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Written in easy language that is simple to r each algorithm, it demonstrates the ideas with a easy example andit explains the primary mathematics used in the algorithm.Of course if you wish to obtain a Ph.D in machine learning, this is not the book for ever, if you have amazing technical background in computer science and math,you will learn a lot on the modern machine algorithms.
I really liked this book, I've read about 70% and I feel that it is very well organized. I first tried reading Applied Predictive Modeling, but couldn't grasp the concepts, then I tried this book, and it created learning the concepts waaay more effective because of Lantz writing style and nice illustrations. Its given me a amazing high level understanding of the different machine learning algorithms and it has some amazing primary intro to R such as vectors, dataframes, and lists. The hands on exercises were super helpful in learning the concepts also.
If you need a proper introduction to Machine Learning for professional reasons or even just for your own edification, do yourself a favor and pick up this gem of ke sure you are 'language agnostic' before you begin. Allow me explain, right now the python libraries are all the rage: Pytorch, Keras, TensorFlow, ScikitLearn, etc... Thus, you might be tempted to believe that in getting yourself acquainted with ML in R you are putting yourself at a disadvantage. You'd be uth it, you should be approaching the topic with the idea of learning from a conceptual and practical standpoint, albeit at a high level. The language you use will create small difference at the beginning. This was my main concern as I required to learn "python ML" for professional reasons. Create no mistake, this book along with the available code up on the author's GitHub will tutorial you through the language, the hard to grasp concepts, and the terminology in a method that is pedagogically so effective that you'd be left wondering how it is that most technical books never reach this level of clarity. You'll be carrying conversations with experienced ML practitioners in no time, without embarrassing yourself (too much).Take it for what it is though, an introduction. If you need to know every pedantic detail about how neural networks learn, the massive mathematical proofs behind the algorithms, etc., then you'd be much better served looking elsewhere.Once you go through this text, you'll be able to jump on the Python bandwagon all while avoiding the risk of having the language's technicalities distract you from the core concepts.Go for it, satisfied learning.
Amazing book. Was a needed book for a masters level class in data analytics program I am in. Simple to read and follow. My professor?? He was a various story. Lol.
I'm torn. There are some useful gems in this book, and for the most part, the presentation is simple, albeit a bit pedantic and cartoonish at times. If I was trying to obtain up to snuff on a fresh machine learning method, I might begin here, since it *does* provide starter code for a dozens of problems. That's quite handy. It doesn't, however, go into much depth at all on any one topic. You can't read this book and expect to know how to do any one of these methods well. Certainly, it's a tall to ask any one book to cover all ML subjects in depth, but any potential reader should be aware that this just skims the surface of a whole bunch of topics.On top of this, who in the globe edited this book? Every other page has horrible typos, missing words, repeated sentences. These are not trivial errors either. This is a book about data analysis and yet the reported data are clearly wrong in places, e.g., a effect is listed as .06 percent in one spot and then .0006 in another (p. 271). Primary subject-verb agreement errors riddle the text, e.g. "These output is shown as follows". Sometimes these are trivial errors, but other times you have absolutely no idea what the intended meaning is. I have about 100 pages more to read but I'm starting to wonder if I'm just wasting my time.
I bought this book with some prior knowledge on Machine Learning. I would say that I am very happy with my purchase. The only weakness of this book is that it mostly an intuitive explanation behind the algorithms, without going into much depth. Therefore, the book works extremely well for algorithms that create very little assumptions or whose procedures are simple: Naive Bayes, k-Nearest Neighbors, K-Means, Shop Basket Analysis, etc. On the other hand, for more mathematically complicated procedures, like OLS Regression, Help Vector Machines, Neural Networks the explanation is not good because without some math it is really hard to perfectly convey what the model is e examples are well thought and I like how the book works the examples from questions previously posed in the chapter.On the code front, the accompanying code is easy and the book works with datasets that are almost complete, clean, and without entry mistakes (something that NEVER happens in true life). Nevertheless, I believe that the code provided can be translated to real-world situations (I have done it myself for Naive Bayes and K-means, at least.).Things that could improve the book:1 - A mathematical appendix that fleshes out some more complicated algorithms.2 - Exercises at the end of each chapter.3 - More examples.Overall, I think the book is beautiful amazing and enjoyable.
If you are looking for a theoretical rigorous ML book, then this one is not for you. I found this book useful when I quickly went through all the chapters in couples days. It is more about "how to use R to build primary ML models and the details/tricks that might be useful in practice".
Awesome book. I can't recommend it enough. Within a day or so, you can be running a Machine Learning algorithm on some data. Lantz provides examples of several various approaches in ML (KNN, SVM, RNN, etc). And then depending on your problem, you can begin playing around with the packages.
I use this book as a go-to manual that tutorials me step-by-step in implementing various machine learning techniques. It has a ton of ready code you can use in R. The author explains very well the logic of each technique and this is very helpful to decide what technique to use depending on the nature of the issue you explore. The book is non-mathematical but it includes references if you wish to dig into the math behind the algorithms which I do every now and then even though I come from the humanities (sociology) and business (MBA). I think it is a amazing book, simple to understand and teaches a lot of very practical skills. I hope the author updates regularly the book with fresh editions as the techniques and the algorithms evolve over time.
A amazing compilation of various machine learning algorithms , implemented and explained with R code that created this book a amazing acquisition for me, I thoroughly enjoyed it . If you're looking for an introduction to machine learning with R this book is amazing . However , the book really doesn't go into much depth on the theory or into more complicated examples and algorithms. Hopefully other books will cover. They should do an advanced ver of this.
Perfect introductory text for a comprehensive overview of statistics! The github repository augments the content very well and provides added value for the statistical subjects covered in the book. Both of the Bruce brothers are statistical gurus and this fact is evident in the writing, which is both informative and witty. Peter is the president of and is well-versed in providing statistical instruction to students of all ages and levels. He is also a proponent of resampling and one of the developers of the perfect Resampling Stats pack for is real that the textbook does not provide in-depth coverage for all topics, but I don't think that was the intent of the authors. However, the text DOES provide an perfect introduction to subjects relevant to students and data scientists. After reading the text and working through the examples, you will be equipped to further your knowledge in whichever subject you require for you data analysis task.Highly recommended!
Info seems plainly written and relevant. No link to datasets makes the "practical" code portion of the book unusable. Will happily modernize my review when the datasets are released.EDIT:Ok the datasets are up. There is a short R script to run to the data, it will require some little modifications to obtain it working need to make a folder named "data".and I changed the second line in the script from:PSDS_PATH
There's always that one person who is unsatisfied, but it sure as hell isn't me, because I knew what this book was going to be like the moment I saw how a lot of pages it was going to have & how the early release ver looked. I still preordered a hard copy (for sharing) & a digital copy (for carrying), because I knew this was kind of what type of book I was looking for & then e concepts are not astronomically explained, but with just enough depth that I can also individually explain to people what they are. What really stands out for me so far is after each or so concept, there is a section labeled as further reading (well, in the digital copy) that is usually at the end of the book altogether & I found myself realizing I have a lot of those books so the authors really know where to look & tutorial those who wanted more ah yeah yeah, the codes are missing (as of mid-June 2017) but if you really understood / know which packages to use, you wouldn't need the code. The first half of the book are two three liners of code concepts anyways; it's the explanations that matter the most. The second half of the book is the amazing part, which separates a white hat statistician from a grey hat data scientist, which is exactly what I wanted in a
I come from a computer engineering and machine learning background, but not so much on statistics so I decided to give this book a is very simple to read, although sometimes too easy. There are very few math equations in this book, which is amazing or poor depending on your taste. E.g., Gaussians, of course, are mentioned throughout the book but I do not recall seeing the equation for a Gaussian in any dimension in this book (maybe I missed it?). In fact, it's so simple to read that I finished the book before the github repo saw its initial commit.I'm not a large fan of their coverage on classification models, but I might be biased because evaluation of classification models is one my things. E.g., they discuss ROC curves in fair depth but don't mention DET plots, which, in my experience, have been well favored over ROC curves for a lot of years. Perhaps this was a decision based on book length, and again maybe I'm biased in this regard, ere are some careless typos in the book, e..g, the word "partiular" appears on page 51, "significiantly" appears on page 273, and there are related errors scattered throughout the book. I don't understand how the authors did not search these with a easy spell checker. Did they typeset this in notepad?Also, the github repo was pushed on June 17. This seems to be the largest complaint people have about this book so summary, a very simple yet worthwhile read for the price. A very high-level view of data science if you're unfamiliar with the stuff. For someone with a solid STEM background this is a light read which can be completed in about two or three weeks of spare time. For someone entirely fresh to the field, it is certainly accessible and worth exploring if you might be interested in the area.
I have an MS in statistics, and work in bioinformatics, so I already have a reasonable background in a lot of of the necessary concepts in statistics and data science; nonetheless this was an enjoyable and worthwhile read. This book takes the reader through a very wide range of concepts, at a nice depth - shallow enough that the book is quite readable, but deep enough to impart understanding and a new perspective on locations already familiar to me, with suggested further reading for each e 50 concepts are split across seven sections: 1) Exploratory Data Analysis; 2) Data and Sampling Distributions; 3) Statistical Experiments and Significance Testing; 4) Regression and Prediction; 5) Classification; 6) Statistical Machine Learning; and 7) Unsupervised Learning. I was surprised how well the contents of the book fit in with what aspects of statistics I actually require for work (as opposed to what my MS taught me) since it really does cover most locations I de included is generally quite short and succinct, aiding clarity and making the Github repository (scripts were committed in June 2017) largely unnecessary. Personally I like to type out code to test as I think I learn better that way, rather than copying and ere are a few topic areas, such as neural networks, that I am aware of but don't yet understand and would like to have been discussed, but it was nice to see a text that actually does cover 'modern' concepts like boosting and bagging as well as the more traditional; on the whole I was very satisfied with the selection of subjects covered. It is clear from their approach that the authors have insights from experience into which locations are practically useful. I particularly liked that the book contains a sizable discussion of diagnostics and has frequent illustration of making true inferences from data.
This book is well written and packs a substantial amount of info into a little number of pages. It is best used to obtain a survey and overview of a lot of of the facets of the domain of data science. This book will not teach you anything in enough depth to actually execute it well — it will teach you just enough to be risky and not realize when you've gone off the rails. I recommend it for managers who may never go into technical depth, for people considering whether or not they are interested in data science, or as a preview book to make a framework from which to hang more detailed understanding. Although this is an introductory book, it assumes you can already program in R. If you can't, either accept that you won't be able to follow the specifics of the examples, or read The Art of R Programming and/or R for Data Science.I dislike that the authors create a number of categorical statements of the form "Data Scientists do this" or "Data Scientists don't need that". I disagree with a lot of of these assertions and I think they have taken a definition of "data science" which is narrower than the prevailing consensus in the is book has some errors (see, for example, the confusion matrix on page 196) but overall the accuracy is above average relative to latest other reviewers have noted, the author's github repository for the book is currently empty. If that's necessary to you, check it under "andrewgbruce" on github and create sure it's been updated before you the book.
This book is superficial. For full disclosure I'm only on page 90 which is in chapter 3, but I don't think it will obtain sically it summarizes concepts at a very general level to the point it's not useful except to present you what you need to go another book to far it's style has been to give a couple paragraph overview of each concept, followed by giving us several references to check out in to learn more. If you intended to use any of the concepts you would definitely have to read about them in more detail elsewhere.If someone is talking about stats to you and you need to be familiar with the terms this books would be helpful, or it could be amazing if you wish a high level look at how stats concepts fit together before diving in to learn concepts at a usuable depth of knowledge. That could be beautiful it's defense, after more carefully reading the back cover I think this is what the book intended to be. it doesnt intend to teach stats or intend to present how to use stats in datascience. It seems to wish to be a glossary of stats concepts from a data science perspective. It definitely could have created more clearer.
This is the kind of book that every aspiring data scientist should have on their book shelf. It is also a amazing review book for data scientists with formal statistics ry clearly written and authors knew what they were writing that data scientist beginners should know. For example, inclusion of concepts like A/B Testing, Resampling e practical examples and R code makes it rich. github repo is available now. This book also includes the "Further Reading" sections which are very important. The book title says "... 50 essential concepts" but I clearly see lot more than that, which is and fun to read :-).I love this book and recommend it.
As an assigned textbook, most potential buyers will have small choice in whether to the book or not; the book is required. For instructors, this is likely the best -- only? -- book available appropriate for the subject. Based on the book content, the author is clearly very knowledgeable on the subject.Unfortunately, the author actually adds to the complexity of the material. In a lot of ways, the book reads like a military manual written by a committee whose priority was to contain input without regard for readability or comprehension/retention by the reader. Conflicting and arbitrary abstractions only add to the confusion of a topic already full of abstraction rst, there is a plethora of acronyms common to military texts. Some acronyms, like PHA, have multiple possible meanings (preliminary hazard analysis or process hazard analysis). Some acronyms are fully justified, like HCF for Hazard Causal Factor, while others seem more like the abbreviation abuse in a text notice among teenagers like ET (event tree), SC (safety critical), or CF (configuration management). JBAPIRDMIDAA (Just because a phrase is repeated doesn't mean it deserves an acronym). Likewise, while an abbreviation might be useful when placed in a flow chart or graphic like STP (software try plan), PDR (preliminary design review), or FT (fault tree) does not mean the author should use that in the text. When every sentence includes 2-3 acronyms explained once, 15 pages earlier, the text becomes unreadable. Reading a lot of sentences is like trying to translate from an unknown foreign language using a r example, on page 89 we search a typical sentence, "In some respects, the MM approach to developing an SMM is very much like a fault tree analysis (FTA) in visual modeling logical SFPs, except without the rigid methodology requirements of FTA." If you don't have your English-Acronym dictionary handy, that is mind mapping (MM), system mishap model (SMM), and SFP is not in the glossary. Ironically, just a few pages earlier, the author cautioned readers on how to write about hazards, "Do not abbreviate or assume readers understand program-special lingo and acronyms."In the author's defense, the field of hazard analysis depends largely on documents written by the US military. Most of the acronyms and abstraction models were likely developed by the military, and a complete study needs to contain the references, however cond, the author adds to the complexity. The author describes conceptual definition of a 'hazard' developed by Pat Clemens that breaks a hazard down into the source, mechanism, and outcome. Propane tank, leak, spark, explosion. The definition is easy and simple to understand. Of course, this has to be abbreviated S, M, and O. The author is unsatisfied with this easily understood and remembered breakdown. So, in the name of "increased descriptive comprehension" he adds numerous syllables without any discernible improvement in comprehension. Source becomes HS (hazard source) [given this is hazard analysis, what other source would we be referring to?]. Mechanism becomes IM (initiating mechanism) [ kind of like always requiring any reference to a machine be qualified with whether the machine was running/operating, or not]. Outcome becomes T/TO (Target/Threat Outcome) [ ...because we might be confused by non-target or non-threat outcomes?].Given the nature of the material, perhaps this is unduly harsh criticism. I doubt I could complete a related textbook. Even if I did, I doubt my work would be as complete or as useful as this book. As a student though, I search the writing style and presentation to be more of an obstacle than an assistance in learning the material.
There are only so a lot of ways that one can create a point, or express an idea without becoming pedantic. The author reaches method past that. The topic matter is necessary but could have been conveyed with a modicum of words. Throughout college, most of my engineering textbooks were from Wiley and well done. I was disappointed by this book.
This is a well written book covering some of the most necessary subjects in econometrics and quantitative inference in an antitrust and tournament law context. The book is technical of nature, as book on quantitative techniques should be, but the well written nature of the book makes you almost forget that this items is in fact advanced. The book also refers to some of the most seminal literature in industrial organization and antitrust economics, making it a amazing survey of some of the most necessary contributions in industrial organization.I have not a lot of amazing reasons to criticize this book. If I had to say something, it would be that as an antitrust practitioner I would have found the book even better if it had gone even further and systematically into the evidentiary value and probative force of the quantitative techniques in an antitrust legal context. Evidentiary value is touched upon in the book on several places, but not in a systematic way. Robustness and assessment of robustness and assumptions are for instance discussed on several places. This could have been systematically treated on one place. Other things I would have liked to read more on, are which model would provide more info when competing models are used?However, this private preference does not prevent me from saying that this is one of the best antitrust books I have read lately, and I can recommend it both to people interested in antitrust and in econometric inference as such. The book is well written and gives more practical understanding to essential econometric subjects such as causality, identification, endogenity, instrumental variables and so on.
A very handy reference tutorial on different analytic techniques, conveniently organized by categories (e.g. Assessment of Cause and Effect, Conflict Management). Each technique provides a brief overview of origin, when to use, potential pitfalls of the technique, etc. The cover, as others have noted, is a bit clumsy, but the spiral binding does permit flat opening of the book. Recommended for those working in product/marketing, project management, competitive intelligence and related roles.
This book has been a life safer for myself, and I highly recommend it to anyone working or seeking to work in the intelligence profession. The book helps to explain a multitude of complex and useful techniques that are not easily understood on a primary online course. This book is an outstanding read, with a lot of very helpful and applicable. A must have for anyone working in the intel field.
There is no question that the techniques contained inside this book are for, today’s analysts. While working a lot of various and complex requests, analysts are expected to deliver the most plausible and best supported hypothesis. This fresh and updated book by Heuer and Pherson, is not only the excellent tool for this process, it greatly refines and externalizes one’s work and helps to respond the intelligence question at hand. The book is very organized and simple to read, and with its flip through tabs, it makes researching for the right analytical technique(s), fast and simple. Amazing work!
The book is well written and provides a introduction to hazard theory and a structural approach to getting the most out of it by emphasizing the seven hazard analysis types (HAT) and the preferred hazard analysis technique to use for each. Still the book contains 15 extra hazard analysis techniques that are most often used in the field. A chapter of the text is devoted to each.
What a disappointment! Amazing content however the not good spelling and translated info takes away the credibility of the subject. Did anyone even proof read the Kindle edition? I would have given it a 1 star but I didn't "hate it" -- still has a lot of relevant points - just painful to obtain through them.
Predictive analytics is actually a gathering and analyzing of data to predict future happenings — are not as dramatic as an engine malfunction thousands of feet in the sky. And the idea is not fresh — predictive analytics is as old as the Info Age itself.But what is dramatic and very fresh is how industries across the board are embracing predictive analysis as a method to revise outdated business models. This ANALYTICS book from author Daniel Covington is the best primer you'll ever need. You will learn in here the steps required to perform predictive analysis and what techniques required to employ in to achieve sustainable success. It gives the reader a full picture of what it is all about. I highly recommend this to all. Very nice one!
Not too impressed. This is a book for something who has just heard the terms Huge Data and Predictive Analytics. Not for someone who knows what these e writing is not so professional, which leads to skimming and page skipping.
This book is excellent for those who wish to become their own boss and succeeding in their chosen profession. A few weeks ago, my uncle suggested me about this book and by reading this book I have learned about data analysis and predictive analytic for is book taught me about some easy and simple to understand terms. By reading this book I have understood about how to take advantage of data from my everyday operations. By the support of this book I have learned about how to conduct data analysis to enhance my business. This book is perfect and by reading this book I have understood about what techniques I need to employ for sustainable success.
Very detailed and descriptive examples - the only confusing part was the addition of the types vs techniques. This to me this just muddied the waters. Would be much easier to state that during this phase use this analysis (example: Design phase - PHL, PHA) and be done with it. Other than that, very amazing book, amazing reference to have at all rry LeMonsSystem Safety Engineer
I picked up a really old ver of this book from a library a lot of years back, and after some struggle googling it figured this was the one. I agree with other reviewers regarding the cover size and style - its not that amazing and is beautiful oversized. However, I had very low expectation from the reviews so it isnt bugging me as much as it probably would have. I was really looking forward to the spiral binding so its simple to flip the pages over. Its a bit offset by the overwhelming colors in the book. I'm going to guess a majority of readers are going to like that, but I guess I just like them old ntent wise, its beautiful related to what I recollect from the ancient version, so I am satisfied with that (so the 5 stars).
This is a very, very, very high-level overview of data and analytics, possibly intended for first-year students or anyone starting in the field. The description is misleading, and you will not learn much from this book other than definitions. It does not delve into any subset of analytics, and you can most likely search the same info online for free. It's unfortunate that I ordered a Kindle version, or I would have returned this item. It is totally useless to me.
I started a business two years ago but without any science on data gathering. Now I know how crucial it is to really obtain the data and analyze it. The process of Analytics may seem overwhelming at first; determining what data to prioritize, how to collect the data etc. But once you have core primary data required for the business, knowing how to analyze it is the next huge step to achieving your business is book is comprehensive yet easy enough for beginners. It helps one understand the importance of data mining and analysis but without overwhelming the reader with too much mathematical concepts. A amazing book to support anyone fresh in any business.
I'm a self-acknowledged O'Reilly fan -- I normally think they do a amazing job of publishing amazing items for nerds like me. But I'm really, really glad I didn't for this book. It's 82 pages of high-level discussion of things that might be relevant to your data project but probably aren'e subtitle is "Practical Techniques for Data Preparation" but that's a bald-faced lie. There's very small practical content in this book -- most of it is superficial and quite far from the day-to-day concerns that a data professional encounters. Most of what's in the book are things that are probably solved by default for you. "What kind of structure should I hold my data in?" Well, if you're using SQL, Excel, or any of the other bog-standard tools you're going to search on your work-issued computer, then surprise, the respond is reover, there's not nearly enough content to justify publishing this pamphlet. Things that I do a lot -- like combining multiple data sets -- got literally one page worth of discussion. Other things that I do a lot -- like wrestling with JSON versus CSV versus XLSX, etc -- aren't really touched on at all. This isn't even a mile wide and an inch deep; it's a foot wide and a millimeter deep.And the content that is there isn't always relevant. For unknown reasons, towards the end of the book, the authors devote 5 pages to explaining the differences between a data scientist and a data analyst, and what their roles are in the data ecosystem. Why? Couldn't tell you, because the distinction certainly isn't a "practical technique for data preparation."I do not recommend this book.
I'm confused why this was published as a book. And the of this book for 94 pages of not a lot of info that, quite frankly, seems kind of obvious. I think if you're trying to process huge amounts of data you could do some Internet searches and obtain much of what you need.
"Principles of Data Wrangling: Practical Techniques for Data Preparation" by Tye Rattenbury, Joseph M. Hellerstein, Jeffrey Heer, Sean Kandel, and Connor Carreras comes in at a very lean 82 pages. While it does have some interesting points, there isn't a lot of fresh info contained within. It is definitely meant for specialists who specialize in the field which also means that the expectations are higher. I think it is worth a look but perhaps at a discounted price.
I gave this book 4 stars as it serves the function of brining together a number of, perhaps not-so-earth shattering, ideas into a framework, and concise summary. The brevity of the work can be seen as advantageous.I think the word "techniques" in the subtitle is extremely misleading - if you set your expectations of the book focused on the word "Principles" you won't feel someone who has worked for the latest 20 years in 'Data and Analytics' this book is a amazing read for the influx of developers breaking into 'Data' given the current sexiness of "Big Data Science".I think the word "techniques" in the subtitle is extremely misleading - if you set your expectations of the book focused on the word "Principles" you won't feel mislead.
Brevity is the soul of wit, and doubly so the soul of amazing technical instruction. This is where this book flounders. This book feels like a powerpoint presentation gone inexplicably wrong. Its generic project management advice, with a thin veneer of "Data Wrangling" mixed in. It's a list of steps one should take in a data wrangling project without any true insight into what it takes to do them well. If you truly know nothing about Data Wrangling and have lacked all ambition and common sense up until this point, this may be the book for you.
It's not clear what audience the authors were aiming towards - principles of data wrangling sound like something that a data scientist or an analyst would do...but every single one of them will know the info in this book (and more than that)! It's not a amazing "primer" for a manager, as it's too in the weeds and so much fluff that it wouldn't give a manager enough information. Can't recommend.
It's hard to call yourself a statastician or data scientist without powerful data visualization skills. Tools like Tableau or Microsoft office can only take you so far. ggplot is an perfect option that allows you to create highly customizable graphs at the cost of a slightly increased learning curve. As a statistics graduate student I found this book to be an perfect introduction to this R pack and found the 2nd edition to be very up to date. It is by no means an exhaustive reference, but that makes the book readable and simple to follow. It provides just enough info to obtain you started and to point out major features and pitfalls, but there is no excess of words. I also appreciated the writer's familiarity with Tufte's philosophy. I highly recommend this book to anyone with at least a primary familiarity with R who looking to up their visualizations skills.
Fabulous textbook for learning ggplot2! Perfect examples and visuals and very simple to read. After just a couple chapters, you can be up and running with primary ggplot2 plots which are sooo much prettier than base R plots. And the rest helps you learn more advanced and customized ggplot2 plotting including info about faceting, scales, legends, themes, etc. I only want I had read this sooner. Thanks to Hadley Wickham for making ggplot2 and writing this book about how to use it!For whatever it's worth, my other current favorite textbook is An Introduction to Statistical Learning: with Applications in R. I highly recommend that, too, for anybody in the data / stat learning / R space!
Granted, all books of this nature are obsolete to some degree when published, but in this case, it is too much to accept. My version, just received, is the 2nd release of the 2nd edition, dated November 15th, ere is no mention of the strong datatable package, which, to me., is the most necessary latest development since purrr, dplyr, and of course, the tidyverse. R programing with datatable a quantum jump in efficiency. The datatable pack long predates the release of this r this reason alone, I would recommend waiting for the next update.
I have the first edition of this book, so was excited to keep the second edition. The second edition follows the tone and structure of the first edition, with updates throughout. I especially appreciate the explanation of what you are doing, versus just blindly following instructions or copying code (hello, tensorflow cookbook). If I wanted to just copy random code I'd go to stackoverflow. The point of a book like this is to provide a more in-depth explanation, and this certainly delivers. It's simple to overlook R in favor of the highly accessible python notebooks and the like, but R is such a strong statistical tool, once you learn it, you'll love it!!
I used this book when I was first learning Machine Learning and, years later, I still reference this book. It is well written, well organized, simple for a beginner to follow, with hands-on examples, and thorough enough to be valuable to advanced is book shows you how to use the different machine learning algorithms, and provides an intuitive discussion of how they work, but it does not go into the mathematical info required to program the algorithms from scratch. Thus, this book is excellent for the practitioner, but does not attempt to teach the theory or mathematics behind the algorithms.
Full of errors and sloppy book. Obviously the author did not even run some of the examples himself. I had to Google and correct the errors myself. After the first two chapters, I moved on to a various book. Save your and something else.
Im fairly fresh to R/data science myself so I can say I was reading this book as basically a beginner.I think it's beautiful simple to understand and does a amazing job of explaining structural functions and foundational concepts. It could be a amazing base for learning primary R functions and concepts but I don't think this book should be your only source. The data exploration section (among others), for example is very, very brief and a fast Google find led me to a lot of more useful functions and most programming books they're amazing resources, but having multiple resources is always better.I'd still recommend if you're a beginner though!Definitely test to used since these books can be so expensive.Hope this review helps!:)
I've been working with this book for a couple of months now -- lots of tabs and highlights. It is amazingly comprehensive for it's length but if you are beautiful fresh to ggplot, this is a challenging introduction in the following way: the examples begin at a beautiful complex level of aesthetic combos and they are a bit too clever. As an example, things like the geom 'label' uses a dataframe named 'label' to put a label of 'label' on a plot -- cute but not helpful to the reader. This happens too much for beginners. It's like someone who is perfect in algebra naming all the various variables in an equation "x" -- they can hold track easily in their head because they breathe algebra and it saves them the problem of thinking up a bunch of various variable names that they don't need for clarity. But it makes it hard going for a newbie trying to learn the pieces. In this method and others, a lot of portions of the book are like a second-level of ggplot use; it's easier to learn the basics on one of the www services or one of the other cookbooks, then come here for clever ways to combine lots of things into a couple of lines of code. Sometimes Wickham gives the impression of wanting to teach the material but can't avoid opportunities to present some cleverness -- that's amazing for experienced people looking for tricks but it makes this book initially a bit frustrating at points for some users (like me). I still use it a lot but it's not a amazing reference for true basics. This is in part because the indexed refs to geoms are to an initial intro of the term but if you wish to see how to use that geom beyond the single initial use, you need to read the whole book because the specifics are embedded in a lot of other examples. This is no doubt on purpose but, again, be aware it's not a very amazing reference-type book.
The R language provides everything you need to do statistical work, but its structure can be difficult to master. Each recipe addresses a specific issue and contains a discussion that explains the solution and provides insight into how it works. Make vectors, handle variables, and perform primary functions; Simplify data input and output; Tackle data structures such as matrices, lists, factors, and data frames; Work with probability, probability distributions, and random variables;
I was expecting kindle to be able to allow me 'scroll' through the pages, since it is electronic. However, it forces me to 'turn' pages, which makes following the code instructions very frustrating. I'm going to test to obtain my back, since I've already given up trying to use it.
Every section in each chapter in this book has exercises. About 1/4 of the book are exercises. But they do not have the respond keys where you can compare your respond to theirs.. beautiful stupid right? This extensively lowers the quality of the book, as the exercises are merely a waste of time.
A lot of of us are afraid of statistics as a field that is obscure; with a jargon that not only we cannot understand but it is seemingly boring and amazing only for sleepless nights.I was very surprised with this book; Polit not only created statistics understandable, but she uses plenty of examples to drive home the point. Even if your course does not require this specific book, obtain it anyway: it will create a globe of difference.
This was a needed textbook for a graduate level nursing statistics course. The text's chapters are organized and provide a comprehensive overview of the various statistical tests. All provided examples are pulled directly from nursing research and are understandable. I found the end of chapter exercises to be helpful but unfortunately, explanations or rational to the answers were not provided in the explanation. While this textbook contributed to my understanding of course content I didn't search it to be a particularly amazing "reference book" and I don't imagine I'll refer to it again now that the course is over.
This is one of the most user-friendly statistics books I have ever purchased. The info and concepts are simple to understand with multiple examples. The specific topic in each chapter is highlighted for simple locating. There are extra tables in the back that can be used to solve problems. I highly recommend this book.
This review is for the 2nd Edition of the book. ggplot2 has changed a lot in latest years and the old book is no longer useful.Hadley has rewritten the book on ggplot2 completely and utilized the examples and questions from the communities on StackOverflow and GoogleGroups as a guide. The book starts off gentle, but does assume you have primary knowledge of R (installation of packages, some base functions, loading libraries and easy syntax). The components of the grammar are brought in piecewise and in a logical method that should support early learners and refresh those of us who have used the pack for a while. There are dozens of code examples which are colourful coded for legibility and syntax reasons. Each block of code is followed by the output from that code, which helps the user understand what is expected. At the end of major sections, there are exercises which not only support you understand what you've learned, but also obtain you thinking about how you would analyze a related dataset. This is really necessary because if you do these exercises, you will be well prepared to implement the visualization tactics herein on any apters 9-11 introduce auxiliary packages in the tidyverse (formerly "Hadleyverse") including dplyr, tidyr, and broom, which are used to discuss an entire data analysis pipeline. This sections does a amazing job of introducing these tools and what you would use them for. If you're interested in digging further into these packages, Hadley has been writing another book called R for Data Science which will hopefully be on in late 2016. An early ver can be found here: [...]The latest chapter is about programming with ggplot2. Hadley introduces some very useful, more advanced methods for plotting with ggplot2 from creating your own functions to using standard evaluation. A very useful introduction for more advanced users.Overall, the book is a gentle and thorough introduction to the ggplot2 pack for beginners and a very useful references to all of the updates introduced in the latest few years since the latest ggplot book (Winston Chang's R Graphics Cookbook)R Graphics Cookbook.
This book is really useful and handy. It is very well written and simple to read. As the name stated, it provides very practical tutorials for those who don't have powerful background in Statistics but are dealing with longitudinal data. It is written in an example guided format. The outputs from the analysis and guidelines on how to interpret them step by step are included. There is no massive Statistical notation and you don't need to translate Statistics into English. At the end of the book, there are chapters of how to handle missing data and used in longitudinal data analysis. This book is probably too boring if you are a hardcore Statistician.
I have been in the development field for several years and I have worked with plenty of languages and seen plenty of books on the topic of development. O’Reilly books are usually among the best books available for a given subject, though sometimes the organization is lacking and the writing less than good. I have a couple of their “cookbook” books and they follow a consistent formula – show a issue to be solved, provide a solution to said problem, and then follow up with a discussion of the solution. I have seen some variations – try results, presentation of other alternative solutions, analysis of performance, etc. The R Cookbook follows the primary cookbook formula and presents a broad picture of the language in bite-sized chunks.I will state up front that I have not previously worked with the R language and this book is my very first exposure to it. I did not know what to expect and came at this with the experience of a seasoned developer learning a brand-new language. I will also state that I am writing this review for the book, not the is book covers a lot of ground. It presents the basics of the language, I/O, graphics, data structures, probability and statistics and a whole lot more. Where it gets interesting, though, is in the manner in which all of this material is presented. Starting from the very beginning, the authors show primary examples to solve problems, but in the they do so and the scope in which they cover the issue zone they’re actually teaching the language from end to end. There are advanced topics, such as time series analysis and all the subfunctions that fall under it, but these are presented later, after the basics of the language have been revealed. Even these examples are kept short and simple to understand. The language coverage is not exhaustive, but it is enough to give the reader a powerful e writing itself is very approachable. The authors did a amazing job of not talking down to the reader and not keeping the writing so lighthearted as to be silly. Instead, this book seems to be written on a high-school reading level and the reader should not feel talked down to or in over their head.I am still working through this book but it is an interesting approach to an even more interesting programming language. The cookbook format is used to amazing result in this book to do more than just solve a few issues but to teach a language well enough to be productive.
I read the Geron book "Hands-on Machine Learning with Scikit-learn & TensorFlow" before reading this book. (Note: I did not read the TensorFlow section). This book provides a better begin for several reasons. First, this book is better organized. Second, the code implementations rely primarily on Python modules, instead of custom garding the first, this book is set-up so that a reader can obtain an understanding of Machine Learning (ML) step-by-step from the bottom-up. For instance, supervised learning, feature engineering, and model evaluation all obtain separate chapters. The model evaluation chapter provides and entire section, as well as graphics, for understanding the roles of training, validation, and try data, which are probably the most necessary bedrock concepts in ML. In contrast to this, Geron throws you right into an entire ML pipeline in the second chapter. It's a mix of feature engineering, linear models, stochastic gradient descent, random forest models, cross-validation, grid search, and even object oriented programming for custom transformers! This might be useful for quickly understanding what ML is like in practice. If the remaining parts of Geron then went step-by-step and elaborated on the second chapter for the remainder of the book, it would be great. Instead, for instance, the second chapter is randomly about binary classification for picture data. You literally only obtain two paragraphs in the first chapter on cross-validation and validation sets, and a sentence or two later in the book. I had to go to Wikipedia to ensure that I understood it correctly and robustly. I want I had read this book garding the second, this book does not assume a massive programming background. Most of the ML pipeline is taught through the Python module Scikit-Learn. This is useful because the programming does not distract from learning fundamentals of ML. In contrast, in the second chapter of Geron, there is object oriented programming code involving concepts like constructors and inheritance. For this book, the most sophisticated chapter at the end, which is on pipelines and which expertly explains why feature engineering should be performed during model evaluation, doesn't even go into summary Geron teaches more advanced subjects interspersed with the basics without a coherent organizational structure. This book has an intuitive structure that elaborates at length on core ML concepts and doesn't overburden with complex programming. I might say read Geron after this one, but I'm not sure that is even correct. You're best bet is probably just using the internet (Towards Data Science; Kaggle kernels) to learn more sophisticated things after reading this book.
Unbelievable introduction to machine learning in Python. The examples are well written, and do a very nice job of introducing both the implementation and the concept for each model. I'm halfway thru the book, and am really enjoying it.I have a background in math and wrote professionally for a number of years, but haven't spent much time doing either for the past 5-10 years. This book is technical enough to hold me interested, and accessible enough to let me to ramp up on the language and the scikit framework.An added - the instructions actually allowed me to set up my development environment, and the code in the book actually runs!100% recommend for someone looking to obtain started in ML with Python.
This is a amazing book, and I'd say it is even amazing for those that are not familiar with python (you just obviously won't be able to run the code). For anyone with some primary understanding of linear algebra/statistics, the authors are able to show to you all the necessary (and sometimes subtle but significant) details, without the usage of equations, and more importantly, how they all relate to one l the concepts mentioned here are heavily backed with well thought of and well presented figures, in such a method that again I'd suggest you don't even need python to understand. If you do know python, loading the data sets and reproducing the figures is just a few lines of simple to understand code away (with the exception of the mglearn library contains which does some "plotting magic" for you. However, I believe each of them were appropriate. You can ignore them and create the plots in your own way, or just print the variables, it just may not look as publication friendly).Normally, I hesitate purchasing books that claim they may explain algorithms without the need of equations, and I expect them rather to be cook books of lightly and disjointly explained techniques (like an encyclopedia). However, I do not think such is real of this book. The power of scikit-learn is demonstrated and the algorithms behind them explained intuitively, and are referred as to how they fit together and complement each with any introductory read, a supplement is required from time to time and the authors' reference to Elements of Statistical Learning is a useful one (equation heavy). There are points in the book where the author defers to elements of statistical learning. I found these points suitable since further explanation would be out of scope.I read this book on my time while on vacation, and much of the time I didn't have access to a computer. The concepts were so well presented that it was just a nice leisurely read. When I finally had time to access a computer, I was able to test the techniques on my data sets with some browsing back and forth through the book again, but otherwise with small ly, since I myself am a researcher, I would recommend this book to any other researcher willing to begin delving into the globe of machine learning. Further reading will always be necessary, but this book will give you such a amazing intuitive understanding and overview of the topic matter that you'll know what to do to proceed next, and how to do it without running in circles. Even better, you'll likely already have applied it to your research!
This book 2016 ver of the 2009 book and is really good. Amazing examples! Updated to the current ver of R so all the examples and references are to functions and packages in the current R ver that I am using today... which makes it much easier to follow.Writing style is very clear. Examples of each concept along with review questions that really create you think about what has just been covered rather than just regurgitating facts.Overall style is concept, some details, and metimes I want Hadley would use more of an primitive breakdown approach to examples. For example one example starts with using loess to build some data for an example. I'd rather just see some plain data rather than a building some data from line fitting. That would create it easer to see how data flows through an example. I appreciate that what Hadley ends up with is true globe data, but I, and this may just be me, I like things explained at a more primitive level.But in any case this book is not just showing you some neat plots, even though it has many, it is giving you the fundamentals you need to be able to implement from scratch the plots you think up in your head to point out statistical features in your data.
I'm a middle level user of R, unfamiliar with the plotting system. This book serves two purposes. It gives a deep understanding of the architecture of ggplot2, so that you will understand it thoroughly. It also gives heaps of examples, so you flip through until you search the one you wish to replicate.
The discussion of sphericity alone is worth the of the book. I particularly appreciated throughout the book the use of computer output along with discussion of interpretation of the results. It really delivers on the promise of being an "applied" discussion. The one thing I want the author had included was the programs that produced the output, or at least had those available on a companion website
There is a whole sub-culture of meal porn, the lavish coffee table books that set out to educate you to an entire culture through its food. I once saw a library of several ese are page flippers – doesn’t that look marvelous, we’ll have to test it next time we search halibut cheeks. The sad fact, though, is that any of these books are lucky if the owner actually uses one or two of its recipes and exalted if a recipe becomes a dinner party e first edition of R Cookbook by Paul Teetor, published in 2011, was, in analogy, related in that it provided flavors with amazing recipes, a lot of of which proved useful for specific e second edition, just out with J.D. Long as co-author retraces and updates much of the content of the first edition, brings RStudio and tidy tools to bear, and subverts the dominant paradigm. You flip through it and it first appears to be the cookbook equivalent of granny’s index card recipe box. But it starts off with enough fresh that you stop flipping pages and begin took me two days to read the 554 pages. I had the support of several airplane hours, but it would have been a page turner anyway. What captivates is the rigorous R guide cleverly hidden in asides, call-out boxes and brief explanations of why a particular code chunk does one thing when you might reasonably expect my childhood, Donald Duck had three nephews, Huey, Dewey and Louie who got their uncle out of more than one jam by consulting The Junior Woodchuck Manual, which somehow contained the respond to any question that could possibly arise. The R Cookbook, 2ND Ed. comes close to that standard for the beginner to intermediate user who is completely innocent, on the one hand, or who has been covertly using functions without a well-founded understanding of their requirements and would be an unconventional choice, perhaps, but this is the book that I would choose for the text if I were teaching a course in introductory R. Why? Because this book tells you what R does, not what it is. Any student coming to it with even an imprecise notion of f(x) = y is going to be able to follow along to the point, perhaps, of wanting to write her own package. The text doesn’t cover this, but points you in the right direction, and suggests source(“good_stuff.R”) as an interim other words, its approach is like R’s, functional, not imperative, definitional not procedural. In my view, the implications in the R vs. Python battles are obvious. In one model, Professor Higgens tells Eliza bring me my slippers. In the other, he first has to declare a topic (the slippers), a possessive (my slippers), object (grammatical, me) and then, with the verb, provide detailed instructions on where they are to be found, how brought into possession and the means by which they are to be transported to their meone once said something to the result that assembly language uses humans as pre-processors. I’m afraid that is real of a lot of of its do-this, then do-that r now, their defense is the superiority of compiled programs in speed on the metal and the much more expensive wetware. And they are right for the one-off case. But as cases proliferate or become abstracted, wetware costs go up faster than throughput will not happen while I’m still sentient enough to follow, but a compiled R or an R to Haskell API does seem to provide the best of both worlds. Place the wetware where it belongs, in defining the issue and the appropriate solution and leave the optimization to languages that are amazing at implementing functional logic.
TLDR: To be successful with this book you need a primary understanding of statistics, and the willingness to work through issues solving methodically. In return you will obtain a solid understanding of R that is at an advanced beginner level.I teach an intro stats class, but to Honors students. So every semester I have a fairly huge handful who wish to go beyond the Excel and online sources we use in that beginning class to do more complex work. Sometimes this is just because they are interested, but often it is because they are working in labs and nobody knows how to do the stat. Excel does not chop it past a primary ANOVA so they come looking for more power (get it? Stats joke?) SPSS and SATS are just crazy expensive, and I see the writing on the wall that is moving toward begin source, like R so I test to encourage them in that direction. I do not teach R in the class and so I need a sources the students can mainly use to learn on their own with a small support during office hours. I have been loaning out R for Dummies by de Vries and Learning R by Cotton out, I have the earlier R cookbook by Teetor alone but that book seemed only comprehensible to my really hard core STEM students and was getting a small long in the tooth. So I jumped at reviewing this version.I am still going to stick with loaning out R for Dummies to my softer science and non science students with less math and programming experiences. It holds their hands a small more and walks through RSudio more slowly. And does not throw around terms like functions and PERL and matrix so casually. But this book is amazing for my STEM students with a small bit of determination and logic skills. I like the structure better than the previous book, it focuses more on actually understanding what you are doing which in the long term is more useful than a bunch of tricks. Working through this book will effect in a beautiful solid understanding of the basics in R and a bit past that as is book mostly assumes you understand the underlying statistics, which is fine, I doubt those who do not are going to be venturing into programming in R.