Data Science Interviews

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:

• Statistics (counting and probability, hypothesis testing, distributions)
• Machine Learning (bias/variance, over/underfitting, regularization, bagging, boosting, gradient descent)
• Supervised Learning (linear and logistic regression, naive bayes, decision trees, random forests, SVM, KNN, neural networks)
• Clustering (k-means, DBSCAN, spectral, hierarchical, evaluation)
• Metrics (precision, recall, F1, sensitivity, specificity, ROC, AUC, log loss)
• Linear Algebra (linear independence, orthogonality, bases, eigenvectors, invertible matrix theorem, SVD)
• Feature Engineering (feature selection, PCA)
• Soft Skills / behavioral and experience questions

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.