Self Driven Data Science — Issue #32
Here’s this weeks lineup of data-driven articles, stories, and resources delivered faithfully to your inbox for you to consume. Enjoy!
Eric Mayefsky, head of data science at Quora, has assessed hundreds of job candidates in his half decade in management at various tech companies. His beliefs on data science interviews are defined by five lessons laid out in this piece.
We have a pretty good understanding of the application of machine learning and cryptography as a security concept, but when it comes to combining the two, things become a bit nebulous and we enter fairly untraveled wilderness. This article introduces Homomorphic and Fully Homomorphic Encryption and discuss its impact on model encryption and training.
When you set out to solve a data science problem, it is very tempting to dive in and start building models. This article proposes that you create a common-sense baseline first. A common-sense baseline is how you would solve the problem if you didn’t know any data science.
It is much reliable to use various different models rather than just one. A collection of several models working together on a single set is called an Ensemble. This article serves as an effective introduction to the popular method.
When building machine learning models, we want to keep error as low as possible. Two major sources of error are bias and variance. If we managed to reduce these two, then we could build more accurate models. But how do we diagnose bias and variance in the first place? And what actions should we take once we’ve detected something?
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Thanks for reading and have a great day!