b i Cracking the Data Science Interview i b is the first book that attempts to capture the essence of data science in a concise, compact, and clean manner. In a i Cracking the Coding Interviewi style, b i Cracking the Data Science Interview i b first introduces the relevant concepts, then presents a series of interview questions to help you solidify your understanding and prepare you for your next interview. pTopics include br - Necessary Prerequisites statistics, probability, linear algebra, and computer sciencebr - 18 Big Ideas in Data Science such as Occam's Razor, Overfitting, BiasVariance Tradeoff, Cloud Computing, and Curse of Dimensionalitybr - Data Wrangling exploratory data analysis, feature engineering, data cleaning and visualizationbr - Machine Learning Models such as iki-NN, random forests, boosting, neural networks, iki-means clustering, PCA, and morebr - Reinforcement Learning Q-Learning and Deep Q-Learningbr - Non-Machine Learning Tools graph theory, ARIMA, linear progr