Goodreads helps you keep track of books you want to read.
Start by marking “Data Analysis with Open Source Tools” as Want to Read:
Data Analysis with Open Source Tools
Enlarge cover
Rate this book
Clear rating
Open Preview

Data Analysis with Open Source Tools

4.03  ·  Rating Details  ·  175 Ratings  ·  13 Reviews
Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contain ...more
ebook, 540 pages
Published November 11th 2010 by O'Reilly Media (first published 2010)
More Details... edit details

Friend Reviews

To see what your friends thought of this book, please sign up.

Reader Q&A

To ask other readers questions about Data Analysis with Open Source Tools, please sign up.

Be the first to ask a question about Data Analysis with Open Source Tools

This book is not yet featured on Listopia. Add this book to your favorite list »

Community Reviews

(showing 1-30 of 1,025)
filter  |  sort: default (?)  |  Rating Details
Louis
Dec 27, 2010 Louis rated it it was amazing  ·  review of another edition
This is a book that is how to think about data analysis, not only how to perform data analysis. Like a good data analysis, Janert's book is about insight and comprehension, not computation. And because of this it should be a part of any analysts bookshelf, set apart from all the books that merely teach tools and techniques.

The practice of data analysis can get a bad rap, especially by those who think that data analysis is only statistics. Most books on data analysis don’t help because they focus
...more
John Orman
Feb 18, 2013 John Orman rated it really liked it  ·  review of another edition
I used this book in my online Data Analysis class, in which we used the open source language R. The book also mentions Octave, a clone of Matlab. I used Octave for my online Machine Learning class.
Python and Java are also given a brief description.
The reader is also asked to investigate Perl, Ruby, regular expressions, databases, and Unix.

This book has some very good sections on graphical analysis of data. Also includes probability modeling, and an interesting chapter on using statistics in myth
...more
Clintweathers
Good in its time. Outdated now.
Daniel
Feb 01, 2016 Daniel rated it really liked it  ·  review of another edition
Pleasantly focussed on methods, so that the somewhat dated view on technologies doesn't hurt too much.
Delhi Irc
Location: ND5 IRC
Accession no: DL026845
Earo
Dec 31, 2012 Earo rated it it was amazing  ·  review of another edition
Shelves: data-analysis
Author keeps placing emphasis on insights instead of numbers while working with data. The ultimate goal of data analysis is to understand how the system works, not to show off how proficient you are at Math. That's the true spirit of professionalism. Some annoying jargon are well explained in a plain manner. Little sections on R.
Sweemeng Ng
Aug 07, 2012 Sweemeng Ng rated it it was amazing  ·  review of another edition
This book focuses on methods and experience, using tools only for demonstrating on the topic.

Where many books already cover tools, this book covers what many don't, insights and experience. While many topics with enough explanation on the method and where to use it.

Highly recommended
Emily Leathers
Jan 02, 2012 Emily Leathers rated it liked it  ·  review of another edition
Recommends it for: Analysis Team
I haven't touched this for a while, so it seemed more appropriate to move it to the on-hold category. I was really intrigued by the sections that I did read though, so I plan to get back to it soon.
Vuk Trifkovic
Feb 17, 2011 Vuk Trifkovic rated it really liked it  ·  review of another edition
Shelves: o-reilly, tech
Very good. Focus is firmly on the methods, but with just enough tooling or practical data. I'd start from this, and then dive into specific toolsets, say R or some Python libs..
Andries Burger
May 05, 2011 Andries Burger rated it really liked it
Shelves:
Just started on this book. Have lots of data to turn into information. Some obscure, some blatant.

Will add to this review as I work my way through the book...
Matt Heavner
Oct 24, 2011 Matt Heavner rated it really liked it  ·  review of another edition
This is a good thought provoking read. It is a reminder of lots of techniques I've already "learned" -- but a great practical review and refresher.
Sefa
May 08, 2015 Sefa rated it really liked it  ·  review of another edition
Shelves: programming, data
Data analysis book using Python/R and focusing on more methods in a not math-heavy way, rather than implementation details.
A
Apr 04, 2012 A rated it really liked it  ·  review of another edition
Good refresher on data analysis for more detailed work
Rachel Eve
Rachel Eve marked it as to-read
Feb 10, 2016
Jakub Orlowski
Jakub Orlowski marked it as to-read
Feb 10, 2016
Valerie
Valerie marked it as to-read
Feb 10, 2016
Marissa Saunders
Marissa Saunders marked it as to-read
Feb 09, 2016
Dariusz Domagala
Dariusz Domagala marked it as to-read
Feb 05, 2016
Swathi
Swathi rated it it was ok
Feb 05, 2016
Dhaval Bhavsar
Dhaval Bhavsar rated it liked it
Feb 04, 2016
Erin
Erin marked it as to-read
Feb 02, 2016
Gbeddiejulius
Gbeddiejulius marked it as to-read
Feb 01, 2016
Fletcherjanel
Fletcherjanel marked it as to-read
Jan 31, 2016
Julienzanni
Julienzanni marked it as to-read
Jan 31, 2016
Jennyliu
Jennyliu marked it as to-read
Jan 29, 2016
Stephanie Krislov
Stephanie Krislov marked it as to-read
Jan 28, 2016
Liang Chang
Liang Chang rated it really liked it
Jan 27, 2016
Dippoman Mukherjee
Dippoman Mukherjee marked it as to-read
Jan 25, 2016
Shriyut Raut
Shriyut Raut rated it it was amazing
Jan 25, 2016
« previous 1 3 4 5 6 7 8 9 34 35 next »
There are no discussion topics on this book yet. Be the first to start one »
  • Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites
  • Python for Data Analysis
  • Machine Learning for Hackers
  • Think Stats
  • Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)
  • Bad Data Handbook: Cleaning Up The Data So You Can Get Back To Work
  • Beautiful Data: The Stories Behind Elegant Data Solutions (Theory In Practice, #31)
  • Head First Data Analysis: A Learner's Guide to Big Numbers, Statistics, and Good Decisions
  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  • Interactive Data Visualization for the Web
  • The Art of R Programming: A Tour of Statistical Software Design
  • Machine Learning in Action
  • Taming Text
  • Pattern Recognition and Machine Learning
  • Statistics in a Nutshell: A Desktop Quick Reference
  • R in Action
  • Natural Language Processing with Python
  • MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems

Goodreads is hiring!

If you like books and love to build cool products, we may be looking for you.
Learn more »

Share This Book