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Data Science Interviews

1.48MB. 0 audio & 27 images. Updated 2020-09-29.

Description

This deck helps you study the material behind common data science interview questions. It focuses on data science theory, mostly statistics and machine learning, rather than practice (it contains no code). It contains general knowledge needed for problem solving, rather than specific problems. Updated 9/20: Revised to make notes simpler, more concise, and more accurate. Many more pictures. Cards now have a reference link for easy access to source material. Split large cards into several parts. Included: Questions are grouped into sub-decks by topic, such as stats, supervised learning, and clustering. You can study just the sub-topics you want to learn and skip those you already know. Questions are tagged as high-level (broad, important, conceptual knowledge), medium-level, and low-level (less common, details, equations). A good study strategy would be to start with all high-level questions, then move on to medium and low. Material is sourced from around the web and from Data Science Interviews Exposed by You et. al. As you are studying the material, one great list of actual questions (and answers) on which to test your knowledge is Data Science Interview Questions & Detailed Answers. Does not cover: Data wrangling; Programming, engineering; Databases, SQL; Natural Language Processing; Deep Learning; Recommender Systems; Bayesian Methods; Time Series Analysis; Anomaly Detection; Visualization; Calculus; or the very basics. Requires Anki 2.1+ for Mathjax equations.

Sample (from 117 notes)

Cards are customizable! When this deck is imported into the desktop program, cards will appear as the deck author has made them. If you'd like to customize what appears on the front and back of a card, you can do so by clicking the Edit button, and then clicking the Cards button.
Front Explain bagging
Back Bootstrap aggregatingTrain multiple models on subsamples and average predictions to reduce variance.Usually uses "strong," low-bias models.
Ref https://en.wikipedia.org/wiki/Bootstrap_aggregating
Credit https://en.wikipedia.org/wiki/Bootstrap_aggregating
Tags high-level
Front How does an artificial neuron (perceptron) work?
Back Applies an activation function to the weighted sum of its inputs.\[ y = f \left( \textstyle \sum w_i x_i \right) \]Common activation functions are linear, step, sigmoid, tangent, rectified linear...
Ref https://en.wikipedia.org/wiki/Perceptron
Credit http://www.theprojectspot.com/tutorial-post/introduction-to-artificial-neural-networks-part-1/7
Tags high-level
Front How do decision trees work, high-level?
Back Recursively split the data into groups based on most discriminating feature; each leaf gives a prediction.
Ref https://victorzhou.com/blog/intro-to-random-forests/
Credit https://victorzhou.com/blog/intro-to-random-forests/
Tags high-level

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Reviews

on 1600225780
Nice deck. Covers main concepts!
on 1586692853
Using it a lot! Thanks
on 1584546694
Good!
on 1581520587
Thank you
on 1562757741
Awesome
on 1550469199
Exactly what I was looking for!
on 1547303850
Thank you! Good job!
on 1535528578
Thanks a lot!
on 1524787200
quite a comprehensive deck, with some subtle questions