Data analysis with open source tools ebook


 

Editorial Reviews. Book Description. A hands-on guide for programmers and data scientists Rent On clicking this link, a new layer will be open. $ On clicking this link, a new layer will be open. eBook features: Highlight, take notes, and search in the. Data Analysis with Open Source Tools.. [Philipp Janert] -- Collecting data is relatively easy, but turning raw information into something useful. 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.

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Data Analysis With Open Source Tools Ebook

Read "Data Analysis with Open Source Tools A Hands-On Guide for Programmers and Data Scientists" by Philipp K. Janert available from. 'Data Analysis with Open Source Tools' by Philipp K. Janert - Download a free ebook sample and give it a try! Don't forget to share it, too. View Notes - Data Analysis with Open Source ruthenpress.info from CRITICAL 10 at Vietnam National University, Ho Chi Minh City. ruthenpress.info

Goodreads helps you keep track of books you want to read. Want to Read saving…. Want to Read Currently Reading Read. Other editions. Enlarge cover. Error rating book. Refresh and try again. Open Preview See a Problem? Details if other: Thanks for telling us about the problem. Return to Book Page. Data Analysis with Open Source Tools: 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.

Data Analysis with Open Source Tools

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Data Analysis with Open Source Tools. Philipp Janert Publisher: O'Reilly Media, Inc. English View all editions and formats Summary: 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 contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications. Along the way, you'll experiment with concepts. Read more Show all links. Allow this favorite library to be seen by others Keep this favorite library private.

Find a copy in the library Finding libraries that hold this item Electronic books Additional Physical Format: Print version: Janert, Philipp. Document, Internet resource Document Type: Philipp Janert Find more information about: Philipp Janert.

Real World Data Analysis shows you how you think about data and the results you want to achieve with it. Author Philipp Janert teaches you how to effectively approach data analysis problems, and how to extract all the available information from your data. This book shows you how to look at the results and know whether they're meaningful.

Reviews User-contributed reviews Add a review and share your thoughts with other readers. 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. This book subverts this by being principally of how to think about data analysis, and providing examples using different tools primarily R and Python, but he uses other examples as well Among other topics, Janert covers graphing, single and multi-variable analysis, probability, data modeling, statistics, simulation, component analysis, reporting, financial modeling and predictive analytics.

In each section he starts by explaining the concepts, what it is for, and just as important what each topic is not. Working through it you get a sense of not just what and how of the various tools and methods discussed, but why they are used as well as some ways these techniques are misapplied.

New book: Data Analysis with Open Source Tools

Janert also illustrates the methods using some data analysis environments. What is helpful here is the focus is on what techniques and capabilities are needed in the tool, not the tool itself. Instead of being a cheerleader for a particular tool, Janert discusses in his appendix the qualities that make environments such as Matlab, R and Python good data analysis environments. However, this focus means that he does not teach any particular tool.

If you want to learn how to use a particular tool for data analysis, you are better off getting a book on R or Python or Matlab, Excel, etc. But this was a book that was on my to download list even if I did not get it from them. The book page at O'Reilly. Data Analysis with Open Source Tools Jan 31, John Orman rated it really liked it. 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 I used this book in my online Data Analysis class, in which we used the open source language R.

Also includes probability modeling, and an interesting chapter on using statistics in mythbusting. How to mine data, perform simulations, and create predictive models. Useful appendices cover scientific software tools, and the handling of data.

An excellent review of using open source software to analyze and graph data. Oct 23, Romain rated it it was amazing Shelves: Si vous cherchez ce type d'ouvrage, je vous conseille de vous plonger dans [Book: S'il existe de meilleurs outils maintenant c'est tant mieux et ils n'exemptent toujours pas -- me semble-t-il -- de comprendre ce que l'on fait. Basic classical statistics has always been somewhat of a mystery to me: Mar 26, Igor rated it really liked it Shelves: Book is focused not on tools mostly outdated by now , but on methods, skills and underlying math, with many examples of applications in real-life context.

I really liked this approach, even though some equations were a bit too hard and some examples are too far from my area of interest. Dec 31, Earo rated it it was amazing Shelves: 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. Jul 13, Sweemeng Ng rated it it was amazing. 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. Learning Bayesian Models with R. Hari M.

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