Practical Data Science with R Kindle ´ Science with

Practical Data Science with R Kindle ´ Science with


Practical Data Science with R [Download] ➻ Practical Data Science with R By Nina Zumel – Centrumpowypadkowe.co.uk Summary Practical Data Science with R lives up to its name It explains basic principles without the theoretical mumbo jumbo and jumps right to the real use cases you ll face as you collect, curate, an Summary Practical Data Science with R Science with PDF/EPUB ç lives up to its name It explains basic principles without the theoretical mumbo jumbo and jumps right to the real use cases you ll face as you collect, curate, and analyze the data crucial to the success of your business You ll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision supportPurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning PublicationsAbout the BookBusiness analysts and developers are Practical Data PDF or increasingly collecting, curating, analyzing, and reporting on crucial business data The R language and its associated tools provide a straightforward way to tackle day to day data science tasks without a lot of academic theory or advanced mathematics Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments such as A B tests , build predictive models, and present results to audiences of all levelsThis Data Science with PDF ´ book is accessible to readers without a background in data science Some familiarity with basic statistics, R, or another scripting language is assumedWhat s InsideData science for the business professionalStatistical analysis using the R languageProject lifecycle, from planning to deliveryNumerous instantly familiar use casesKeys to effective data presentationsAbout the AuthorsNina Zumel and John Mount are cofounders of a San Francisco based data science consulting firm Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win vectorTable of ContentsPARTINTRODUCTION TO DATA SCIENCEThe data science processLoading data into RExploring dataManaging dataPARTMODELING METHODSChoosing and evaluating modelsMemorization methodsLinear and logistic regressionUnsupervised methodsExploring advanced methodsPARTDELIVERING RESULTSDocumentation and deploymentProducing effective presentations.


10 thoughts on “Practical Data Science with R

  1. Rodrigo Rivera Rodrigo Rivera says:

    Practical data science with R is an original book, yet not a great one, and I would not recommend it This book belongs to the trend of data science by practitioners They promote themselves as material with a practical focus and accessible writing style However, usually they fail at explaining the theory behind This book suffers this malaise, it struggles to explain the principles and sometimes is even wrong about basic concepts in stats for example, the explanation of heteroscedasticity Practical data science with R is an original book, yet not a great one, and I would not recommend it This book belongs to the trend of data science by practitioners They promote themselves as material with a practical focus and accessible writing style However, usually they fail at explaining the theory behind This book suffers this malaise, it struggles to explain the principles and sometimes is even wrong about basic concepts in stats for example, the explanation of heteroscedasticity Not everything was terrible, it introduces R, version control, databases, a bit of visualization and some techniques that everyone doing data science should have on their toolbox Definitely better than Doing Data Science Straight Talk from the Frontline , but not memorable at all


  2. Ji Ji says:

    Quickly scanned through this book The code base is well prepared The business use case are described Also glad to find that the author took care of model preparation, which is rare for a book on data science and R Drawbacks are obvious as well the theories behind the codes are explained neither well nor too accurately Still, I may go back to this book for its richness of R code.


  3. JDK1962 JDK1962 says:

    This is my January book for my read one work book per month New Year s resolution Good practical book on applying machine learning Lots of examples, though I probably would have appreciatedeffort to use a single domain or business , rather than constantly leaping around, just because taking a number of approaches to a single problem area is a useful skill to develop I d also have liked to seegeneric functions most of their illustrations would need to adapted For example, the This is my January book for my read one work book per month New Year s resolution Good practical book on applying machine learning Lots of examples, though I probably would have appreciatedeffort to use a single domain or business , rather than constantly leaping around, just because taking a number of approaches to a single problem area is a useful skill to develop I d also have liked to seegeneric functions most of their illustrations would need to adapted For example, they used a notation in their function for calculation of Euclidean distance to indicate that you do the calculation for each dimension, but it would have been trivial to write the function to take the number of dimensions from the input vectors Final quibble is that their treatment of kernels and SVMs seemed fartheoretical than the other sections.So not, a definitive reference, but definitely a good book to have on your shelf when working an ML project in R.Also, I seem to recall that they had a n part series on their blog recently on verification and validation of modelsmaybe for the second edition, they ll add a chapter specifically on this topic, in addition to the tips throughout on which summary stats are indicating model soundness


  4. Clintweathers Clintweathers says:

    I m not always happy with the Manning texts in comparison to the ORly books but this one was great.Step by step instructions walk the reader through getting the results shown in the book.The code is all in a github repo, and the authors introduce new tools that they created SQL Screwdriver, et al for use by everyone This isn t a book about R per se, but a book about how to choose and attack datascience projects and maybe the title is misleading since most of us data science types actuall I m not always happy with the Manning texts in comparison to the ORly books but this one was great.Step by step instructions walk the reader through getting the results shown in the book.The code is all in a github repo, and the authors introduce new tools that they created SQL Screwdriver, et al for use by everyone This isn t a book about R per se, but a book about how to choose and attack datascience projects and maybe the title is misleading since most of us data science types actually do data analysis or data engineering The chapter on classification and clustering algorithms is a perfect example They use R to teach the algos, rather than using algo examples to walk you through coding in R.It s easy enough to just follow along with the code in the book, but you ll get the most out of it if you sit down with RStudio and work through it.Couldn t be happier having spent the money for a dead tree copy of this one It s already been heavily marked up, and there sto come


  5. Troy Troy says:

    This book is great introduction to Data Science in R However, as the title implies, it is geared towards those looking for only a high level, quick overview to Data Science practices as they apply in the business world as well as how to communicate results to non practitioners and business partners If this is what you are looking for then I recommend this book If you are looking for ain depth introduction to the theory of data science and machine learning, I would look elsewhere, as the This book is great introduction to Data Science in R However, as the title implies, it is geared towards those looking for only a high level, quick overview to Data Science practices as they apply in the business world as well as how to communicate results to non practitioners and business partners If this is what you are looking for then I recommend this book If you are looking for ain depth introduction to the theory of data science and machine learning, I would look elsewhere, as the topics are covered in a very superficial manner Had I doneresearch into this book before purchasing, I would not have bought it instead opting for atheoretical and statistics heavy primer Zumel and Mount do an excellent and concise job however of making data science accessible to those who have an interest in it at the business level


  6. Delhi Irc Delhi Irc says:

    Location ND6 IRCAccession no DL026996


Leave a Reply

Your email address will not be published. Required fields are marked *