Download Doing Data Science By Rachel Schutt and Cathy O’Neil

introduction

Data science is an emerging field in industry, and as yet, it is not welldefined as an academic subject. This book represents an ongoing investigation into the central question: “What is data science?” It’s based on a class called “Introduction to Data Science,” which I designed and taught at Columbia University for the first time in the Fall of 2012. In order to understand this book and its origins, it might help you to understand a little bit about me and what my motivations were for creating the class. Motivation In short, I created a course that I wish had existed when I was in college, but that was the 1990s, and we weren’t in the midst of a data explosion, so the class couldn’t have existed back then. I was a math major as an undergraduate, and the track I was on was theoretical and prooforiented. While I am glad I took this path, and feel it trained me for rigorous problem-solving, I would have also liked to have been exposed then to ways those skills could be put to use to solve real-world problems.

Table of Contents

  1. Introduction: What Is Data Science?. 
  2. Statistical Inference, Exploratory Data Analysis, and the Data Science Process. 
  3. Spam Filters, Naive Bayes, and Wrangling. .  
  4. Logistic Regression.  
  5. Time Stamps and Financial Modeling.
  6. Extracting Meaning from Data. 
  7. Recommendation Engines: Building a User-Facing Data Product at Scale 
  8. Data Visualization and Fraud Detection 
  9. Social Networks and Data Journalism 
  10. Lessons Learned from Data Competitions: Data Leakage and Model Evaluation. 
  11. Data Engineering: MapReduce, Pregel, and Hadoop 
  12. The Students Speak 
  13. Next-Generation Data Scientists, Hubris, and Ethics 

 

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