How can you start to work in a wide range of interdisciplinary fields which is to obscured the hype? This insightful book, based on the Columbia University introduction to class data, tell you what you need to know. In many of the lecture program, scientists of data from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting the case study and the code they use
Table of Contents
Chapter 1. Introduction: What Is Data Science? Chapter 2. Statistical Inference, Exploratory Data Analysis, and the Data Science Process Chapter 3. Algorithms Chapter 4. Spam Filters, Naive Bayes, and Wrangling Chapter 5. Logistic Regression Chapter 6. Time Stamps and Financial Modeling Chapter 7. Extracting Meaning from Data Chapter 8. Recommendation Engines: Building a User-Facing Data Product at Scale Chapter 9. Data Visualization and Fraud Detection Chapter 10. Social Networks and Data Journalism Chapter 11. Causality Chapter 12. Epidemiology Chapter 13. Lessons Learned from Data Competitions: Data Leakage and Model Evaluation Chapter 14. Data Engineering: MapReduce, Pregel, and Hadoop Chapter 15. The Students Speak Chapter 16. Next-Generation Data Scientists, Hubris, and Ethics
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