数据科学

Naive Bayes for Article Classification Sample R Code for Dealing with the NYT API5. Logistic Regression Thought Experiments Classifiers Runtime You Interpretability Scalability M6D Logistic Regression Case Study Chck Models The Underlying Math6．1ime Stamps and Financial Modeling 7．Extracting Meaning from Data 8．Recommendation Engines：Building a User-Facing Data Product at Scale9．Data Visualization and Fraud Detection 10．SociaI Networks and Data Journalism 11．Causality 12．Epidemiology 13．Lessons Learned from Data Competitions：Data Leakage and Model Evaluation14．Data Engineering：MapReduce，Pregel，and Hadoop 15．The Students Speak16．Next-Generation Data Scientists,Hubris，and Ethics Index

现在人们已经意识到数据可以让选举或者商业模式变得不同，数据科学作为一项职业正在不断发展。但是你应该如何在这样一个广阔而又错综复杂的交叉学科领域中开展工作呢？舒特、奥尼尔著的《数据科学(影印版)》这本书将会告诉你所需要了解的一切。它富有深刻见解，是根据哥伦比亚大学的数据科学课程的讲义整理而成。