laptak2003/AmazonDataScience huggingface dataset:
I am a fan of the elegance and coherence of the Tidyverse. This book has proven to be an invaluable reference and recipe guide, especially when it comes to data wrangling. I'm an experienced developer and am very comfortable with R. However, I'm not always able to exercise the R muscles in my daily work. When I take on a new project, I like to quickly review the most pertinent sections of this book to re-sharpen my "R wrangling toolbox".
In my opinion, this is the best book written about using R for data analysis! Very easy to read and follow, even some bit entertaining.There is some typos and errors in the book, like the wrong graph in the wrong place or typos. But you can look at the online electronic version of the book, it is the most updated and corrected. And it's free! Also, the author provided answer keys for the exercises. Just google it, you will find it. It helps you to compare your answer to the author's and learn more!In summary, I love love love this book! I now know why Hadley Wickham is famous for a reason.
I wouldn't say this qualifies as a beginner 'R' book, although I'm not sure if it's even advertised as being one. I was recommended this book after taking a college course in which we utilized the software, so I could had good foundation that made it easier to jump into it without any problems. This book contains an impressive amount of useful material, capabilities that you weren't even aware that 'R' was capable of, especially as a beginner-intermediate user of R.
This book is intended as an introduction to the tidyverse package in R, and not as an introduction to the language or statistical analysis as a whole. For someone who wants to start learning R from scratch, would not recommend this book. Furthermore, the exercises contains questions with solutions that were never actually taught in the text. One has to go online to search for the answers to find out.
As a practicing R user, I am familiar with many of the things in this book.  That said, it puts my knowledge in much better context, helps me understand _why_ the tidyverse does things the way it does, and taught me multiple new tips and tricks.
This is a great book to reinforce all the basic R knowledge you need to create great data proyects. The book uses libraries available from the DSLABS and other CRAN sources and covers all fundamentals to be able to use R Studio proficiently.
I've been using R for at least 10 years, but I only recently began to explore the tidyverse. I bought this book as a guide to help me learn more about the tidyverse, which I did, but I also learned a lot more. Not only did I pick up some ggplot() tricks I didn't know, I learned even more about R notebooks and a workflow in R. If you use R seriously, you should read this book and keep a copy handy to refer to.
Great book. Hadley Wickham is arguably the most important single person in data science in the past 10+ years ... I would make that case ... and this book shows you how to use the dead-on tools he's built, with a singular focus on what's necessary and useful for data manipulation and visualization.
learning on internet is definitely easier and better, but if you need a reference book, this is as good as any
Nicely organized, color coded text, color figures, and a great tutorial on tidyverse/how to be introduced to R. Lots of solutions can also be found online in similar formats to hadleys site. Bought a copy for myself and will buy another for my lab when I leave.
I got through the Preface and about 60% of the first chapter and had to stopped.Before I get into the review to explain why I had to stop reading the book, it is important to note that this book is available online for free. I prefer print over screen, when possible. But if you donâ€™t have a preference, just use that.Why did I put this book down midway through Chapter 1?Cascade of events that started w/ me requiring solutions to practice problems that are in the book.The only way to learn math and software development is by doing. Books on these subjects should ALWAYS contain exercise problems and solutions to those problems, either at the end of the chapter or by way of an appendix at the end of the book.The best solutions that I found are at jrnold's github page. I quickly noticed, however, that the answers posted on that site didn't quite fit the exercises in the book. When comparing the online version to the printed version (book), I noticed that exercises from the book had been reworded or completely dropped. So from the beginning of this year, when this book was published and released for sale, to this summer, it is apparent that many errors had been found and revisions needed to be implemented.There were so many differences between the online version and the book that I decided to stop reading the book in lieu of the online version.My 5-star Rating:The author does an excellent job explaining topics. He is very knowledgeable and it shows. With the amount of revisions in such a short time, however, I can't help but think that this book was rushed.But if I am stopped reading the book b/c of errors, why 5 stars? The book, by itself, might have gotten a 1-star review from me, but I am still going to learn from this author. The online version costs him/someone to keep up-to-date. Purchasing the book is an easy (and very fair) way to support this project.
Wickham and Grolemund have produced an excellent book that would help a beginning R user become very efficient in explanatory analysis. Unsurprisingly the approach that they expound utilises the "hadleyverse" a collection of packages (ggplot2 for visualisation, tidyr for reshaping, dplyr for selecting and filtering, purrr for functional programming, broom for linear models etc) that dramatically speed up most of the common steps involved in an analysis. One benefit of Wickham's involvement in these packages has been a coherent philosophy that sits behind them. It can be a little tricky when learning this philosophy, but the long term benefits are enormous.The book is broken up into a number of sections that effectively builds up the ability to ingest, transform, visualise and model datasets. A good portion of the book is available in an online version, to give you a taste of how it is written. Many have been following it as it was written. I have passed on copies of the book to a number of colleagues who were just starting out and the response has been uniformly positive. In my own case I was familiar with some of the these packages; ggplot2, dplyr, tidyr, but found the book taught me purrr and how to better use the packages together.Probably my two biggest caveats to readers are that there are situations where packages from outside the "hadleyverse" maybe required. The authors do a great job of pointing this out, but it does pay in my experience to know data.table and lattice for example. Both because they can occasionally fit a problem better but also because you inevitably come across other people's code where these packages are used. The other caveat is that the modelling is a little rudimentary. Most of the examples are just fitting independent regression models, whereas it seems to me that a hierarchical model would be a better fit. Still these are small things and it would be silly to expect a single book to cover all of these areas.In short this is the book I would give to someone who was keen to learn about how to use R for data science. It reads really well building up the different components whilst still being a valuable reference if you just need a reminder of a particular package (what is the difference between tibbles and data frames again?). Even though a good portion of the book is available online, it is well worth it to have the full thing on your bookshelf (digital or otherwise). On a broader note with Max Kuhn (author of the excellent "Applied Predictive Modelling" with Kjell Johnson) joining Wickham and Grolemund at RStudio, it is a great time to start your R journey.
This is a solid book and I am glad I purchased it. That said, the book is not for the novice. I think it's most useful to people who have had an exposure to R or at least programming. I am a novice to the programming world (although with good experience in statistics using stats applications like SPSS and some basic syntax writing experience),  and I found that while certain parts of the books were helpful,  others moved very fast and completely over my head, without the sufficient detail or an explanation that I could dig my teeth into. The exercises were not terribly helpful at cementing the knowledge either: many are far more complex than the chapter itself (and no answers that I could find in the book--although I found the answers online).  However, I don't know that there really are any solid guides written for the novice R user trying to learn data science, so this may still be the best of the bunch. In addition to reading this book, expect to be taking online courses on R and watching YouTube videos when you are stuck on a specific question.
Really enjoyed this book. Full of  examples. Is a learning by doing book.High quality printing, full color code and graphs. The book stay open.
I am an architect that got into studying data analysis as kind of a weird mid-life crisis. After some Coursera classes and a few books, I am really starting to finally understand R.  But, this books and the Tidyverse set of packages is a game changer. So much more clear and intuitive. I highly recommend this book!  Buy it.
If you are going to be using R extensively, you'll want to dive into this book and get yourself set up to work efficiently and quickly in R.  It covers all the things you need to know about working in R and RStudio EXCEPT for the stats and analysis part.  You learn about getting data in, cleaning the data up (because who is ever given a clean dataset to start with?), exploring and organizing your data, a quick primer on ggplot2 (you'll want theÂ R Graphics CookbookÂ for the full story), how to work with dates, different variable types, and write your own functions.  RStudio is more than just a coding environment - it's got all sorts of features to get you from code to interactive web graphics (shiny), practically self-writing results (knitr), and even something akin to a live lab notebook (R notebooks).  This book walks you through setting those up, as well as touches on finer points of things like vectors, models, and automation.  It's written at a level understandable to laypeople with a bit of coding experience, but doesn't assume you are a pro.  Hadley does a fantastic job of pointing out potential stumbling blocks where some functions might give you unexpected results.  If you can stumble through doing your stats and making a few graphs in R, then this book is your next step to being a professional R user.
To anyone who may be unaware, all of the contents of the book are available for free online and are updated regularly. I purchased this book in October 2019 and it was already out of date compared the the version online. However, not enough out of date to deter me from using it.I prefer to read books in print versus online, which is the main reason I purchased this book. It also allows me to study while on a plane, which is very convenient. The book is great, easy to understand, and provides a lot of questions and examples to work through in each chapter.Iâ€™m halfway through the book now, and one complaint that I have is that the quality of the print leaves a bit to be desired. If you look at my photos, youâ€™ll see that I drew boxes around certain words/letters. I had to do this because the printed lines were SO faint that I completely missed it the first time I read it. This made the chapter more confusing than it should have been. I had to reread it a few times before I made out the super faint boxes. I compared it to the online version, which had the lines/boxes clearly drawn. I also wish the solutions to the practice questions and exercises were printed in the book so that I could check my answers. However, the answers are available online...it just adds a bit of unnecessary hassle. Those are really my only complaints so far.Overall, good book for beginner R and data science learners.
The main reason you would buy this book is to get introduced to the "tidy" way of doing data science in R - in particular, learning about the many packages the Tidyverse has to offer for data visualization, processing, string manipulation, modeling, etc. You could probably do equally as well doing DataCamp courses on similar topics, so it's up to you to decide what's best for your learning style.Caveat: Not for beginners. Wickham assumes more comfort jumping into R than most programming newbies will have. Ideal if you have some experience with any programming language (not a lot, maybe just an intro class' worth) and have seen a little bit of R already.
I like to mark up books that I use intensively, so it was worth it to me to buy the hard copy. I am thinkig of using it as a supplemental text in a data mining course. It covers all the manipulation steps before you run a DM algorithm. These strewps  are important, difficult, time consuming, and often tedious. There is only a little discussion of what or why to manipulate the data, but it has solid examples and discussion of HOW.  By the way, I only own 4 or 5 R books. This is good enough to be one of them. If it had come out a few years ago, I might have skipped one of my earlier purchases.
I am well-versed in multivariate statistics and found this text to be extremely helpful building on what I already know in R. I still have some statistical questions that I would like explained more, but I think that is for another book; this is to say, this book does not cover statistical analysis, but rather data mining, cleaning, and visualization. This is a great addition to a library and very very helpful for someone relatively new to R, but familiar with statistical programming in general. You will need more than this to work multi-level/multivariate statistics in R, but this is a useful start for a data scientist in any field.
If you want to get started in R and learn about cleaning, wrangling and visualizing the data, get this book. Recommended.
Well written and easy to follow. Thorough, though a bit rushed in the last section. Well worth reading and working through the examples and exercises.
This book made learning R very easy! It gives you step by step instructions and has images to guide you  along. I used it in part of my thesis, and it truly is amazing!
This book changed my work life! Accessible and well written.Unsolicited advice: Do all the exercises. Get involved in the R community.
A great introduction to the Tidyverse. Coming from SAS programming with only a little R experience this provided a good introduction to me to using R and the Tidyverse in a consistent manner that is focused on getting you programming and giving you useful tools.
It's free on the INTERNET, but I still enjoy the physical copy.  It's very accessible and wouldn't be a bad book for beginners who want to learn the language without the stats.
I love this book and is learning R systematically with the very-detailed guidance. I usually donâ€™t buy books, but this time I feel worthy. Strongly recommend to somebody who hopes to arm themselves with abilities and knowledge.
Book arrived in great condition. I learned so much R programming for data science. It's useful for self-studying rather than from some online courses; or even very good for learning in conjunction with taking courses.I highly recommend this book. The material is easy to understand, designed from basics to more advanced. Build up your skills with R pretty easily.
This the Bible of the Tidyverse in R. If you have been trying to pick up R but haven't found a proper foothold yet. This book is the best next step for you. Of note this book is also available for free online r4ds
Excellent book for beginners. The fundamentals provided here were good enough for me write quite a few tools at work. I have since ventured into machine learning and AI but I still find myself referencing this book every now and then. Highly recommended
Great book for someone with a basic working knowledge of R. Don't jump into it if you've never even opened the program before.
Great book to start to learn R! The first chapter focuses on visualization and you'll be able to write some code for scatterplits, bar charts and more within the first 20 minutes. I love the training excercises. Some are easy, some are hard and challenging.
Very useful and practical examples.  will remain a great reference book for many years
The best book out there for what is intended for.
This is a fantastic introduction to ggplot2 and the tidyverse.
Great product to begin learning more about R (I used Rstudio ide). Cleaning, preprocessing data, and machine learning for R, visualization of data. Great reference and resource to have on the shelf.
Good, very usefull
The final word on tidyverse. Hadley had me a tidy.
Fantastic, great intro and builds on each concept.
It is a very good book about ggplot2! Already recommended my several friends to buy it.  â€” A stat major grad studnet
A MUST. If you do any kind of work with R this book is great. Concise and to the task, from wrangling data to plots, every step is covered and is a good base for more advanced techniques
The content itself is interesting but I would ask for more explanation. And the book is full of mistakes with wrong graphs and I have to frequently go back to their website. That makes no sense of buying a physical copy.
Useful book to enhance my coding skills. Quick delivery.
The book will teach you how to do the analyst data.
Best book on the market for R programmers. Starts with the basics and methodically progresses to advanced topics, so beginners and advanced users can profit from using this book. I read it from cover to cover when I received it and still refer to it frequently.I am looking forward to their next book.
Incomplete information, Jumps around, and doesnt teach in a sequential manner.
I wish I had started reading this book earlier!! This helped organize lots of important concepts into one place. Easy to read and utilize in actual work.
Talks about not only coding but what a data scientist is responsible for in a team and how to workflow through the project. Also very technical about r and itâ€™s capabilities.
Perfect condition! Great book- easy to read, and well organized.
Very useful information
I am very happy with this book.  Its easy to read and the exercises/code work and are applicable.  The text is clearly written and the supporting graphics are well constructed.  I have started working through some of the exercises in R and find them very helpful.  I bought this book after completing a graduate course in data analysis using R.  Much of the course was constructed around Hadley Wickham's work.  We used ggplot2 and tidyverse extensively.  We did not use this book in the course and I now wish we had used it as a desk reference.  This book is answering many of the questions I had.  It is also providing an overview of many basic concepts and tools in R and would have been helpful to have at my fingertips during my graduate course.  Google is a great resource, but a well written and easily sourced desktop resource like this is indispensable.  I am learning something new about R constantly...I would consider myself a late beginner to early intermediate user of R for data science/data analysis.  I often find books that cover the basics extremely useful, because its easy to forget.  So for me, this was a good buy - regardless of my skill level.  Others will have to make their own decision.  This book does not cover mapping in R.  In fact, there is a sentence in Chapter 1 that states - this book does not cover mapping.  I would like to have had more on that topic or a follow-up book committed to mapping and integration with other tools like Leaflet or ArcGIS.
muy completo y claro
would have liked a more basic approach to learning R. Book jumps right into concepts that a novice user would still find a bit overwhelming.
if you want to start working on R programming, then this book is the way
If I can only choose one data science book to read, then I will choose R for data science. It teach your the elegant way to do data science.
good resource
Is to follow. Very well written.
Nothing to say but its great!!
Great book for data visualization.
If youâ€™re curious what data science is and isnâ€™t, this book is perfect. It gives information in a little bit of everything. It also has a LOT of great references to come back to if you need data sets or specific program needs. This book is more of an overview which Iâ€™d highly recommend if youâ€™re just beginning your data science journey. It isnâ€™t a walkthrough of the in depth programming. Itâ€™s more of a intro to data science class which is still very involved.
The first half of the book was great (overall theory and computer program types), then it got into the math and I was lost. I was really hoping for easier explanations.
this is a very good introduction to data science
Good book, with a ton of content. Great intro into AI.
Good book for starters. Very high level and gives some real life scenarios.
This is the jumping off point for me to provide context for my studies in data science
Great book with a lot of information.
Excellent introductory book to Data Science! It provides an overview of the subject, without deviating from the initial training of technical skills. He helped me get started on Data Science.
Nice book to know about data science!! Recommend to basic learner! easy to follow! Thank author for making me understand easily.
It is an easy book dealing with interesting issue.
Awesome book. Worth buying, even though probably most of it is online free (with permission of authors).
Really good book and well written. I'm already familiar with R programming so always great to learn more and more !
Awesome book with everything needed to code correctly and efficiently in R.
Great book. I use it often.
low printing quality. All pages are like this.
Makes R easier to use. I am, overall, a fan of Hadley Wickham's work, and this doesn't disappoint.
Useful book for especially for data visualization.  They give you a good trunk of knowledge in the ggplot2, tidyverse, and modelr packages.  Would recommend for anyone starting out.  Learning how to manipulate data is a huge and often underrated part of machine learning, is useful and will be useful until the end of time or until data takes on a different form.  Learning how to set up a basic system for modeling is invaluable, and will not likely ever be outdated, just updated.  The book does much better at teaching data visualization than modeling.  Not limited to ggplot2, tidyverse, and modelr but certainly heavy in them.
It is a must-read for all who work or are interesting in data science and statistics
Fantastic text for the latest tools in R.
for those that want to learn R this is a good book. Very detailed.
...to understand conversations about data science, "big data" and the tools associated with them.For the truly brave, it also offers plenty of free and open source tools and data sources to get you started.My education is not in a STEM discipline, but for many years I have worked in areas where the power to reach  smart conclusions about important matters arose directly from the quality of the databases that categorized past events in order to characterize current ones (and perhaps identify trends for the future). Even with a liberal arts background, comfort with the tools used to manipulate complex, multivariate data was essential to professional competence...and confidence. To prove my "not for the timid" headline, I refer you to page 261 in Chapter 17 ("Doing Data Science with Excel and Knime), where you can find this actual statement: "Creating pivot tables is easy..."  I consider myself a strong Excel user, but also consider that effective pivot table creation and use is something of a dark art, and it takes some skill  to use effectively.This is not meant as a criticism,  but as pointing out that the standard in place for this  book might be a little higher than the typical "for Dummies" title.This book contains  both basic and in-depth discussions of big data concepts viewed from both mathematical and statistical models. Some of the programming discussions exceed my own likelihood to explore them.  On the other hand, Lillian Pierson offers excellent open source resources for working with your data, and excellent descriptions of uses for data science and large data sets in a variety of disciplines.I work in an organization where big data and data science  are some of our crown jewels. At minimum, access to the contents of this book have left me better equipped to converse more confidently with practitioners of these trades.
This review is based on a copy I obtained from an associate. I wavered on whether or not to do four or five stars given the issue of how deep should the author have gone in the topics. If it is for novice data science learners, too much depth may put them off and they will get tied up in the minutiae.  For more seasoned investigators, they may want more detail.  That is always a judgment call. This book does a fine job of presenting important topics for those just starting out in research or those who want to up their game in data knowledge. It will also work for veterans who need a brush up on some concepts. Lillian has produced a fine effort which lays a solid foundation. I am sure the work will be updated in the future when new data approaches become available.
This book starts off with an overview of Data Science and takes you down the road of probability and statistical modeling. Having some background in R, SQL, Python is helpful but one can still read and understand the computing concepts easily. The use cases around e-commerce, journalism, environment and crime provides a good eye-opener. Overall a solid book for anyone looking to understand Data Science concepts.
This book is as good as it gets. It is a very good overview providing the right amount of depth, introducing the reader to numerous technologies and programs as well as the whole data-science/big-data ecosystem. Most importantly, it is not boring at all, whereas many books in this field are. The author provides a very high number of useful references to other sources and websites that broaden the reader's horizon. I can't imagine a better introduction to data science than this book. I think further editions should be made regular...
This book just felt like a bunch of white noise. Any insight it provided into the world of data science could honestly be Googled and provide nearly the same, if not more detail. I would recommended learning what the common practices are for a data scientist and taking a deep dive into those subjects. This book is just not worth it.
Awesome book! Really easy to understand. Would definitely recommend this for beginners or for someone trying to brush up on data science.
Probably full of useful information, but I couldnâ€™t get through it. The voice for the audio book is very monotone and I couldnâ€™t focus on it.
I love this book. Itâ€™s very helpful in my data science class. It helps me understand the information much better. It gives big data info and itâ€™s bert easy to understand.
Sorts out the high level topics and allows understanding to decide if you want to jump into this field.Package was open on one end when delivered; contents could have easily fallen out. Looks like the end of the padded envelope was intentionally torn open and someone decided the 'nerd' books were a disappointment . . .
The moment I start to read the book, my eyes catchÂ  the author writing which already mentioned that this book will not go too deep about data science as you guys also can see from the book title, itâ€™s for dummies aka someone new. So please do not expect too much deep knowledge here. Not many authors will mention about it in the first place and honest about it.Okay, about the book, I can tell that the author thought a lot when writing it as he/she highlighted some important part that can help dummy people to easily understand the main point that he is trying to explain. The length of explanation is also not too much and not boring. Lots of point parts rather than just all paragraphs. Some important parts are being labeled with an icon remember. I personally think it's a very useful and good idea. And overall, personally, for me. Itâ€™s a good book. Even someone dummy like me gain something good from this book. Thank you Authors.
I perused this in order to make a review and because I wanted to know if this would be useful for my cousin who is thinking of entering this career. There are several introductory chapters, entitled "wrapping your head around data science" and "exploring data engineering principles". Next few chapters appear to be an explanation of how data science can be applied to real world examples. Next few chapters seem to be the meat and potatoes, entitled "math, probability, statistical modeling", "using clustering to subdivide data". It goes on and on and there are 23 chapters. Without knowing much of the industry I'd say you would come out of this book with a good education on the subject. There are great visuals and examples throughout. The price on amazon is about 1/3rd off the list price at the time of this review
It's an excellent beginner book for data science. It tackles a lot on the fundamentals as well as theories in practice. Comprehensive as well and I was able to absorb the information like a sponge. I guess the only thing I found lacking was that it didn't have much practical application such as how to program in R or Python. However, I would still recommend this book for those who would like to have a solid foundation on data science since and it's a great starter book overall.
Broad scope and current.  This is the book for anyone looking to see the range of available tools (from machine learning to d3.js) for data science.  She is better than most authors at the intro stage of every topic -- for example, why do I care about reducing dimensionality? How to predict groupings within a dataset?  One reviewer said that this book does not get much into implementation of the concepts.  That is true, but then how could you in less than 400 pages?  Entire books are written on just one topic, such as time series prediction models.  The key for beginners and intermediate data science practitioners is to fully understand options before spending weeks or months going down a rabbit hole (wrong model).  I can say this with some regret because I wasted a lot of my own time in a steel demand prediction project a few years back.  Had I been better acquainted with options, I probably would have completed it in half the time.  Unless you are already a guru, this book is well worth the money.
I bought the Kindle version of this book a few days ago when it had been discounted. While I had read some books on data science before and have a somewhat solid understanding of statistics, I thought that this book might be a refreshing departure of the more dry texts based on the overall positive reviews.Sadly, this wasn't so. Admittedly, I only made it through the first 25% of the book before I couldn't get myself to continue. I found myself spending more time marking up mistakes in the book than actually reading. Even the simplest of concepts like singular value decomposition (SVD) is full of errors; matrices are being called vectors, the order of matrix multiplication is displayed incorrectly, etc.Everybody makes mistakes, and this wouldn't be such a deal breaker if the rest of the book up to here contained useful information. To put it in terms of data science, if you had to perform data reduction on Part I of the book, the eigenvalue would be zero, yet this would be a good predictor for the next 5%. There's absolutely nothing in the first part that would be worth keeping if I was the technical editor. A bunch of pointless dribble to fill the pages of what was touted in the abstract as"Some books on data science are needlessly wordy, with their authors going in circles trying to get to the point. Not so here."Pierson, Lillian. Data Science For Dummies (p. 1). Wiley. Kindle Edition.Oh, the irony. The author surely fooled me. Take for example the following 'explanation'"Spark SQL: You use this module to work with and query structured data using Spark. Within Spark, you can query data using Sparkâ€™s built-in SQL package: SparkSQL."Pierson, Lillian. Data Science For Dummies (p. 58). Wiley. Kindle Edition.The book is full of these kinds of circular zero-content statements. Despite only having paid about a lunch's worth of money, I will kindly as Amazon to issue a refund. This book should not be for sale before it has been edited by someone.If you are interested in data science, have a look atÂ Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, for example. I recently also checked outÂ Naked Statistics: Stripping the Dread from the DataÂ from our local library, but haven't started reading it, yet.
In order to learn data science you need to practice it, like anything else,  either in R or in Python, or some type of software package. As the other reviewer noted, this book (for the most part) exposes you to the landscape of data science terms and concepts, but it does not show you how to implement them. Had the book showed you how to actually do clustering, regression, component analysis, etc. in R or Python, it would have been a far better and practical book. In other words, if you wanted to learn how to play piano you would buy a book to teach you how to read music. This book is like a student of piano buying a CD of piano music and just listening to the music with no instruction on how to actually "press the keys" of the piano.
I am a complete novice when it comes to data science.  Most of the material left me more or less confused.  The worst part of the book was the author's discussion on Python.  As someone who is a Python expert she didn't do justice to her chapter on that subject as far as I am concerned.  Lots of definitions and then the snippets of coding examples were too confusing.  What would have worked better is to give a introduction to Python and then work through simple examples that were fully annotated.
This book is a geat start for anyone who is curious to know about data, In today's world every aspect of the business and its future relies on the historical data and this is a great book which covers-1. Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value2. Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL3. Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things4. Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate
This is a good read for people who are alreadyeducated in general STEM topics, and looking to discover the overalllandscape of data science and big data. It's an awesome handbook forwrapping your head around the whole data space, but it's not a place to goif you are looking for details on how to implement.
I really enjoyed working through this book. It is definitely mathematical and algorithmic in its treatment of the topics covered: Statistical "Learning", Monte Carlo Methods, Unsupervised "Learning", Regression Models, Regularization and Kernel Methods, Classification, Decision Trees and Ensemble Methods, Deep "Learning" ( Neural Networks )  As a statistician and data scientist, I find the convoluted "machine learning" terminology very affected and as helpful as trying to design a plane based on the flying dynamics of a bird.  Sometimes the rigourous mathematical notation is difficult to follow on the initial reading.  Most algorithms are implemented in Python but the code should be more clearly documented so that one can follow the implementation of the solution without getting stuck on coding issues as the book encourages the reader to focus on the algorithm and to not treat the python code as a black box.  The list of references is quite complete and it was interesting to check my library to see just how many of the references I already had.  If this book is to be used for training analysts then there should be more practical examples and code solutions available
I'm very early on in the text, but the text is impressive in the breadth and depth of its coverage, along with its attention to the mathematical theory.  The exercises are challenging, which makes the text a little tricky for self-study -- at least to the extent your self-study is enhanced by knowing if you got the problems right or wrong.  If I ever make it through the whole text, and change my opinion, I'll come back and edit this review; but I think just getting through Chapter 2 will be a semester's worth of knowledge.
A reasonable survey of core statistical methods, not super-clear, plus a slapdash review of a few machine-learning models, with very little explanation.Pros: * Decent review of core concepts * Good coverage of importance of distinguishing between sample and population statistics * Better discussion of bootstrapping than I've seen anywhere else * Good ideas on dealing with non-normal data and avoiding the assumption that all data is normally distributedCons * Assumes that you know R. Lots of code, no explanations of the code. * Inconsistent level of detail and depth. Detailed coverage of mean, range, quartile, but rampant hand-waving when you get to bagging and boosting * Many of the math explanations are unclear or incomplete. The authors make you do a lot of work to figure things out and you will need external resources * The last part of the book is a thin and purely practical survey of ML models. You don't get much understanding of how or why things work.
First of all, this book is not for you if you want a deep and thorough explanation of statistical concepts. It serves a completely different purpose: to familiarize a reader with high-level concepts; to enable them to continue their statistics education elsewhere.I found this book a very engaging read: it sets itself apart from other books on statistics in clearly telling which concepts are not-so-relevant for the modern computerized explorative analysis toolset. Many concepts that are presented in classic books on the subjects are rooted in 20s and 30s where computing power wasn't available and researches resorted to various pre-calculated distributions and formulas to do their work. A modern data-scientist's approach would eschew some of the old ways and instead rely on randomization, resampling and computing power.This book not only tells what something is, but also why it is that way and if a concept is still relevant today.I can recommend this book if your statistics knowledge is spotty or ephemeral, it serves its purpose well and doesn't bog down the reader with (sometimes) unnecessary mathematical concepts to demonstrate an idea.Why the four stars:1. Lack of examples in programming languages.2. Complete lack of exercises (at least 1-2 exercises are necessary).3. All scarce examples that are available are in R. No Python. :(
I am still trudging through this book (at page 200), and I will try write a more comprehensive review when I finish.However, with that said...The concepts discussed in this book are surface level at best. You end up learning more from Google as you try to grasp a better understanding of what concept is being talked about. An intuitive understanding will not be learned as math examples are replaced by steps and R scripts. Also, one big caveat to the R in this book:**** The R scripts in this book are not complete compared to the R scripts that you get from the GitHub page! ***- You will end up debugging a lot of the scripts to make the examples work.- Not all of the data you get from the Github page matches what you see in the graphs of the book. It ranges from small errors in percentage points, to entire directions being the opposite.- Good luck when you get to Chapter 5. Some of the code is incomplete or contains a typo. (See image)The only positive I can comment are the resources cited, though I'd avoid purchasing anything by the Bruces at this point.Save yourself the $18.
Excellent introductory text for a comprehensive overview of statistics!  The github repository augments the content very well and provides added value for the statistical topics 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 Statistics.com 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 excellent Resampling Stats software package for Excel.It is true 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 excellent introduction to topics 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 topic you require for you data analysis task.Highly recommended!
I love this book as a reference.  Clear, efficient but detailed explanations.  It is not designed as a textbook but as a reference.  When I wonder "what is that test used for again?" or "what was that formula?" this is the first thing I reach for.  Sure, Google has become universal for that too, but I like having a single hard copy reference that I can get to know and that becomes a trustworthy old friend.  This book is taking on that role for me.
This book is well written and packs a substantial amount of information into a small number of pages.  It is best used to get a survey and overview of many of the facets of the domain of data science.  This book will not teach you anything in enough depth to actually execute it well â€” it will teach you just enough to be dangerous 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 create 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 make a number of categorical statements of the form "Data Scientists do this" or "Data Scientists don't need that".  I disagree with many of these assertions and I think they have taken a definition of "data science" which is narrower than the prevailing consensus in the industry.This book has some errors (see, for example, the confusion matrix on page 196) but overall the accuracy is above average relative to recent norms.As other reviewers have noted, the author's github repository for the book is currently empty.  If that's important to you, check it under "andrewgbruce" on github and make sure it's been updated before you buy the book.
I come from a computer engineering and machine learning background, but not so much on statistics so I decided to give this book a read.It is very easy to read, although sometimes too easy. There are very few math equations in this book, which is good or bad 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 easy to read that I finished the book before the github repo saw its initial commit.I'm not a huge 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 many years. Perhaps this was a decision based on book length, and again maybe I'm biased in this regard, anyway.There are some careless typos in the book, e..g, the word "partiular" appears on page 51, "significiantly" appears on page 273, and there are similar errors scattered throughout the book. I don't understand how the authors did not find these with a simple spell checker. Did they typeset this in notepad?Also, the github repo was pushed on June 17. This seems to be the biggest complaint people have about this book so far.In summary, a very easy 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 new to the field, it is certainly accessible and worth exploring if you might be interested in the area.
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 many pages it was going to have & how the early release version 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 some.The 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 & guide those who wanted more depth.Yeah 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 good part, which separates a white hat statistician from a grey hat data scientist, which is exactly what I wanted in a <300 page book.Thanks for keeping me waiting since November though, thought it would never come! The O`Reilly books always keep me in awe at how they always know what topic I want to have a brief book (probably data collecting on me :P) & simultaneously leave me in suspense because I never notice I am preordering the books! Sigh. My only request is to be able to preorder the Kindle editions rather than the physical editions; my data science book cubby is starting to overwhelm my statistics cubby (NOT FOR LONG MASTERS PROGRAM ~).
Took me awhile to actually get into this book. Overall I like it, it covers a lot of topics, but not in great detail. Some topics I would like a better explanation, but the book does suggest further readings for each topic. My biggest problem is that some of the code isn't explained in enough detail, they will leave work there a real world example, show the code and not explain what each aspect of the outcome means or how to interpret the outcome. I do feel like my domain knowledge of data science and the available method has grown, and I think this book is a good starting point (although I may be a bit biased, because I have computational background).
This text explains statistical concepts, starting with basic ones like exploratory statistics and moving on to more advanced concepts like clustering.  Each section explains the concept clearly and concisely with the mathematical formula, then a programming example.  While this text recommends familiarity with R and uses R for the examples, I had no trouble following it without using R.
This book will present the spark notes of what's important, but it won't really give you the fully explanation into the concepts. For me, this means I wouldn't really understand these concepts without going deeper elsewhere.
I pull this off the shelf often whenever I need a quick mental refresher of some basic stats that I've haven't touched in awhile.  Awesome book that's a quick read and easy to understand.
Well written, and serves as a great refresher for those in research, mathematics, AI, machine learning, data analysis, etc..