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Author: Cathy O'Neil
ISBN : B00FRSNHDC
New from $17.27
Format: PDF
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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.
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- File Size: 11364 KB
- Print Length: 406 pages
- Simultaneous Device Usage: Unlimited
- Publisher: O'Reilly Media; 1 edition (October 10, 2013)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B00FRSNHDC
- Text-to-Speech: Enabled
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- Lending: Not Enabled
- Amazon Best Sellers Rank: #14,566 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Free Doing Data Science: Straight Talk from the Frontline
... helps the medicine go down, as Mary Poppins used to say. An IT-focused publisher, O'Reilly has twice before used the "book as collection of chapters by different contributors" formula in its foray into the attractive "data" niche, with such titles as "Beautiful data" and "Bad data". "Doing data science" - by the way, I prefer Hastie and Tibshirani's "statistical learning" to the fuzzy and grandiose "data science" - follows the same approach, but, with its subject matter being closer to the academe, the company enlisted two young PhDs to steer the collaborative effort. Rachel Schutt took the lead as author and editor, and, assisted by Cathy O'Neil, produced an engaging, informal - you don't often see "science" in the title and "huge-ass" in the text - yet sufficiently technical to be hands-on, sequence-of-vignettes-styled book. Imagine a mash-up of a magazine article and a textbook. Neither part may be best-in-class, but their combination makes for a "unique selling proposition".
Well, maybe not a textbook. Most textbooks are carefully written and carefully checked. In contrast, when I see "Doing data science" introduce the ROC curve in three places, one of which translates the "O" as "operator", I can guess that this is a copy-paste of papers by three contributors. When Dr. O'Neil casually redefines an English word ("causal") to avoid rewriting a couple of sentences, or pronounces, on page 159, that "priors reduce degrees of freedom" - this is painfully meaningless, and neither term is defined, only name-checked - I suspect that she knows better, but just did not feel like spending more time on her half-chapter. Neither author speaks of their own projects. The occasional code listings are borrowed as well, thrown in without editing or comments.
I found this book to be a very odd bird indeed. It is one book you can read from back cover to front cover and not be at a disadvantage. This is because the book is really just a collection of presentations made by various people to a class taught by the primary author Rachel Schutt at Columbia University in the Fall of 2012 – Introduction to Data Science. It wasn’t entirely clear what content Schutt was directly responsible for since only some of the chapters indicate who the contributors were (one of the chapters was contributed by a group of her students!). The co-author, Cathy O’Neil, I’ve encountered before as an outspoken blogger going by the name “mathbabe” but it wasn’t specifically stated how she became part of the book project, other than to say she was one of the students in Schutt’s class. Chapter 6 was partly written by O’Neil.
Both Schutt and O’Neil are Ph.D.s data science appropriate fields, but the book was not “written” by the two, rather they seemed to have performed some kind of editing function with the materials submitted by each contributor and added commentaries of their own. As a result, the book is a hodgepodge of anecdotes, factoids, R code snippets, plots, and mathematics, all from the in-class presentations. I enjoy seeing math in data science books, but the equations in this book were sort of just floating there requiring the reader to explore further at another time.
Although I have issues with the book as it is not any sort of text for the field, I did enjoy reading it with a number of “Ah, I didn’t know that!” moments. Schutt’s credentials in data science are considerable, having worked at Google for a few years around the same time that “data science” was growing up in Silicon Valley.