Doing Data Science: Straight Talk from the Frontline

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.

In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.

Topics include:

• Statistical inference, exploratory data analysis, and the data science process
• Algorithms
• Spam filters, Naive Bayes, and data wrangling
• Logistic regression
• Financial modeling
• Recommendation engines and causality
• Data visualization
• Social networks and data journalism
• Data engineering, MapReduce, Pregel, and Hadoop

Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

• Reproducible Research with R and R Studio, Second Edition (Chapman & Hall/CRC The R Series)
• The Statistical Sleuth: A Course in Methods of Data Analysis
• The Art of Data Science
• The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists
• Practical Statistics for Data Scientists: 50 Essential Concepts
• Python Data Science Handbook: Essential Tools for Working with Data
• R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
• Naked Statistics: Stripping the Dread from the Data
• Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
• Data Science from Scratch: First Principles with Python