics Toolbox
With assistance from the open-source MCP (model context protocol), Mariya Mansurova believes data scientists stand to make their work more streamlined—and more interesting.
This Week’s Must-Read Stories
Compensate for the articles our community has been buzzing about in recent days:
Other Really helpful Reads
Explore just a few more standout articles we published recently — they cover timely topics like bias in LLMs, scalable AI, and freelancing as a knowledge scientist:
Meet Our Latest Authors
Discover top-notch work from a few of our recently added contributors:
- Dave Flynn‘s first TDS article focuses on change-aware data validation.
- Jens Winkelmann joins our writer community with a multidisciplinary background in physics, data science, and AI.
- Ashton Gribble dedicates his debut story to the algorithm powering song-identification app Shazam.
We love publishing articles from latest authors, so if you happen to’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it with us?
