Media praise for Data Analysis with Open Source Tools

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"Data Analysis with Open Source Tools [author] Mr. Janert has pursued a consulting practice in algorithm development, data analysis, and mathematical modeling. As such, his specialty makes him the ideal subject matter expert to write such a book."
-- Mike Riley, DrDobbs.com

"The emphasis on thinking informs the book throughout, and the end result is that the reader is challenged to think about problems of data analysis and the application of different analytical methods and tools. This is definitely not a book that simply teaches how to apply formulas; Jenert encourages the reader to learn to think about data analysis problems on a conceptual level."
-- A. Jurek, BlogCritics

"Data Ana­lysis with Open Source Tools by Phil­ipp K Jan­ert is a simply superb, solid and exhaust­ive syn­thesis of instruc­tion, work­shops and hands-on exer­cises designed for those ser­i­ous about con­duct­ing pro­fes­sional data ana­lysis. This is not a light­weight under­tak­ing...All in all, this is a very good book. It actu­ally does more than it prom­ises and deliv­ers a com­pre­hens­ive and effect­ive course in data ana­lysis with superb hands-on exer­cises to drive home the learning."
-- Shawn Day, randomosity

"Data analysis using open source tools is a straightforward, well explained and practical book which does not give just a laundry list of techniques but also useful pointers in how to use them intelligently. The book...is strongly recommended."
-- Manoj Rengarajan, BookPleasures.com

"This book offers a deep approach to real world tasks. There is much more text than code...It is hard to fault this book except perhaps that at nearly 500 pages, it is too short! "
-- Neil McNaughton, Oil IT Journal

"This book is perfect for hands-on readers, wanting to achieve specific goals without getting entangled in formal definitions by getting right to the point. Here is an analogy: a Common Kite is depicted on the front cover of the book and as the bird waiting for its prey, you’ll be able to analyse the situation, understand it and take a clear decision after this read."
-- Mathieu Hanna, Adviso

"I love this book a lot."
-- Rik Farrow, ;login:

"This is, actually, the only book I have seen that covers both theory, tools and practice with such good command and depth."
-- Edmon Begoli, Toolbox.com, Researcher

"Philipp K. Janert, physicist and software engineer by training and consulting profession, this book demonstrates that with a little intelligence, a solid understanding of statistical tools and the needs of businesses is possible to obtain the information needed. The book is not about the use of sophisticated programs available only to large companies, any normal computer with Ubuntu or any other linux distribution with Windows Python interpreter can execute." Full Review >
-- Diego Gonzalez, dggonzalez5549.wordpress.com

"Summary:Data Analysis with Open Source Tools is a reference book that explains in detail the many ways to make sense of data. You will find ways to interpret data using statistics, plots, and mathematics. It also covers traditional data mining (simulation, clustering) andfun topics on modeling for making estimates. You… " Full Review >
-- Boanerges Aleman-Meza, aleman.blogs.rice.edu/

"Data Analysis with Open Source Tools does a great job covering a lot of topics in way that balances theoretical explanations and practical demonstration. In keeping true to its title, a wealth of tools (and data sources) are identified and explored. Because the book offers a balance between explanation and… " Full Review >
-- Hal Smith, profhal.blogspot.com/

"First of all I have to state clear that this book has "run over me"! It is a very good and comprehensive exercise by Mr Janert on how to produce "readable" graphs (read information) on top of massive data volumes, all with open source tools such as gnuplot, matplotlib, R, numpy, chaco, etc." Full Review >
-- Rafael Flores, zalakain2010.blogspot.com

"If it is true that this book will not guide you to develop a data analysis tool with all the specific programming details of Python and R, it is also true that you will gain worthy professional experiences to design strategies, architectures and policies for data analysis." Full Review >
-- Eder Andres Avila Nino, ederandresan.blogspot.com/

"The author takes the approach of educating the reader on what is necessary to turn raw data into useful information or knowledge, then provides some example code "seeds" to get the reader started. If you are looking for detailed code example you can copy-and-paste into an interpreter and run, you will be left wanting. However, if you need to learn a foundation that can be utilized in multiple languages or platforms, this will be a useful tutorial and reference." Full Review >
-- Matt Keranen, www.hybriddba.com

"Although somewhat intermediate (and not for those that fear math) this is quite a solid book. It also makes me happy whenever I see Python or open source. This book offers helpful ways of turning raw data into something useful. This book is split into three parts: 1. Graphing 2. Modeling 3. Mining 4. Applications Section 1 was a bit dry, as statistics often can be. Section 2 was also useful but I got the most value from section 3 and 4. " Full Review >
-- Benjamin Osment, benosment.blogspot.com/

"Data Analysis with Open Source Tools is an excellent primer for those who need an overview of the field of Data Analysis, along with pointers to some of the most popular free and open source tools. The book does have it's short comings." Full Review >
-- Levon Lloyd, levonlloyd.tumblr.com/

"If you are expecting a book filled with examples of NoSQL databases like Hadoop and Cassandra, you are definitely going to be disappointed. The key with this book is to look at the cover. Data analysis is the main point of this book, and open source tools are really just a nice sidebar. The data analysis information is fairly solid and ranges from some basic methods, through statistics and eventually getting to some machine learning methods like clustering and categorization. The open source tools portions of the book are based on examples of the various analysis methods, but do not delve too deeply into how the tools work." Full Review >
-- Robert Diana, regulargeek.com/

"Whether analyzing data is part of your everyday job or one of your projects needs specific data analysis, the same questions arise: What tools are the most suited for the task? Which techniques are the most adapted? What are the numbers really saying? Are they meaningful?" Full Review >
-- Laurent Jacobs, www.adviso.ca/blog/

"It starts a little slow with an enumeration of some graphical methods. The enumerative approach in the beginning chapters might work for some readers but it usually puts me to sleep so initially the reading was a little tough but I'm glad I stuck with it because the later… " Full Review >
-- David Karapetyan, articulationstudy.posterous.com

"Data Analysis with Open Source Tools by Philipp K Janert is a simply superb, solid and exhaustive synthesis of instruction, workshops and hands-on exercises designed for those serious about conducting professional data analysis. This is not a lightweight undertaking. This is a serious get-down-to-it and do-it-right kind of manual. The author (as has been mentioned elsewhere) is passionate about his subject and it shows. He knows how to convey the most complex concepts in an approachable and effective way." Full Review >
-- Shawn Day, shawnday.com/randomosity

"Data Analysis with Open Source Tools is an excellent book for experienced analysts of data. The author is obviously enthusiastic about his topic and presents the information in a lucid, readable format. The book is not, however, a cookbook, or step-by-step guide to using a tool or suite for delving… " Full Review >
-- John Brady, www.jfbrady.com/blog/

"Data Analysis with Open Source Tools by Philipp K JanertMy rating: 5 of 5 starsThis 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… " Full Review >
-- Kiatikun Luangkesorn, lugerpitt.blogspot.com/search/label/programming