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 does not contain specific problems, but instead the knowledge needed to solve them. Requires Anki 2.1+ for Mathjax equations. 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. 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. Material is sourced from around the web and from "Data Science Interviews Exposed," by You et. al. As you are studying the material, a great list of actual questions (and answers) on which to test your knowledge is http://rpubs.com/JDAHAN/172473. Covers:

• 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

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
• The very basics