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.
