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I mean, it's statistics and data, so it is hard to obtain really excited about it. I have a decent background knowledge in stats, but I still found some of the verbiage hard to follow. A more clearly defined vocabulary would help... but that's a issue in ever stat book I've ever seen. I really did love that it used real-world healthcare scenarios to teach. That created the concepts more accessible to me. I passed with a amazing score, so it's not impossible, just be very careful about vocabulary when answering the questions.
This book provides an perfect depth for most users, and manages to cover the material without getting too bogged though it would be hard work for a beginner to either spatial data or epidemiology, I doubt they would be looking for such a book. It does however cover the subject range from the entry level up to the more complex topics, so makes an perfect text for graduate and postgraduate work.
This is the kind of amazing book that gives an overview of applications in spatial statistics. It obviously lacks - since it's not a math/stats book - theorems, proofs, and demonstrations, but it does have essential ideas and explores the topic in a very friendly language. If you are in social sciences, don't loose it. If you are in stats, some spatial statistics book (Cressie's, for example), and read them apter 2 has been written very carefully, and gives the reader necessary ideas about risk, rate estimation, and the various kinds of data effect from various experimental designs. If you bought 'that' spatial stats book, don't bother about chapter apter 4 gives (again) a very careful description, in this case about graphics and spatial data presentation. I'd say it does the om chapter 5 on, you'll need 'the other book' in to understand what really is at could be a small less expensive...
This book is incredably well-written. Although I had not read a statistics text in 12 years, this book balanced theoretical development, explanation, and examples in such a method that it was actually simple to read. The organization of the book makes looking up a particular subject very easy. The reference list is extensive. The best thing about the book is the set of graphics and maps to illustrate the mathematics, modeling, and outcomes. This book is a must-have for anyone involved in spatial statistics, disease clustering, epidemiology.
The book explains all the subjects in reasonable detail and also info the implementation in Python. But, I suggest that only beginners without a primary knowledge of statistics to this book.
The book is perfect as extra reading on statistics learning and data science foundations. It provides an alternative look at statistics : univariate, bivariate and multivariate analysis, data distributions, sampling methods, statistical experiments (testing hypothesis, chi-square, ANOVA, ...) and statistical learning models.Anyone with exposure to Machine learning without explicit exposure to statistical machine learning may search a significant portion of content in the latest few chapters as familiar.I feel this is one of the must-have for an Aspiring/Working Data Scientist since it can work as both a reference and an interview prep. Thanks to AI Publishing for making learning easier for beginners and e concepts are well explained with examples and applications, but with just enough depth that I can also individually explain to people what they are.What stands out for me so far is there is a section labeled as sources & references that are at the end of the book giving a lot of books, online courses, and others learning kinds of stuff. I think AI Publishing knows where to look and tutorial those who wanted more depth.
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.
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!:)
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!!
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.
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.
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;
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
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.
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.
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!
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.
This is a very cool and clever “cookbook” that outlines how to use a tool that is and also super helpful for me when it comes to data analysis that I use for forecasting and budgeting. I had no idea this existed but I’m so thankful there is a book that breaks it down in easy-to-learn chunks not unlike a meal cookbook. A serious is that the is accessible to all. I’ve got a LOT more to learn but I am very impressed and excited about what I’ve added to my repertoire thus far!
I have long thought that the most effective teaching way is to use a "present a problem" approach and allow the reader's curiosity drive the is book uses this 's enjoyable and has helped me understand the basics of data analysis quickly.
Various issues need various methods to be solved properly. This book takes different examples and lets the reader work through the problems. It is actually fun to read this book. Very well explained. Of course, not all the issues worked 100%, but I have not read a book with examples and issues that all work. Especially, some of my R did not work too well. Other than that it is a amazing book, and a amazing method to learn about data analysis.
I got the book promptly. It has that softbound textbook feel. but amazing binding not or ready to fall apart. the intormation in it so far seems interesting and well organized. its in the "head first format" which means there is a lot of nice visual lay out and side notes and some graphics to create understanding the concepts by seeing them when possible. I like that format. It is still beautiful clean and gets to the point. but I have only read and used so much of it at this point so I cannot go much farther into the content than that. -- in short I think it is a solid book to obtain if one wants to better understand how to interpret social science numbers, or other scientific numbers that they are shown in a method that they are wise to different ways that data can be spiked and spiced. how in depth I cannot comment on as I have not fully digested the book. But it is a book that is designed to be both read and used as a topical reference. And it has the "Head First" style keeping things clean but providing insightful commentary, context and graphical illustrations where it might really speed up or enhance understanding of a particular idea or complicated example. it also uses bolding in locations where you can attention to the fresh vocabulary you might wish to learn in to lay the ground work for even more technical education in data analysis. it even has a chapter where it goes over some of the more obscure plug ins for excel that are there for helping a person analyze data. I would basically treat this book as a nice survey of both the human technical sides of data analysis. it also covers things like data collection or effective data presentation, and as I said it refers to several readily available tools like excel for example and how they can be used by someone who wanted to know how to leverage their computer in tame and extract meaning from data they have been given to interpret. -- I think that its a useful primer that is like a survey course in the topic sans the professor. But how amazing each section is I cannot comment on as I have only started with the book for a several weeks. but what I did read I found completely intelligible and because I am not a total novice at looking at Data, there were times I could use its nice formatting to skip past explanations I did not need because I already was familiar with them. If I fall in love with the book I may come back and say so and create my stars 5 instead of a 4 but at this point I would highly recommend this book for anyone who wanted a nice primer that went into to a very serviceable level of detail for a primer or survey type info source.
...and will always for the rest of my career. Simply laid out and explained. Allows for fast and accurate data analysis helping at least my managers understand their dilemmas and their options. Amazing.
This was a attractive book that really refueled my interest for Statistics (which I've been struggling to begin learning...even though I know calculus and LOVE mathematics)...but it really caught my eye because it goes into detail about the R statistical programming e first few chapters obtain you going on a specific mindset of how to interpret data, which is VERY necessary to hold throughout the entire reading of this ter that groundwork is established, you are taken on a really cool journey of some Excel features (don't freak out...those of you who don't know excel proficiently will be fine in the hands of this book) that you never would've believed were there! You can even use Google Docs to do the same things if you don't have a valid copy of Excel!Finally, R comes into play with all its glory...I would've loved for a deeper dive with this technology, but there are several other books out there in which you can obtain down and dirty with R ( and are my favorites and I own them both on my kindle).I hope that eliminates all your FUD's (Fears, Uncertainties, and Doubts)...go and grab this book RIGHT NOW! You'll be blown away with what you'll be able to do after you read everything here!P.S. It only takes about a week and a half to obtain through it going at a nice, slow, and comfortable pace...if you're HUNGRY like I was, you can knock it out in about 4 days.
Head First continues to be my series of choice on any subject.I have to agree that sometimes its too basic, but you still have the option of quickly skimming through till you reach subjects needing attention.
This book provides an excellent, approachable introduction to data analysis. Although most experienced specialists or advanced students will search this text trivial, it serves as a god starting point for those who are completely fresh to data analysis. The text provides numerous interactive examples using Excel and R, but the examples do not cover these tools in any amazing depth. If you're looking to learn more about statistics, data analysis and data mining, this book is a amazing starting point.
This book does a thorough job covering the concept of data analysis, touching on both the side (requirements gathering, mental models) and the technical side (Excel, R). Like other "Head First" titles, it does it in an entertaining manner that makes reading the book a joy. The material is presented more like an enlightening conversation with an smart teacher than a brain dump of facts and theories.
I strongly recommend this book for your business based library. The detailed examples and simple to understand language really helped me and it created statistics, a difficult topic to comprehend and conquer, so simple for me. If you wish to start making ultimate business decisions relying on figures and numbers, this complete beginner's tutorial to statistical science will bring amazing advantage to you.
Having read a couple of introductory books to statistics, it was refreshing to search a book for beginners that is simple and clear to follow without the author trying too hard to create statistics so-called “fun” & “entertaining”. For me, the historical context added to this book was very insightful and for the most part, the author explains how the different concepts tie in with one another. The further resources section could be more extensive – it seemed more like an afterthought – but all in all an perfect introduction to inferential statistics.
I emphatically suggest this book for your business based library. The point by point models and straightforward language truly helped me and it created measurements, a troublesome topic to grasp and vanquish, so natural for me. On the off possibility that you need to begin settling on extreme business choices depending on figures and numbers, this total apprentice's manual for factual science will carry extraordinary preferred position to you.
This book is an eye-opener as to some of the ways our info is taken from us and being used. Some of the ways featured in this book I had absolutely no clue that was happening. I mean we have all noticed that if we look something up on Google, that following that Fb starts to use ads targeting something we are already interested in. This is a amazing book to read if you wish to know how and why your info is taken and used.
Amazing one, being in a globe where technology rules, we don't realize what is going on in the background. I mean sure you have conspiracy theorists that believe Alexa works for the CIA and such. But in reality everything we do is being monitored in some method or function. This book is an eye-opener as to some of the ways our info is taken from us and being used. Some of the ways featured in this book I had absolutely no clue that was happening. I mean we have all noticed that if we look something up on Google, that following that Fb starts to use ads targeting something we are already interested in. This is a amazing book to read if you wish to know how and why your info is taken and used. Thankful! very nice one !
Literally one of the worst textbooks I've ever attempted to read. It has virtually no detail just generalizes everything. On top of that, the homework in each section is nicely broken into 4 questions per question. So, when assigned 50 questions you end up actually doing over 200. I felt like I was slamming my head versus a wall while trying to use this book to learn statistics. If I could give a - star review I would.
This book does a not good job explaining the content. Some concepts are presented with pages of examples and others obtain a one-paragraph explanation & then moves on. I spent so much time on the internet trying to inderstand what the book was saying (or not saying) that I ended up buying a second book to support me understand the concepts I was trying to learn with this book. The chapters are stuffed full of practice questions, but it does not explain the answers for the practice questions. If you obtain a question wrong, you are left on your own to figure out why & how they arrived at the correct answer. The tone of the book is rather condescending.
Does not break issues down step by step adequately. Often presumes you remember "which end is up." (You won't, if your class moves as quick as mine just did.) Pages in the loose-leaf edition tear right out, which is not good. Suggest reinforcers for famous pages - you'll need them to obtain through the chapter reviews. Also suggest page flags and tequila.
This is a amazing book on dynamic models for survival data. It provides the right mix of theory, intuition, examples, and code to use for related analyses. The R pack timereg (developed by the authors) can be used to duplicate their examples and for your own research. The book goes much further than the standard texts on time-to-event data. For example, Additive hazards models (Chapter 5), multiplicative-additive hazards models (Chapter 7), Competing risk model (Chapter 10, beautifully written), and Marked point process models (Chapter 11) are discussed in amazing generality (time-varying coefficients and covariates, ...), and illustrated with very nice examples. Hopefully the next edition will have a small bit more on how to interpret the results, for example the cumulative regression functions in Section 5.8, and also address multistate models and recurrent events. A must have book, right next to Therneau & Grambsch!
The purpose of this book was supposed to serve very broad groups of people: students, statisticians and engineers. Unfortunately, I found this book not quite suitable in engineering practice. From practical point of view, when dealing with reliability estimations, one has to connect mathematical theory with real-life data. It appears that to accomplish this task it is important to understand some primary statistical ideas, plus specifics of the topic under consideration. Sometimes common sense knowledge can come in handy. Strangely enough but a lot of fundamental principles are in fact surprisingly simple, elegant and thus beautiful. What is missing in the book is the lack of clear explanations of fundamental statistical concepts that certainly can be presented in a complicated form but in reality they are not. On the other side, the book could serve as a solid textbook to students, statisticians and mathematicians.
This book is amazing iff:1) You have fun doing math by hand on paperOr2) You travel back in time to a point where computers don't existOr3) You have lots of time to reinvent the wheelOr4) You plow a field with an ox and feel the old ways are the bestOr5) You're Amish or a LudditeFor the rest of us, don't bother. This book wold have been something unique 40 years ago. Now, it's as outdated as the chapter on linearizing functions (so you can plot them on log paper.)Many of the questions in this book remind me of conversations I have with my grand mother about the "old days". I think my grandmother is the target audience.
This book might be of use to someone who's interested in all of the math behind reliability estimation, but it's of small practical use to anyone who doesn't have a year to study it. It's full of small gems like: "This [equation] can be used to parametrically adjust the nonparametric estimate of probability plot shown in...". You obtain equation after equation with no explicit method to obtain from the abstract math to the plots and conclusions you really t for anyone who needs a quick, practical tutorial to reliability analysis.
Reliability and survival analysis both with time to failure data. Much of the methodology is essentially the same. The term reliability is generally used to apply to hardware or whereas survival analysis is a term for biological systems such as animals or humans. This book contains the standard nonparametric and parametric methods for estimating reliability functions and parameters. It contains system reliability and repairable systems and with latest developments with repairable systems including Nelson's mean cumulative function. A couple of years ago I asked Wayne Nelson if and when he might revise his famous text "Applied Life Data Analysis". He said he did not plan to do it because Meeker and Escobar had just finished a work that would be as amazing as any revision he might wish to produce. Other subjects contain failure time regression models including the famous Cox proportional hazards model and accelerated life try models. It also contains modern subjects such as bootstrap confidence intervals (both semi-parametric and nonparametric) for reliability parameters. The book is comprehensive and up-to-date. It also contains discussion of Bayesian methods. Some case studies are also included. The only subjects it misses are reliability growth and warranty and service contracts. These subjects are covered in the latest book by Blischke and Murthy "Reliability Modeling, Prediciton, and Optimization" also published by John Wiley and Sons, merical examples are done using the SPlus from MathSoft. An ftp website is available to data sets to use with SPlus.
The info is fine and well explained, but my copy was missing several pages of text. Nearly every second page in chapters 2 and 3 were blank--and should not have been. For a 30.00+ book, this is absolutely unacceptable. Do not this book.
Data and evidence are parts of education's reality, whether you like it or not. This book is simple to process and an enjoyable read whilst giving you the nuts and bolts of data analysis and inference.
Simply a listing of skills necessary, not a how to book I was expecting from the title.
It is known from this book. I truly trust there is a greater amount of such sort of book out there!good just on the off possibility that you need to utilize the fundamental e author discussed here step by step that very helpful.
Lacking in specifics. Author throws lists of breezy generalizations at the page. Doesn't live up to the title. Proofreading very sloppy.
This book is very practical and helpful. It includes the python pseudo code for a lot of primary Data Analysis From Scratch With Python, which was exactly what I was looking for. It would be helpful to have more info of what libraries commands come from. Recommend it .
If you are looking into true dive into data science skip this book. You can google most of the information.
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I was expecting this book to have more info on becoming a data scientist. More on how to pull from SQL, and place into a language, or how to clean up a data set. Upon reading, I search that its more about buzz words for newcomers of the Computer Science field. Unless you just wish a dictionary to support explain the terms simply, I would not recommend the buy.
Neural networks and algorithms are described in an simple method with program examples, I'm learning python and working with neural networks, its a helpful resource for students.
A very general and useful overview about data science. But not much more beyond this level. I think it is more like a collection of commonly used terms.
I'm running a little business on my own and have to say that analysing data is super important. I overlooked that aspect in my early career and had a lot catching up to do. From taxes, to shop research data analytics is very important. This book includes valuable info of how to do it, how to analyse your data. I won't obtain into fancy terms, like predictive or regression analytics. It is much easier to read this book and understand that yourself. I have to warn you though, that it's not an simple to learn book. Data analysis is not an simple thing to learn, but very beneficial if you do.