laptak2003/AmazonDataScience huggingface dataset:
As a graduate student in econometrics, I found that this book provided a comprehensive introduction to the Bayesian analysis, from the very basic fundamentals to recent development in non-parametric analysis. It takes advantage in both scope and depth over many books about Bayesian analysis written to economists.  As a result, I recommen this book to economists who need to do substantial Bayesian analysis in their research.
What can you say when a classic like this is updated? The original was THE reference on the topic and this one expands on it and adds all kinds of little things they've thought about over the last 15+ years.They've added chapters on Basis Function models, Gaussian Process models, Finite Mixture models, and Dirichlet Process models, and also lots of important but small concepts that we've previosly seen only in places like Andrew's blog, including things like boundary-avoiding priors. The coding example Appendix C has also been reworked to use Stan rather than BUGS.The physical layout of the book has been improved as well. It's the same thickness, but slightly larger in the other two dimensions and with a smaller bottom margin, which I think gives a much better amount of information per page. The only thing I could ask for layout-wise is to have chapter/section numbers at the top of each page to make it quicker to find something.
Great book full of examples coded with R. Bayesian statistic is hard, this book makes it a little easier and understandable
Carefully written and complete coverage of topics.
This is my go to book for looking up information I need on Bayesian data analysis. Everything is covered, and is quite easy to follow for the most part. There are great references for going into more detail where needed.
We used it as textbook.Very well written, combines theory and model setup.Worth the price. Very useful to me
It's not possible to read pages side-by-side on Kindle for PC.  "Appendix A, Table A.1" is laid out across left and right pages in the 2nd edition.  The 3rd (Kindle) edition can be viewed only page by page (affects new Table A.1 entries for Lognormal, LKJ correlation, Logistic, Log-logistic distributions).  BTW, old people like me need to read digital text on large screens -- or wear reading glasses.  I'm probably going to return my Kindle version of the 3rd edition for the hardcover (it's that good!).  I didn't download the Kindle sample, but I doubt that the sample would have revealed the side-by-side page reading problem unless "Appendix A, Table A.1" was part of the sample.
Statisticians must read this book. It is excellent and as authoritative as any I know of on Bayesian methods.
While ordering this book for school, my first qualm was that it comes from an international seller. I was concerned that this edition wouldn't be up to proper standards or that I would receive a book in an entirely different language. Yet, nonetheless, I received a pristine clear copy, English and all.Buying this copy was an absolute steal in comparison to all other sellers.
My impression from people around me was that this book is the canonical textbook for those who want to get into Bayesian statistics. After having read this book from cover to cover, however, I do not think it is a good idea to start learning Bayesian statistics with this book, as it covers very wide range of topics and therefore does not get into much technical depth for most of them. I think this book is ideal for someone like me who has very basic understanding of Bayesian statistics but would like to get some exposure to variety of existing tools in the literature so that when some of them become needed at certain point of my career, I can reopen this book and follow its references to learn enough to actually use them.The stance of this book is very practical, and it is great to get a glimpse of how these grand-master Statisticians approach data analysis. First few chapters regarding the underlying philosophy of Bayesian statistics is also brilliantly written, a must-read for any Statistician.I was somewhat disappointed with changes in the third edition though. The addition of Gaussian Processes and other advanced topics is greatly advertised, but I found these new chapters to be relatively poorly written compared to those in the previous edition; notations are not consistent with previous chapters, and clarity of writing is disappeared. It was a stupid idea to buy the new edition while having the second edition.
Very technical, not good for a beginner in my opinion. I give it 3/5 as a beginner who found it difficult to follow. Probably better suited to an expert?
I will talk about 2 things: how this was shipped and then about the book itself. This book was packed in one of those self seal gold bubble mailers but whoever packed it got the seal on the book itself. I have tried rubbing alcohol and lots of other things to get that seal off to no avail. So I definitely have a complaint on the shipping. As for the book itself, I am very happy it includes sections on nonparametric models. I needed to learn about mixture models and Dirichlet Process Mixtures and this book covers that as well as give lots of examples. Word of warning tho: this book is NOT for beginners. You need to have already gone thru some more basic stats books before reading this one.
Tough read, but rewarding. Gelman dives deep, but if you hang on, you will find some really good insights. I would suggest a solid understand of Bayesian Statistics, and a proclivity reading statistics papers as good background.
I have a decent econometrics background but I still found much of the book very difficult to understand.  People smarter than I might get more out of it.  Nonetheless it does cover a lot of material and can still provide good guidance even if one doesn't fully grasp the underlying theory.
Bayesian Data Analysis is written in a textbook format with problems at the end of each section.Many important concepts are embedded in the problems.  An instructor solution manual would be useful for a better understanding of these concepts.
Excellent book. Right from the start it explains everything with good examples from authors' research in a very clear and understandable way. Good list of exercises at the end of each chapter (some are easy, some are hard) that really helps anyone using it for self study. Content has some minor overlap with another of Gelman's book, but that was fine with me.The book has a lot of good content and assumes previous knowledge on basic probability and statistics.Definitely recommended as a starter, refresher, self-study guide, textbook or even reference for anyone interested in bayesian modelling.
I was told to buy this book as a supporting text. It is very good for that.
One of the best books about Bayesian Data Analysis. I read 4 of them.
BDA3 is a must have for every applied statistician.There is a good balance between theory and examples with good some surprise (gaussian processes).
All very well, I received the books without problems and very son too
Very good textbook to help understand Bayesian Stats and its applications. I highly recommend this book to anyone interested in applied research.
It is a good comprehensive of bayesian data analysis. The book is too verbose to follow. It is better to use with other textbooks in Bayesian
A+.  Great experience.
The text in this book seems overtly redundant/irrelevant; many details (words, sentences, even paragraphs) can be dropped without affecting the content. Future references and historical notes are tangled with the examples that deliver the major concepts, while some equations are left underived. This make each examples that are meant to convey simple concept into a gigantic mess that one has to work through. This is a text book, not a paper, what's the point of enforcing accuracy in details and suppress derivation of equations. As a reader who wish to learn instead of to judge the content, such writing style only induces unnecessary headache. Might as well read the papers.This is at least the impression I get from the introduction and the first chapter. The process was already slow and tedious, and in the end there were only a handful of points that are noteworthy. Seeing that the style doesn't seem to barge for the rest of the chapters, even ones that describe the algorithms, I don't think I will regret dropping the book.
It seems that I am in the minority among reviewers in that this is my first experience with any iteration of this text. I will also admit that I am in the unusual demographic of applied statistics hobbyists. After reading the reviews of others, I realized that if an understanding of the Bayesian approach is to be pursued, this is the text to have.I don't mind saying that I was intimidated by how quickly the text ramps up from introduction to application. I suffered no shortage of having to reference outside material to catch my bearings, but this is due to my own unorthodox path to this text. It is probably only due to the repeated use of real-world examples and a down-to-earth writing style that I am able to keep up at all.Realizing how little I know, and how much I am able to glean from this text - which should be way beyond my comprehension - I can confidently say that this text is about as accessible as it gets without watering down the subject. I can't even imagine what a boon this text must be to those who have a broader educational foundation to build upon!
I grew up in the frequentist world, learning frequentist ways, using my p-values like a diligent little statistician. A few years ago, I was introduced to...the other. Mostly through more basic books, talking broadly about Bayesian theory and its place in the world. Picked up an older version of this book and sat down with some far more educated people to really get it. I get it.This is THE definitive book on Bayesian analysis. It is not for the faint of heart, or the light of math. If you're looking for an introductory topic and you don't have deep mathematical skills and knowledge, this isn't for you. If you want to dive in deeply to this at a graduate level, then this is what you should buy.Many examples worked through, multiple models, and plenty of symbols to confuse the uninitiated.
This book provides a short one chapter introduction to the use of Bayesian Analysis suitable for someone who has completed elementary statistics in an undergraduate program.  The body of the book which is well illustrated by examples requires a significant background in statistics, calculus and linear algebra.  Graphics and application examples are provided for these topics.One physical criticism of the book (which may be limited to my copy and a given batch), is the light grey type making up much of the book.  One would expect a high level of legibility in an expensive reference work.
As other reviewers have mentioned, this isn't a cover to cover read, but a summary of a reference/text book. This book is nicely chunked into 5 sections: Fundamentals of Bayesian Inference, Fundamentals of Bayesian Data Analysis, Advanced Computation, Regression Models, Nonlinear and Nonparametric Models.  I have performed some very basic Bayesian Analysis in the past and found the introduction of Bayesian theory to be very clear. Each chapter (23 in total) has a nice set of questions at the end (3-15 generally) that cover both the theoretical (derivations) to the practical (data set analysis). The best part of the book was its reliance on looking at actual data. One of the first examples looks at football point spreads in a very nice way.The problems encourage computation and program writing to gain a fundamental understanding of the methods discussed. This, along with the math requirements, makes the book suited for the graduate student and/or practitioner. The figures are clean and beautiful and the typesetting is also superior to many books now on the market.
This is the Bible of Bayesian analysis. High level book. An excellent book.
The book is the 3rd edition of the authority on Bayesian Data Analysis. The authors have designed the book to be useful to all levels of users - that is, all level of students of the subject.The book starts with basic principles, describes current approaches to Bayesian modeling, computational statistics and related fields. It also highlights Bayesian methods in Applied statistics. What keeps this book interesting is the author's focus on practice rather than philosophy. The forte of the book is in its inclusion of worked examples from real life applications.The 3rd edition adds 4 new chapters to the 2nd edition - Basis Functions, Gaussian Process Models, Finite Mixture Models and Dirichlet Process Models.The most impressive feature of the text is that despite its magnitude, it is beneficial to all level of users - from students starting off in statistical analysis, to graduate students looking to leverage effective approaches to a reference guide for statisticians in enterprise.Highly Recommended!
I recently started programming in R and wanted to get to the theory of the Data Analysis and supplement my knowledge of programming.It starts with basics of Bayesian Data analysis and moves on to more complex analysis.  Few times, I found myself lost in the theory and I had to go back and google and brush up on even further fundamentals.Overall, I took a lot of time to study this book and questioned my long forgotten fundamentals in theory.  It was definitely a journey worth it and I can now proudly claim that I supplemented my data analysis theory other than just programming knowledge.
I read the 3rd edition and I don't see it as much different from the 2nd edition, so my rating stays the same; this book is one of the most overrated books in the field of statistics.
The authors of this book are pioneers in the field and they have produced an excellent book that is a must-have for anyone who wants to know about Bayesian statistics. The 3rd edition has two new authors and quite a bit of new material. Finally, the price is very reasonable compared to other textbooks, making this book a great value for the money.
This has been updated, and is unquestionably a standard in the field. If you do work on probability, you should have a copy of this on your bookshelf. If nothing else, you can use it to kill small animals. But you'll probably want to use it for something more intellectual. I admittedly didn't sit down and read the whole thing, since it's a reference book, not a work of fiction. But my casual perusal suggests that it is broad, deep, and very, very well written.Representing some of the most up-to-date methodologies in statistics (i.e., three hundred years old), Bayesian analysis is an important tool in the kit of any statistician. I know this to be true, despite the fact that I view Bayesian statistics with a deep, deep distrust. And I'm not alone. From Wikipedia: "Bayesian probability is the name given to several related interpretations of probability, which have in common the notion of probability as something like a partial belief, rather than a frequency." I'm a devout Frequentist, but mostly because I think that assumptions probably shouldn't go into the conclusion that you're testing to determine whether the assumption is correct. That kind of seems logical to me. And to turn it around and do it the other way feels likeâ€¦wellâ€¦the opposite of logic. Choose your term, illogic, magic, nonsense. They all seem to apply, even if Bayesian methodologies are quite popular and well-accepted.But I guess, if you're going to do magic, you might as well get a really good grimoire. And this would be that. So enjoy!
This book continues the very high standard set by the first and second edition. With expanded coverage of weakly informative priors and nonlinear and nonparametric methods (as well as a strong appendix covering the authors' "stan" package for probabilistic modeling), this belongs on the shelf of even owners of one of the previous editions. Just great, definitive, even. An excellent book for both academics and practitioners.
When I saw the advertisement for this book, I noticed that it said "R programs".Since I'm not a mathematician but an end-user, this appealed to me.Then I got the book.This book is more the academic version of Bayesian Data Analysis, with very little on R.Namely, it's not what I expected.The text (at least the chapters that I read) were good, but again, not for an end user.When will mathematicians learn that not every math book is just for mathematicians?
My son requested this book for birthday. He was very pleased.
This is one of the best reference textbooks on Bayesian statistics on the market.
Leading text in Bayesian statistics.
Good book but a very complicated read
This book is awesome. Has some exercises in the end of each chapter. The text is very good and easy to understand even if you have only basic statistics knowledge. If you have none, however, it should be a bit difficult to read it.
Great expansion of a classic text.  Too much to use to teach for a single course, but has many courses in it.
I like this book, it is well written and the problems are challenging
Excellent coverage and examples by someone who is clearly the leader in this literature
Provides a great overview of Dask with the right mix of theory, syntax and use cases. Explanations in both technical and lay terms make it easy to understand and level up in Dask, Python and data science in general.
+1 for concise and to the point.+1 for relevant topics.-1 for lack of real life library usage.-1 for lack of real world data.-1 NaN^ 2/5 stars.I don't normally leave reviews. I buy books, read them, and put them away. I decided to make an exception for this one.Before I go any further I'll give the benefit of the doubt that perhaps I had a misunderstanding of what this book was about. I am a data scientist who works in cyber security. I have been through a good amount of data science boot camps, books, and online training. They all have one thing in common. For being about "data" they all have pretty useless and unrealistic examples. This book is no exception. (I laughed at a part where the author literally says something similar to "real life data is usually very messy" and continues to not use real data).I will start out with the good parts about this book. It gives you a good crash course into Python and pretty much every critical data science concept. It is concise and filled with code examples written from scratch with little to no libraries being used (which is also a bad thing, I will explain shortly). The flow of the book is well designed as well.Now for the bad.1. Pure Python is great, but should be kept at a minimum. Sure, it gives you a good understanding of how to implement a concept in pure Python, but that is not the industry standard whatsoever. There should have been a healthy amount of real world implementations to offset the typical college classroom feel to the book. It also tends to add too much filler to the content. It would have been much better if it was "read resource X to see how to implement this in pure Python" and not "read book X to see how to use a real world library to do this."2. The data in this book is like all the data everyone uses in their examples. Completely useless. Randomly generated numbers, endless usage of the "coin flip" probability examples, typical artificial data that, I promise you, nobody analyzes on a daily basis. The book starts off with you role playing as a new data scientist for a fictional social networking platform for Data Scientists. Which was very promising start and I was eager to see how this "character" would deal with the data problems they would face... Spoiler, barely ever spoke about it. Most examples are riddled with typical Statistic 101 and randomly generated data. Yet again, another disappointment on that end.Maybe I misunderstood what this book was about. I could be wrong. That being said, I am now afraid to touch another "Data Science" book or online resource because I am sufficiently tired of reading about 300 ways to solve a problem with np.random generated arrays then turning to my screen with real world data and literally looking like the Persian Room Cat Guardian.I am not sure if I recommend this or not. It is a good book in the sense that you learn about what Data Science "contains" but definitely not how it applies to the real world.
In my view, too many people want to be data scientist and use advance statistical techniques that they don't fully understand. I believe Grus closes this gap with this fun introductory book to some of the basic techniques in data analysis. If you don't come from a statistical background (MS/PhD), then you should start with this book and move on to other texts such as Pattern Recognition and Neural Networks by Brian D. Ripley for a thorough review of classification methods, or the more popular and modern The Elements of Statistical Learning By Hastie, Tibshirani and Friedman.
Err:509
I thoroughly enjoyed this book, one of my favorite books ever on programming. It does three things superbly: covers the basic low level tools of a data scientist (the "from scratch" part), gives a great overview of useful Python programming examples for those new to Python, and gives an amazingly succinct yet high level overview of the mathematics and statistics required for data science.At first I was very worried about this book based on the first few chapters for the one reason that the author was cracking jokes throughout the text and I thought if it kept up for the rest of the book I was going to be very upset. But it did not happen and it turns out to have been a very reasonable way to ease into this complicated subject.The author steps through the toolbox of the data scientist, chapter by chapter, giving useful, insightful, clear pieces of code and textual explanations of each topic. So, for those new to data science it gives just enough to get the basic idea of a concept in terms of code and mathematical explanation, and then moves on to the next topic.It is often said that in writing, less is better and this book gets things down to their essence. That is one of the great things about the book - that the length of each chapter is about 20 pages (over 25 chapters). So each chapter can be read and the code even exercised in about an hour. Further, the references at the end of each chapter invite the reader to expanded information at the level of one or more entire textbooks or references. Thus the book can be seen as kind of boiling down a 25-volume set of highly technical subject matter into roughly 300 pages.The topics that were explored the best seem to be the ones on probability, working with data, regression, clustering, and databases (SQL). Some of the small but dense code samples were tough to follow but that is based on their algorithmic complexity - such as that for logistical regression and MapReduce. Occasionally the author uses a term that is not defined or in the index (such as data munging - which I still haven't looked up to see what it means). There are only a small number of typos which indicates good editing. While the Python crash course was pretty good, Python is a vast language and there could have been more to that section.I read this book from cover to cover and stepped through logically all the code (but did not actually run any of it) and I would wholeheartedly recommend this book for anyone wanting to work in the area of data science or its related fields, such as big data engineering or data analysis.
Started programming Python immediately with this book. Lots of concepts for one book. I like that that there are enough details to write a functional program for each chapter and not too many details. It's important to find a proper material to start, and this book, in my opinion, does it perfectly.
Minus one star for using outdated Python 2.7. Essentially ALL data science tools you are likely to run across have been updated to Python 3.4+. I would have knocked off two stars but this book is actually quite good and delivers on its title.This is a very basic book on Data Science but it gives a broad overview which helps you get a perspective on the tools that are available. This book teaches methods by developing actual code for these methods. You will find in work situations that you will use library functions instead of "rolling your own" but this book helps bring the details together by having you actually code these techniques. I support this approach 100% Once you have this overview, you can drill down into specifics with other materials like textbooks or cookbooks.I'd did flinch at some of the explanations in this book but it really is a "from Scratch" approach and some things are simplified to avoid distractions.This book also teaches basic Python 2.7 with a quick start chapter, so it is self contained for any scientist or engineer that wants to get started adding Data Science techniques to their repertoire.
You will construct functions that calculate the variance, standard deviation etc. up to constructing more advanced machine learning functions, from scratch.What is pretty underrated about this book is the very clever/clean (and challenging) way of constructing code and solving problems. Things can get very ....nested. Very valuable resource that I come back to again and again.
The phrase "from scratch" made me think this was a beginner book, but it's not really. It builds step-by-step from first principles to quite advanced algorithms and topics. For me, it's usually much easier to understand a mathematical or statistical concept if I can implement it or see it implemented in code, and so I found the approach taken here very effective.As a bonus, Joel has a very entertaining sense of humor and writes some seriously elegant Python code. I learned as much about coding as anything else.  I swear I felt like I was in the movie Inception while trying to unpack some of his amazingly efficient list comprehensions.I'll be returning to this book again and again.  Great job Joel!
Previously I used SPSS and MatLab for data analysis. This is my first encounter with Python. I am an experienced programmer, so I had no difficulty understanding the book's material. However, the book doesn't say anything about the most important tests that data analysts use such as t-tests, analysis of variance and several others. I would like to see the outputs of several functions that are used for data analysis, but this important information is not presented in the book. Thus I give it 4-star rating.
It's a great start if you want to understand data sciences. Another thing this book does particularly well, is that it tell you what the next step are in term of other books to reads. I have not try using the codes example yet but I ll do so in the future. That s being said if you are just curious about data science and you are new to the field, you can skip the code part. I love this book.
Be careful, this book is a scam. The Author likely paid his friends and strangers to review the book. Nonetheless, here is an overview:- Random lines of code demonstrating random tasks in data science.- Cursory overviews of topics in machine learning, tantamount to an introductory lesson in an undergraduate course- Chapters consist of 3-4 pages, so obviously, thorough and insightful are not words one would use to describe this book
Great, engaging book. A little hard for someone without a mathematical background, since the book touches on integral calculus and statistics and stuff. But you should be okay with a little research, plus the author provides reference books. Super informative for someone learning about data science with Python
Book is good but with technology moving so fast within a year you would already need update. It hs good concepts though
This book is good to understand the concepts behind the most common data science algorithms. It also enables readers to build their Python skills by reading the associated code. However, there may be better resources for people wanting to learn how to use the algorithms for real, as all the code learnt in this book is typically included in data science packages.
Trying to read this on my iPad was hopeless. The line wraps in the Python code were so frequent as to make it very frustrating to try to read the simplest snippet. I gave up. A waste of $20.
Very intelligently written, so resonates with reader. Concise. Still challenging especially bcs all math is done in code whose functions and classes are built up throughout the book. Works quite well as reference too.
Saints like Joel are the reason we can avoid R
Lots of coding examples for basic simple foundations to jump start your own projects.
I had to order this book twice as the first go yielded a book that was clearly damaged. The second go, the book was still damaged but less so. I decided it was a losing battle to try and get an undamaged book. Not happy about the damaged spine and bent pages. Clearly this book was used.
This book is very practical and helpful.  It contains the python pseudo code for many basic data science modeling methods, which was exactly what I was looking for.  I really hope there is more of such kind of book out there!
This is a great book-- well written, easy to digest and informative.  I've been in Data Mining and Statistical Analysis for a little over a decade now;  I was looking for a book to share with my team to ensure we were all up-to-speed on some foundational concepts: this book is it.  EDIT:  I also forgot to mention, it has probably the best get-up-and-running in Python introduction I've seen (see, e.g., Chapter 2, ~20pp.)It's the right size and correct coverage for the content and the author's sense of humor (indeed, that of a data scientist) resonates with the audience.Solid introduction, even better review or brief explanation of commonly encountered topics.One of the best O'Reilly books I've read in a long time-- in fact, a technical book at the level I used to expect from O'Reilly.
This book is outdated and there are better books out there now.  It's a decent book if you're completely new I guess.
Nice book. Like the title says, all the techniques presented are "from scratch." I find it is not so practical when in the real world, there are many packages to do the same tasks. I started learning my coding from this book and found it is very difficult until I have gained much more experience with other packages. The book should focus more on the basic idea of data algorithm and make use of packages. In this book, the basic ideas were not presented so well which makes it hard to follow the code which write in great details. As a data scientist myself, I would prefer learning the basics from the book and find the most efficient tools for usage. This book does its job the other way around. The word "from scratch" is a bit misleading because if you think you have no idea what data science is and try to learn it from this book "from scratch" then you will probably get lost. It is for intermediated level data scientist who knows what they are doing and want to learn how the algorithm works. At least, that's what I feel.
Detailed explanations for common ML modelsIf you are looking for sklearn, pytorch, pandas this is not your bookBooks is in python 2.7 but python 3 is available in github
Pretty good intro to the topic, and covers a decent bit. Cleanly written, but a bit abbreviated in some parts.Either the author or else the editor makes it too difficult to follow the mathematical explanations because it isn't explicitly specified when an algebraic identity is being used that was established earlier. So you have to take the time to get pencil and paper out yourself and work through the algebraic manipulations before anything makes sense, searching for "how did they get to _there_ from _here_?"The book lacks some compelling examples of how/why one can and should use a technique that has been addressed.
Very good book.  The examples are diverse and well thought out.  I'd say it is a solid text for beginners and intermediate Python programmers...perhaps advanced if someone needs a refresher in "pure Python" approaches to data munging.  I was especially pleased with the parsing examples.  As a statistician and programmer in C/R/Basic, I have a pretty good understanding of cleaning, reshaping, and recoding rectangular data.  This book really helped me improve my skills in working with unstructured and/or non-rectangular datafiles.  Even if you intend to use add-ons like pandas, NumPy, or SciPy, this book will give you a solid grounding in the fundamentals.  Clear and easy to follow.
concise and good coverage, good data science introduction and reference book
This is a good book for people trying to learn the absolute basics of what data science or machine learning is. It's fairly good to give you the foundation.I don't know if I would recommend the first half of the book if you're familiar with Python + basic statistics (or don't really feel like reading about stats). The second half picks up, and does a good job introducing you to the foundations of data science: data modeling, regression, classifying, clustering, NLP, Map Reduce, etc. Each of those is really only touched/introduced.This is a great read if you just want something to race through in a couple of hours to understand the vocabulary in a project or class.
Great book, good first principal understanding of core data science concepts. Code along with the examples as you will need the functions you write throughout the book.
Great for beginners to python.
Joel does an excellent job tying a "real-world" scenario into the flow of this book to teach python for Data Science. I have experience with python but even those with little to no python experience can learn from this book. The only reasons for 4/5 stars are first, I would like to see the code for all charts used in the book like in section on "Statistics". Second, the setup used in book assumes you have placed sample code in your working directory. Some code calls classes from other code and expects it to be in the working directory. I would just call these out clearly. Other than these minor thoughts, I think this is an excellently written book and I highly recommend for anyone interested in data science, data analysis, or just wanting the skills to visualize clean and meaningful data. Well done!
This book was great at providing a survey of fundamental in a real work application view
Very well written and organized.
Really enjoyed working my way through this book. It was an excellent refresher on core Data Science concepts, reinforced by building the equivalent of very basic data science libraries. I greatly enjoyed Joel's writing style, examples, and introduced problems. Highly recommended.
An enjoyable read but this book is really designed for beginners. Introduces many topics but few of them in any real depth.
It's a nice hands on introduction. It has a heavy focused on application with a lot of code examples, but sometimes I was missing the theory behind the example. It's very good for someone who wants to start in the field by actually just doing stuff, but not if you wanna understand all details behind what you are doing. In that case I'd suggest to add a more theoretical book, even though I would still recommend this book for practical stuff.
If you want to learn the basics of Data Science using Python, this book is a must have. This is a great reference for applying beginning python programming to database exploration. It is filled with techniques, examples and illustrations of how to do practical procedures using python. A great trip "Down the Rabbit Hole." You will not be disappointed.
Good introduction to "what's out there" in data science and machine learning. Having said that, the ideas are presented in a somewhat unstructured way. I am not sure if writing code "from scratch" is a good use of time for someone who is starting with Machine Learning/Data Science. It is probably much better to explain the concepts of Machine Learning in a greater depth first and have easier coded implementation of them.
Only covers Python 2.  Don't buy if you are looking for materials covering Python 3.
good only if you want to use the basic python , but these days you got use some packages like pandas scikit etc , this book do not covers those at all .
The book is hilarious and engaging. If you're part of the intended audience (someone who knows a bit of math and a bit of computer programming) then the book should move at a nice brisk pace for you. Quick review of some stuff you should already know, then goes over techniques that you may not be familiar with. If you're a relative newcomer to either of those two topics, then the pace might be a bit fast for you.
Lots of topics covered but not much example code.  I'm disappointed and will need to get another book that's more comprehensive.
Very beginner. Tries to cover too much material with too little depth.
This is among the handful of very best technical books I have ever read.As the "from Scratch" in the title implies, the objective of this book is to teach the fundamental ideas and techniques of data science from first (or nearly first) principles. After working through this book, you'll be better able to meaningfully utilize the pre-packaged software (whether it's Matlab, R, scikit-learn, or whatever) that you will use in "real life".And although the knowledge you'll gain is largely independent of the programming language, you will as a bonus learn from the clear and elegant python code included. Every key topic, from probability, statistics, and other mathematical subjects, to machine learning and databases, is covered in a crystal clear manner.In summary, this book is the bee's knees.
Uses Python 2.x but also takes the best from the __future__.  Suitable for experienced data analysts who may not be familiar with Python (which is the reason I purchased this book).
Learned a lot getting the scripts to work and modifying them for my own projects.  It would be helpful to have more information of what libraries commands come from.
Great starting point to further understand basic principles.  Rarely rate items as a 5 unless "wowed."  This is not a "wow" item hence the 4-star.
The book begins with the basics of the Python language in a chapter entitled "A Crash Course in Python."  Grus recommends the Anaconda distribution of Python 2.7, as do I.  It is free, includes Python, NumPy, SciPy, matplotlib, and IPython that are used in the book, and includes pandas which we will use to handle financial data.  This is not the book I would recommend for a person new to Python to learn the language, but it establishes the style and notation used for the remainder of the book.Chapters 4, 5, 6 are quick reviews of linear algebra and the Python data structures used, frequentist statistics, and probability, respectively.  Chapter 7 discusses hypothesis and inference, and has a nice discussion of the beta distribution and its use in describing the "prior" distribution for Bayesian analysis.Chapter 8 begins to get into the data science with a description of the gradient descent method of finding the set of parameter values that maximize (or minimize) the objective function.  The "from scratch" approach shows all the details.Chapter 10, Working with Data, begins with methods for exploring the data.  Examining the distribution, plotting single dimensional data, comparing multiple data series, normalizing, rescaling, and dimensionality reduction.Chapter 11 begins machine learning -- models, overfitting, underfitting, bias-variance tradeoff, and feature extraction.Chapter 12 continues with k-nearest neighbors and the curse of dimensionality.Chapter 13 illustrates naive Bayes to implement a spam filter.Chapters 14 and 15 treat linear regression and multiple regression, fitting a model to data, and regularization to limit the tendency to overfit.Chapter 16 explains the logistic function and logistic regression.  Examples look at measures of goodness of fit.  The concept of support vector machine is explained, although the mathematics are beyond from scratch.Chapter 17 has a nice explanation of decision trees (the models that result from rule-based trading system development, such as AmiBroker).  Entropy, as it applies to information content, is well explained and used to partition data as the rules are created.  Random forests, one of the ensemble techniques for machine learning, is described in surprisingly concise code.Neural networks are described in chapter 18, including code for a feed forward, back propagation network that identifies digits.  The interpretation of the weights of each of nodes gives insight into the workings of neural networks.The book continues on with discussions of clustering, natural language processing, network analysis, recommender system, and databases.While this is not the best book to learn Python, machine learning, or model development, it is valuable in explaining each of these topics with fully disclosed logic and computer code.This book gets five stars based on meeting its objectives -- to clearly illustrate some of the central concepts of data science.
Its very good book if you have decent knowledge of stats/data science and need to refresh your skill or for R user who quickly want to learn things in python. For beginner without any stat and math background it is going to be Data science from scratching your head instead.
Easy to follow, gives the reader a good intuition for what the algorithms are all about.
Warning: definitively not for programmers beginners. But totally recommended if you have middle to high programming knowledges, low to none python knowledge, and if you really want to be aboard Data Science ship (from scratch it is definitively well chosen word). It guides you from data handling to first principles in Machine Learning. Just 4 stars only for not clarify that you must necessarily know about programming.
For a high level summary of applying Python for common analytic tasks, this book does as well or better than most of the titles out there. Nicely done.
Interesting book for beginners.It is the really basics, though.You will not learn advanced Technics nor the associated maths behind.So, do not go for it if you have a relevant background.If you want really to start from scratch, it is a sweet introductory book.
Keep reading and visualize. There are some script available from github but no data sample to play with. No end to end example and instructions to do hands on. Did not keep up with title which says "from scratch" . Very disappointed.
Timely delivery. Book is very informative and well written!
I like it. I am new to programming but I like the topics and information  book has.
I think this is one of the best book I came across in data science esp. those who interested in location/spatial intelligence and GIS. It covers some of the basics and meaningful insights how we can apply this emerging technologies together and solve a particular problem..
as expected.
The contents of the book shows how much of hard work authors have put in. Its a must read for Data Scientist looking to propel GIS career. The authors have taken a very clear cut approach in show casing all aspects of techniques for performing smarter geospatial analysis using location intelligence. Loved reading it.
This book just hits high points of the topic and doesn't go into enough depth or detail to be really useful.
Excellent to get an introductory approach to statistical and machine learning using python.
Really enjoyed this book, so many examples, well written, I'm buying a paper copy for the library.  Well worth the purchase
Joel does a great job walking through the tasks a data scientist would take to solve hypothetical problems, and explaining the models most popularly implemented in machine learning. An overwhelming majority of the code examples are useless, which is intentional as Joel notes how to build things from scratch. Libraries (like pandas, scikit-learn, etc) provide APIs to accomplish many of these tasks without writing from scratch, but without the underlying knowledge and appreciation for how these are implemented, it may be easy to take for granted or not grasp the concepts.
Fun to read, and a great intro to the subject!
The author fails here, and badly! What seems like a nice introduction is quickly seen to be an out of order mixed up bunch of code that is not very friendly to use in practice. The code online does not match what is in the book, making you figure out what functions to call.
This book introduces basic concepts, math, and algorithms in data science with easy explanation. Also, it  covers most of critical things in the field without serious math formula and complicated programming examples. I think people not in math or programming background can approach to data science nicely with his book.
amazing book, very recommendable for anyone interested and wants to become a data scientist...
Joel rightly understood the thirst of an "softweering" mind. He has cleverly navigated the newborn through the maze of  Data Science, neccessatized with Python "only required" python skills and then taking it through need-to-know the ground-laying algorithms. This book lays the foundation of skyscraper if one is ready with the sweat.
Good beginners guide to Data Science
Didn't like seeing wheels re-invented. I would probably classify this book as a Python first, data science second. It is not bad overall.
Very happy with this product!
This book is really helpful
very good book, a lot of details of implementations
Intesesting book...
Good knowledge but he need to write down the original algorithms in every function the writer use that algorithm. However it is very good and I use most of his ideas in most of my projects
As a python programmer interested in learning applications of data science, I was really excited to come across this book.  Unfortunately what I expected to get out of this book and what I eventually got were not quite in line with one another.My expectation for this book was that it would give a good overview of how to apply statistical libraries in Python (NumPy, scikit, pandas) in the field of data science.  Instead, the book shows you how to build a lot of formulas "from scratch" and apply them conceptually.  As someone not very classically trained in statistics, I had a hard time grasping the notation used and was hoping the book would provide more "real world" applications to explain the theory.  Instead it starts often with the theory and applies it to hypothetical examples - a format which may be better served to someone with a good statistics background.For me personally, I foundÂ Data Science for Business: What you need to know about data mining and data-analytic thinkingÂ to give a better introduction to a lot of the theories used in data science (albeit without any mention of Python) and foundÂ Data Smart: Using Data Science to Transform Information into InsightÂ to provide better applications of data science techniques, granted it's mostly done in Excel with a very brief overview of R applications at the end.
Great book. Recommend it.
Great book for those interested in learning or refreshing their memory on data science using python. The book takes you through different examples of topics and the python code you would use. I did find, however, that the descriptions and mathematics behind some of the concepts were a little lacking but still great in tandem with other resources.
Like new as described!
Good introduction to data analysis.The clear syntax grammar of Python helps a lot to clarify the meaning of author.
Printing quality of this book is too low, but content is good. If you are interested go for Kindle version and don't waste your money!
Was clean copy and was delivered on time.
The book a good and basic idea how data science works. The book is very good conditions