Recommendations for Aspiring Data Scientists. SQL Interviews @ the NYTimes. Your AI-Designed LBD. [DSR #179]
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This week's best data science articles
In this post I provide advice for junior data scientists as they onboard onto data and product teams at Airbnb. I break down effective execution into the following 4 categories: prioritization, estimating how long tasks will take, how to get your questions answered, and communicating & sharing your work.
Ohh this is very good. Data-adjacent skills are typically more important for success in early roles than technical skills, and this post gives excellent advice.
Saved you a click:
Get hands-on with cloud computing
Create a new data set
Glue things together
Stand up a service
Create a stunning visualization
Write a white paper
If you want to dig in further on any of these recommendations, click through to the full post.
The New York Times Data & Insights team works with departments across the company, and our analysts need a strong grasp of SQL. This is what to expect from the SQL portion of data analyst interviews.
This isn’t earth-shattering, but it’s neat to see that the NYTimes does a lot of the same stuff to hire great analysts that the rest of us do. I love that they’ve put in the work to create a anonymized, randomized data set to use as a part of the assessment: I think that creating this real-life data set is critical and often harder than expected.
Solid, short read.
Tangible steps you can take to identify and fix issues with training, generalization, and optimization for machine learning models.
Not fancy, but very useful. This is an under-discussed topic.
There isn’t a lot to read here, but the pictures are interesting. This is another “we built a GAN to design a thing and look what it came back with” project. We’ve seen this plot before, but it continues to come back with interesting results—the resulting dress has real flair.
There is a huge amount of scope to apply AI to the design of real-world objects. This story will play out over a very long time frame.
We’ve created OpenAI LP, a new “capped-profit” company that allows us to rapidly increase our investments in compute and talent while including checks and balances to actualize our mission.
This is a bit of inside baseball, but very long-term interesting for the field. OpenAI—to my knowledge the biggest independent research organization in AI today—just announced a change in its corporate structure that will enable it to attract more outside funding, while maintaining its mission as the prime directive of the combined entity. Its mission:
Our mission is to ensure that artificial general intelligence (AGI) benefits all of humanity, primarily by attempting to build safe AGI and share the benefits with the world.
Important development for an important organization. It’s quite hard to attract and retain AI talent without being able to provide equity-like returns.
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The internet's most useful data science articles. Curated with ❤️ by Tristan Handy.
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