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Mining of Massive Datasets | #datascience #freebook

Mining of Massive Datasets | #datascience #freebook | Public Datasets - Open Data - | Scoop.it

Via ukituki
luiy's insight:

Preface and Table of Content

Chapter 1. Data Mining

Chapter 2. Map-Reduce and the New Software Stack

Chapter 3. Finding Similar Items

Chapter 4. Mining Data Streams

Chapter 5. Link Analysis

Chapter 6. Frequent Itemsets

Chapter 7. Clustering

Chapter 8. Advertising on the Web

Chapter 9. Recommendation Systems

Chapter 10. Mining Social-Network Graphs

Chapter 11. Dimensionality Reduction

Chapter 12. Large-Scale Machine Learning

 

Download Full Book :

http://infolab.stanford.edu/~ullman/mmds/book.pdf

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ukituki's curator insight, August 28, 2014 6:22 PM

The book is based on Stanford Computer Science course CS246: Mining Massive Datasets (and CS345A: Data Mining).

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Mining of Massive Datasets | #bigdata #datascience #freebook

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Jay Ratcliff's curator insight, February 20, 2014 11:24 AM

I like this book.  I had a class a Coursera where we used this text.  One of the things it helped me with was the mechanics of clustering and using different ways to measure distance between objects in a euclidean or non-euclidean space.  Plus there is a lot of stuff on Map-Reduce as well.

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Now available: Planning for #BigData | #opendata #freebook

Now available: Planning for #BigData | #opendata #freebook | Public Datasets - Open Data - | Scoop.it
Planning for Big Data is a new book that helps you understand what big data is, why it matters, and where to get started.
luiy's insight:

" thanks to an open source project called Hadoop, commodity Linux hardware and cloud computing, this power is in reach for everyone. A data revolution is sweeping business, government and science, with consequences as far reaching and long lasting as the web itself. "


Our aim with Strata is to help you understand what big data is, why it matters, and where to get started. In the wake the recent conference, we’re delighted to announce the publication of our “Planning for Big Data” book. Available as a free download, the book contains the best insights from O’Reilly Radar authors over the past three months, including myself, Alistair Croll, Julie Steele and Mike Loukides.

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Mlik Sahib's curator insight, March 4, 2014 9:17 PM
Where to start?

"Every revolution has to start somewhere, and the question for many is “how can data science and big data help my organization?” After years of data processing choices being straightforward, there’s now a diverse landscape to negotiate. What’s more, to become data driven, you must grapple with changes that are cultural as well as technological.

Our aim with Strata is to help you understand what big data is, why it matters, and where to get started. In the wake the recent conference, we’re delighted to announce the publication of our “Planning for Big Data” book. Available as a free download, the book contains the best insights from O’Reilly Radar authors over the past three months, including myself, Alistair Croll, Julie Steele and Mike Loukides.

“Planning for Big Data” is for anybody looking to get a concise overview of the opportunity and technologies associated with big data. If you’re already working with big data, hand this book to your colleagues or executives to help them better appreciate the issues and possibilities."

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Introduction to Data Science | #datascience #freebook #bigdata

Introduction to Data Science | #datascience #freebook #bigdata | Public Datasets - Open Data - | Scoop.it
luiy's insight:
Version 2 of Introduction to Data Science is Here!

This is the companion site to the electronic textbook, Introduction to Data Science, by Jeffrey Stanton. This book provides non-technical readers with a gentle introduction to essential concepts and activities of data science. For more technical readers, the book provides explanations and code for a range of interesting applications using the open source R language for statistical computing and graphics.

If you find errors or omissions, please contact the author, Jeffrey Stanton, at jmstanto@syr.edu. The book is suitable for an introductory course in data science where students have a varied background or as a supplement to an advanced analytics course where students would benefit from an introduction to R. 

The electronic textbook is available in an interactive version for the iPad at the iTunes bookstore. Apple has recategorized the book from the "textbook" category to the "book" category in order to make it available internationally:

http://itunes.apple.com/us/book/introduction-to-data-science/id529088127?mt=11 

You can also download the non-interactive, PDF version of the book here (caution, 22 MB download):

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