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Cracking the Data Science Interview: 101+ Data Science Questions & Solutions

Cracking the Data Science Interview: 101+ Data Science Questions & Solutions

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Product Details

Necessary Prerequisites (statistics, probability, linear algebra, and computer science)||18 Big Ideas in Data Science (such as Occam’s Razor, Overfitting, Bias/Variance Tradeoff, Cloud Computing, and Curse of Dimensionality)||Data Wrangling (exploratory data analysis, feature engineering, data cleaning and visualization)||Machine Learning Models (such as k-NN, random forests, boosting, neural networks, k-means clustering, PCA, and more)||Reinforcement Learning (Q-Learning and Deep Q-Learning)||Necessary Prerequisites (statistics, probability, linear algebra, and computer science)||18 Big Ideas in Data Science (such as Occam’s Razor, Overfitting, Bias/Variance Tradeoff, Cloud Computing, and Curse of Dimensionality)||Data Wrangling (exploratory data analysis, feature engineering, data cleaning and visualization)||Machine Learning Models (such as k-NN, random forests, boosting, neural networks, k-means clustering, PCA, and more)||Reinforcement Learning (Q-Learning and Deep Q-Learning)||Non-Machine Learning Tools (graph theory, ARIMA, linear programming)||Case Studies (a look at what data science means at companies like store and Uber)

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