laptak2003/AmazonDataScience huggingface dataset:
The kindle edition is garb. The formulas are not formatted correctly and is some cases wrong (through omission). That is a pretty big problem for a Stats book. I opened a ticket with Amazon, and they said that they will contact the publisher. If the publisher fixes the equations, I will update the review. But for now. Save your money the Kindle version is useless.
This was a really good book for a really tough class. It went in depth with explanations and had a lot of resources to help you learn. The standard odd-number answers were in the back of the book, and it needed very little supplementation to its curriculum that it covered.If your professor recommended this book, just know that there are MANY worse options out there!
This book came in great condition and at a unbeatable price.
Great book for an even better price and in like new condition.
Sold just as described, Great!
Good quality on time
The book is a little worn but perfect for my class!
Excelentes condiciones!!
I was really happy to purchase and get my kindle up and running it made my day with its ease!
I passed the course.  This book was assigned by our teacher.  The book described the formulas with great pictures.  The book also has exercises to help reinforce the material.
This book is excellent for several reasons.  First, it has interesting examples from politics, psychology, economics, and the sorts of things you see from science in newspapers to hold the students' attention.  Second, the homework questions require the students to use statistical thinking, rather than mere knowledge of procedures.  Finally, the teachers' edition has great explanations of the students' most common misconceptions, along with some great suggestions of how to tease out those misconceptions.
the book was in perfect order and it came earlier than I had expected. the material in it is simplified for me to be able to understand.
If you're in UF STA 2023 You don't need it
Item arrived on time and looked exactly as described. Thank you.
Item as described & fast shipping. Thanks!
It was the teacher's addition, so I can't resell it without lying.
This is sold as a new book but dies not come with access code. The code is 95.00. This should have had an access code!
Great paper weight. Didn't actually use for the class.
Great condition!
This book arrived quick and in great condition for school. I was able to sell it back at the end of the semester. Checkout was easy and the book was exactly what I needed.
As far as a textbook about statistics goes, this one makes it digest-able.
Easy to follow. Enjoy the examples used.
Good product, fast delivery
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This is a book that I used in a graduate course.  It was written very clear to where I could understand some complex concepts in statistics, a subject I am not that good in.
This book is a little vague and could be better. I don't like it very much. It will just have to do for now.
The book is much heavier.... I disliked the class due to the professor but at least I didn't have to carry around a big heavy book....
goodbook
First of all, this book is full of typos and grammatically incorrect/awkward sentences. The figures are blurry and sometimes unreadable. They look like low-resolution screenshots copied and pasted onto the pages. I was hoping this book would at least give a helpful introduction to statistics and machine learning, but I was disappointed. Take, for example, the section on distributions and statistical tests. The descriptions were so terse and confusing that I wouldn't even have understood the relation between Bernoulli distributions and binomial distributions if I hadn't already known this. I was expecting much better for the price I paid. Also, the book doesn't mention anything about the author's educational background, and he didn't seem super experienced in the field (it says he started doing data science in 2010, and this was written in 2015).
Do not buy this book. It is worthless. The concepts are not explained well at all. The code doesn't work at times. The English is quite poor. You will not learn anything from this book.
Wishing to learn Python's machine-learning toolkit - I am an emigrant from R Country - I rounded up several relevant books, and set out to narrow the field to one or two suitable for further study. My haul included (in no particular order)"Machine Learning in Python" by Bowles, published in 2015 by Wiley, 360 pages, $25 for the cheapest hard-copy now available from Amazon (including shipping)"Designing Machine Learning Systems with Python" by Julian, 2016, Packt, 232 pages, $42"Mastering Python for Data Science" by Madhavan, 2015, Packt, 294 pages, $39"Learning Data Mining with Python" by Layton, 2015, 369 pages, $43"Python Data Science Cookbook" by Subramanian, 2015, 347 pages, $48"Data Science From Scratch" by Grus, 2015, 330 pages, $24"Learning scikit-learn" by Moncecchi and Garreta, 2013, 118 pages (?!), $28"Building Machine Learning Systems with Python" by Coelho and Richert, 2015, 305 pages, $49"Python Machine Learning" by Raschka, 2015, 454 pages, $34I started with Grus and Julian - both made an OK impression but were not what I was looking for; I will cover the rest of the field before reviewing these - and then opened, and pretty soon (on page 45) closed, the book by Madhavan. No point continuing: this is the Packt-standard low-quality fare, written without any expertise, care or spell-check; an obvious step-down from the two earlier books. The positive reviews on this page do not surprise me at all: Packt titles are often supported by praise from people who had not reviewed anything, or only reviewed - and loved - other Packt titles. Moving on...
This book covers a lot of ground quick, which I liked a lot.  Like everything that has pro's and con's.  It does a really solid job of hitting all of the significant topics.  If you're an experienced Python programmer getting into data science then this is an excellent resource for you.  If you are newer to Python then you might want to hold off.  The downside of the fast and broad coverage is the depth could be enhanced some.  You will also likely struggle a bit if you are new to Data Science.  This book is less about how to do data science and more about how to do it with Python.  It also could use another round of editing to fix typos and grammatical errors but they aren't bad enough to detract from the value of the material.Bottom line, a really useful addition to my library for quick examples and coverage of using Python in Data Science applications.
Madhavan's book has proven useful for some of the projects I'm working on.The first chapter includes a brief primer on Numpy and Pandas--useful for someone that is new to the Python ecosystem, but assuming you are already familiar with those packages, it should be okay to skip to the second chapter. The second chapter includes some Python statistical examples that I have not seen in other texts, but are important when looking at different types of distributions. These distribution examples and explanations are a must-have in my collection of Python recipes. There are also data visualization tweaks that I've not seen in other Data Science + Python texts.The book also provides an intro to some of the canonical machine learning algorithms (Chapter 5). These examples are great for becoming familiarized with some of the ML algorithms out there without being overwhelmed by all the other algorithms out there.If you are looking for a good primer on Data Science with Python, this is a good book. I'm using the book as a reference more than a primer and the book is also useful.
The pace of the book is quite quick, so you will be up to speed in no time. The book gives a nice introduction to the algorithms used in data science,  explained well and backed up with source code examples of how to implement them in the Python language. My only criticism would be that there are a number of grammatical errors in the text but they do not obstruct the reader from understanding the material. Overall a good beginners book for getting to know the world of data science
Hello I would like to recommend this book I like this book because its content is about  Python with appliations in Science and has very interesting programs that we can develop using this language. It explains since the beginning to the most interesting projects. in order to apply them.Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python.
Their previous books include:An Introduction to the Bootstrap (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction (Institute of Mathematical Statistics Monographs)The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)Roughly 50% of the book consists of abridged topics from the above. In particular, most of part III is machine learning topics that are presented better in Hastie's books. The deep learning chapter is just too brief to be useful. I with they cut these out and only included new insights.Part I and some chapters from II and III are new. The comments about frequentist and Bayesian inference are illustrative. Same for James-Stein estimator. I was hoping for an update on bootstrap, but it's the same material, just with new presentation and simpler examples. All of this makes reading the book worthwhile.The level of difficulty is similar to their other books. There are many examples and illustrations, but more could have been included for Bayesian multiple testing.
This book is a must have for mathematically sophisticated readers wanting to expand their knowledge of traditional statistical inference techniques as well as inference done by machine learning.I say mathematically sophisticated because the book is full of equations, derivations and theorems.  The authors state that their intended audience is beginning graduate students and they have matched that intended level of mathematical expertise. The book is not then for those from a non-STEM background desiring a better understanding of these methods.If I had to make a criticism it is that the authors cover so much of the landscape of statistical inference they are forced to be rather terse. Because of this, I had difficulty understanding those techniques where I didn't have any prior experience. But that will vary from reader to reader depending on their statistical expertise. Some may find the brief summaries not sophisticated enough.In short, Computer Age Statistical Inference does a masterful job of linking the traditional inference techniques of Fisher and Neyman to modern machine learning all the while showing their similarities and differences.  For those working in these disciplines and wanting to have a mathematically grounded understanding of the wide variety of methods now available for statistical inference this book is a much needed guide.
Yes, as many other reviewers noticed this book has a lot of commonalities with other books by the same authors.But still, it's substantially differentIt's a historical review from experts in the area about what methods were developed for which purposes and how they are connected to other methods and how they are comparable to other methodsThe book provides an intuition on very different methods from Fisherian to EM and survival analysis to others  what makes this method working and what math behinds it and what tasks it's good for
A great book for statisticians, machine learning scientists.
A good book includes the essential topics about the contemporary statistical inferences. This book may suitable for who with some intermediate statistical background looking for an introductory reading.
Very insightful and informative statistical inference book. I own and have read the authors' other books and as always, this new book is fantastic.It's very up-to-date and a great reference book for both intro-level students and statistics professionals.The explanations of concepts are vivid and easy to understand, and quite often it makes you think from a different angle. Love the writing style!It's an academic book, but a quite accessible, insightful and pleasant read.
Just amazing. I read the book like a novel. It made the topics that I struggled for many years extremely easy. I sincerely thank authors for this service.
Excellent review of Statistical inference.
The book is absolutely all I was looking for. I strongly recommend to professors, students, and practitioners of statistics.
Totally awesome clear summary of the development of inference, the current state of the art, and some intriguing speculation on where it will go in the future.
Perspectives about statistics missing in other expositions that has insiteful looks at what statistics is all about historically and currently
Wonderful exposition of modern statistical techniques. Would benefit from having extended problem sets
This magnificent book contains almost everything in statistics up to 2010 except for classic multivariate analysis and some topics in nonparametrics.However, here is the reason I gave 4 out of 5:pros: comprehensive, heuristic, vivid and full of examples.cons: no code is provided; some chapters are just the copy from â€œelements of statistical learningâ€.
Really interesting and helpful.  Arrived right away.  Thank you!
I've always loved their books.Both macro views and micro analysis.
As a R language programmer, I'd like a book so clear, concise, to the task and motivational as Russell's but for R . Worth every dollar
Agile and "Science" are the anthesis of each other. Science is a commitment to rationalism. Agile is a word unintelligent people use when trying to appear intelligent to other unintelligent people.So to set the record straight, I read this book cover-to-cover (unusual for me).  I found it to be practical, well organized, insightful at times and overall a good introduction to the topic of Data Science.I would like to clear up something not about this book but about our entire culture-- that always wants something for nothing. You will NOT be generating deep insights about your business effortlessly or quickly. By definition, these things are difficult and time-consuming.So buy this book and get started.
I am reviewing a copy of "Agile Data Science 2.0" by Russell Jurney that I received at no cost through the Amazon Vine program.Working in a data science group in I.T., we've had a lot of conversations about how I.T. operating approaches - agile, devOps, PMO - apply to data science. Data Science tasks are different in that not all work is intended to lead to functioning software, as well as the strongly-iterative approach that is necessary to deliver results to stakeholders in a way that discrete units of software might not otherwise be reviewed.Russell Jurney's "Agile Data Science 2.0" goes a long way in moving that conversation in the right direction. I had three target audiences in mind when I acquired this book. The first was our PM, who had worked in I.T. for years as a director and project manager but continued to try to wrap his head around the data science process. The second was a director who was new to the data science process and wanted a better grasp of how to communicate expectations to the team. The third was myself, having spent time in both IT and in research, I had seen the two worlds and wanted a way to help explain how the two mesh.Jurney has offered, as have many data science books, a suggested stack and how to implement it, but the most valuable part of the book I thought was the first two chapters for their emphasis on the agile manifesto for data science, a description of the many roles that go into a team, and highlights of how agile can make for better data science both in terms of research and in terms of products.This is not a text to learn Spark from a developer's perspective but rather to understand how spark can fit in. Spark isn't the only platform, so those using Dask or other tools will still find value here.If the book has a weakness it's the focus on developing a web portal to expose the data science product; this isn't a bad way to do things, not at all, but it's not where our work is going at the moment, so this limits the applicability of some of the chapters. But there's nothing that keeps the book from being useful .. so much so that I honestly don't know whose desk it's sitting on at the moment, since as soon as our PM finished it he gave it to a BA, who gave it to another PM...
This book attempts to introduce a new methodology for analytics product development.  And within this scope, I feel that the book accomplishes it's stated goal.  Although somewhat lengthy, the flow of information within this book stays focused on the critical path to the end product while covering documentation, facilitation, exploration, and discovery.  A reappearing theme of aligning data science with the rest of the organization is present throughout.After the obligatory introductory chapters, the book introduces a suite of tools used for the remaining chapters.  These include Jupyter Notebooks, Python 3, Spark, sci-kit learn, and lightweight web applications.  The data it introduces is the OpenFlights Database that is freely available from the Bureau of Transportation Statistics followed by weather datasets available from NOAA.  The first goal is to use the tools and the data to predict flight delays.With this setup, the book continues with detailed studies of collecting and displaying records, visualizing data, exploration of data, making predictions, deploying predictive systems, and improvements.  I appreciated how the book followed the same datasets throughout as it moved through all the stages it's proposed methodology.  Overall, a solid addition to the data science library.
At the outset, author Russell Jurney describes his intended audience: â€œAgile Data Science is intended to help beginners and budding data scientists to become productive members of data science and analytics teams. It aims to help engineers, analysts, and data scientists work wth big data in an agile way using Hadoop. It introduces an agile methodology well-suited for big dataâ€. In my opinion, he achieves all of his goals. As a computer forensics specialist, I always deal with data. What has changed in the past two decades is the scale of the data I have to analyze. Weâ€™ve come a long way from analyzing a few megabytes of data. Now, the possibility exists that I may have to deal with petabytes of data to find what I am looking for â€“ or confirm its absence. To that extent I have to deal with people who tell me things canâ€™t be done, usually within an adversarial relationship. What this book does for me in a big way is clarifying the process of getting from here to there. Jurney describes the process clearly and in great deal. While I am not exactly the intended audience, I think those who are will benefit greatly from this book.Jerry
Overall, this book is pretty good.The first part of the book is basically the author trying to convince you what literally everyone who's ever been in a dev environment knows is false; Agile environments aren't the great savior of development, and it's really nothing more than today's buzzword-filled tech management style, and please don't let this book fill your head with illusions.But when the book is focused more on technical matters than the author's opinion, it does a pretty decent job of covering important data science concepts.The introductory/getting set up sections are a bit light, but honestly, I struggle to envision someone who's tech-savvy and mathematically sophisticated enough that they're attempting to set up something like a machine learning system with Spark, but still need to be walked through a Jupyter Notebook.The reason I took off a star, though, is because this book suffers from the same problem that a lot of data science books do: it casts a wide net, and covers concepts in a mile-wide, inch-deep fashion. Data science is a huge field, and trying to condense it into a handbook like this certainly isn't easy, but the end result of trying to cover it all in under 350 pages is that you get things like "Introduction to Predictive Analytics," where the author spends less than three pages introducing the idea (or rather, just regression and classification) and then jumps straight into some functional examples. The examples are good, but I can't imagine someone feeling competent (or even intro-level capable) if that were your only introduction to the concept.
As a erstwhile data scientist, this reviewer doesn't agree entirely with the premise as suggested in the book that "the most effective output of the data science process suitable for effecting change in an organization is the web application. It asserts that application development is a fundamental skill of a data scientist." From my perspective, being a good data scientist means really understanding the data, and figuring out what to highlight. Sometimes an application (web based or other) may be the most effective method, but not always. And occasionally, there may be very important data security issues that need to be addressed if using the web as a medium for data transfer, display or analysis. So, this reader is not sold on the exact idea, but it provides an interesting way to consider data science, and whether there are more and better approaches that can be used. Keep in mind that people think of data as a medium with out bias, and machine learning in a similar vein; however, if data are collected in a biased fashion, or questions or slanted in such a way to capture answers in a desired fashion, the data itself is not bias free, and machine learning turns out those flaws as "facts," so there are many facets that need to be considered when using data science with only an empiric framework for exploratory data analysis.Agile methods are becoming the gold standard of approaching technologic solutions, but, as Jurney points out, may be difficult when approaching data analytics and big data. understanding the nuances and caveats of each dataset make the "agile" approach sometimes a little dangerous and difficult to create as a gold standard for data analytics vs some other methods (such as scrum) that currently exist in a software development environment. In some cases, it's also difficult to weigh the impact of "failed" experiments (data was wrong), as the onus is usually on the analyst to present the "right" answer.There's a lot of good stuff in here, though, as analysts and managers of analysts think about how to transform the work they do. With more and mroe data available, in structured and more unstructured formats, how do you iterate and improve upon the data understanding? Consider the importance of the visuals. Move out of the framework of only relational tables. This book is relatively up-to-date with the most commonly used tools (Spark instead of Mapreduce); examples of how to use Spark in Python (load PySpark), MongoDB, Elasticsearch, etc. There are good code samples and a very well indexed appendix.Overall, some interesting concepts that a data team will enjoy discussing, even if they don't agree with all of Jurney's points of view.
I like this book, but I felt it skipped some important details and tries to cover too much in too small a space.  It does a good job talking  through things but isn't ideal for someone new to the field
Too many generalities, no real recipes to call it a handbook. The colloquial tone by the author is appropriate for general discussions but not for a data science handbook
Half the pages are stuck together and is generally just not an easy book to read compared to other Data Science books in this area.
Many of the mistakes on the first chapter gave me a feeling that no enough effort was put on revising this book. I did not continue reading ..
I am 100 pages in and I love this book. It is important to note, however, that you should not expect a ton of exercises with in depth explanations. The real value in this book is not the exercises but the author's thoughts and advice he shares throughout the book. It is super useful to hear from somebody who works in the data science field and who explains how you should think about the job, what steps to take from beginning to end in the data science process(broadly speaking), what questions you should ask about the data, what techniques you will rarely use, what you will often use, the limitations of certain statistical methods and much more. I basically look at this book as a way to complete the circle after you have learned much of the deep concepts of python and the syntax. This book will serve me well when I begin looking for employment in this field as he gives you a clear understanding what you need to do to be a good data scientist.
One of the best all around data science books I have found. It covers lots of real-life applications rather than the typical pie in the sky theory examples. I think it would make a good desk top tool for the number cruncher or database jockey of any skill level.
If there is one data science book you need or wish to buy consider this one. In just a couple of hundred pages the author distills theory, practice and plenty of actual examples and real code! The theory is succinct and erudite whetting the appetite of readers that need to understand it. The practical examples go into adequate detail. As a bonus the author covers related topics like programming tips, hints for improving performance of code, his personal preferences and idioms etc. The breadth of topics covered is impressive. Almost all machine learning and data science algorithms and software packages are explored. The book can be skimmed in a few days, can be used along with the copious references to probe deeper into any particular topic and will prove an invaluable reference guide as well.
The author did an incredible job writing â€œThe Data Science Handbookâ€. I like how he organized the book so that readers could easily browse through the chapters that they want to read without losing the content. Besides, each chapter has its own audiences. For example, if the reader is an experienced data scientist, he/she could start from â€œAdvanced Topicsâ€. If the reader is a business analyst or a manager, he/she could start reading about â€œBig Dataâ€, â€œDatabasesâ€, â€œMachine Learning Overviewâ€, etc. Every chapter has its own level of complexity, real life examples and Python Codes. This is exactly how a great engineering mind would organize the book.I usually feel that textbooks and handbooks are boring in general. However, â€œThe Data Science Handbookâ€ is not at all. The author utilized a conversation style language â€“ it almost feels like he is talking to you and sharing his extensive real life data science experiences.So, what about improvements for next edition:There was a comment on changing examples from Python version 2.7 to Python version 3.0. I think this is not an immediate need. Programming languages evolve but the fundamentals stay the same. The author explains the fundamentals very well, and this book does not have an intention to teach programming languages as well.Mr. Cady provided real life examples in each chapter. I believe he could add a capstone data science project at the end of the book. He can define a problem or problems and data sets and let the readers of his book design a solution around the problem and let them publish it in his website.I think â€œThe Data Science Handbookâ€ will be an invaluable reference book for data scientists, students, business analysts and managers for a long time.
I am reading the book and I find it well written. Field, you should take up teaching. you have explianed all the details very well. The only thing missing is the sample data so your scripts can be tried on immediately.I did a 10 course - data science certification recently and this book is helping me, brush up on my newly learnt skills.Well done Field
The clear writing style with code examples makes this a great way for me to learn data science on my own. It is also a super reference book to have on my shelf. This is very readable and explains some tricky concepts in an accessible manner. Thank you for this great book!
As a handbook, I think the coverage is too shallow. It's a mile wide, but about three inches deep.That's not necessarily fatal -- there's a value to giving readers just enough so that they now have tho proper vocabulary and context to pursue additional learning -- but it's not what I would expect out of something called a handbook.The author mentions that this book should serve as a self-contained course in data science, and that's partially true. It's written at a level that would be good for someone unfamiliar with the basics in order to get up to speed at an introductory level, and to talk competently about general ideas. But you'd want to follow it up with more focused learning, and that's absent in this book.So I guess it comes down to audience. If the audience is aspiring data scientists, this book doesn't go far enough. If the audience is people wondering what data science is all about, or business folks looking to better understand what data science is and what data scientists can do for them, then this book hits the mark.
While Cady is successful in collating a large range of topics, it is unclear who the target audience is and how anyone can use this book as a supplement or even a pointer for additional reading.  The ambitious breadth of the book forced to sacrifice the depth for all the topics making this book more of a thought starter for an executive (with no data science background) leading data science team.  The notes on classifiers and advanced classifiers are helpful.  The treatment of NLP, in particular, is very superficial.  The lack of detailed reading lists and specific examples/business problems to relate the explanations limit the utility of the book.
The people who most need to read this book are those who would decide that this isn't useful because it doesn't have enough real examples.I've been doing data science for tech companies for almost 20 years -- long before anyone knew to call it data science.  And the biggest problem in the industry is that there are so many people who think that data science or machine learning means "implementing a gradient descent" or something like that.You need to understand how to frame questions, where to look, how to structure data, how to choose features.  There' so much waste in the industry created by engineers who implement "machine learning" without understanding enough of the data science to know if what they're doing is even useful, practical, or sufficiently accurate in a real-world way.This should be required reading for anyone in the field.
Good things: Talks lightly about many topics that a main stream data scientist needs to know nowadays. The author uses Python throughout the book.Bad things: Material is presented in a poor way. References are made to hyperlinks. The book seems to be typed in Word, or any other document processor not suited for professional book writing. All the techniques are presented in the book in a very deficient manner, just as an example, take a look at the Time Series Analysis chapter. You can tell the author has no more knowledge about it than someone at a undergrad level.This books is not at the level of what Wiley typically has to offer. Shame on the Editor and the publisher.
I think there's a real need for this book.  There are plenty of higher-level books on the subject.  You're still going to need Python for Data Analysis, Think Stats, Statistics in a Nutshell, An Introduction to Statistical Learning and Hands-On Machine Learning to really drill down on these subjects, but this book does an excellent job of filling a gap - more detailed than other surveys but also connects the dots between data analysis, stats and ML.
I'm only going to give this book a brief review as I didn't find it very useful and didn't finish reading it. What the author wrote is not terrible, but the book has one pervasive flaw and that is the amount of coverage and explanation on any given topic is so brief as to be almost pointless. Beginners are going to need a lot more explanation, detail, and hand holding, whereas experienced data science people aren't going to find the cursory coverage of topics much use. Experienced data science people should look at the table of contents and  ask themselves am I really going to learn anything from 1-2 pages of material on topics on a topic such as neural networks or decision trees. I'm sure someone is going to tell me the point of a handbook is brief coverage of many topics, well maybe, but in this case the coverage is far too brief. Also the book doesn't exist in a vacuum and in addition to many vastly superior competing books there is comprehensive information available online for basically every topic and software library. This book is a waste of time for beginners and experts alike.
I worked with Field previously and found him to be precise and focused on the job at hand. He brought that same sensibility to his book. If you're new to data science topics, you'll find concise explanations that quickly give you the explanations you need, with just enough intuition that the concepts make sense. If you're an experienced data scientist, you'll find quick reminders for topics you know, but the book has enough breadth that you'll find topics that are probably new for you.
This book gives a great overview of the field (sorry for the pun) and then delves into the nitty gritty.  I applaud Field for writing a comprehensive book on the subject. There are many examples in the book and reference on his GitHub to experiment with and hone one's approach.  It is very helpful if you have some programming experience, but it is not necessary.  Field points you to all of the necessary references to fill in the gaps.
I've worked with the author of this book, Field Cady, at the Allen Institute for Artificial Intelligence, and I attest to his wide knowledge and unique experience in data science. He's also a thorough and passionate illustrator. The book covers many topics related to data science and does not require much beyond programming.
I will not be buying this book -- and thus not considering it as text for a course that I am teaching -- because the Python is version 2,.7. Yes, I know that a lot of data scientists use -- or previously used Python 2 -- but it is time to change to the new version. I do not want to add the additional burden of mental conversion to my students.Since the author says that the differences are small, let the users who are on Python 2 do the mental gyrations."The End Of Life date (EOL, sunset date) for Python 2.7 has been moved five years into the future, to 2020. This decision was made to clarify the status of Python 2.7 and relieve worries for those users who cannot yet migrate to Python 3."  (PEP 373).Please have 20/20 vision (pun intended!) and revise.  The content looks otherwise quite good.
I have just started reading this book, but it has repaid my serious study.  It is written in a very clever way, focusing on giving the big picture, while not neglecting considerations related to direct application.  The concentration on analysis of illustrative examples really aids self-study.  The author displays a brilliance in this book in presenting Matlab and interweaving it with the study of important topics in numerical mathematics.  The emphasis is on the mathematics, understanding, and applying numerical methods, rather than strictly in technical details of Matlab.  I recommend this book as a learning aid for Matlab, if one is interested in certain areas of numerical analysis that the book addresses.  The first part of the book is pretty elementary, but the presentation is interesting and engaging, and aspires, to a certain extent, to serve as a nice survey introductory treatment at the high school and freshman college level of applied mathematics.  There is a kind of juvenile cuteness about the first several chapters, but the level does start to pick up when differential equations are discussed.  Applied mathematics is establishing itself, more and more, as a competitor for traditional pure mathematics, thanks to the power of modern computation.  The problems the applied mathematicians work on are growing in significance and originality, due to the impact of improvements in computers and computational techniques.  This is the "experimental" or "engineering" side, perhaps, of mathematics, and it can often be as exciting, clever and original as the "theoretical" or pure side of mathematics.  My own feeling is that computational science (as we might call this area of mathematics now) hearkens back to the ancient Pythagoreans, and an interest in understanding and grasping the harmony of the cosmos.
Excellent, detailed presentation of Singular Value Decomposition and other essentials of computational data science.  Strong emphasis on a groundwork for image processing.Many interesting historical references.  Large easy font with great figures and illustrations.Includes a quick/indirect Linear Algebra review; but you really need to have that beforehand (I recommend Gilbert Strang's intro).  Also an intro to Differential Equations would be a prerequisite for some later chapters.Some of the math is above my level; and I'll be revisiting those chapters.  I learned of Dr. Kutz through his online course, which is great; and this led me to his book.
I have to admit that I owe Nathan alot. I have  listened to his online Data Analysis course, and I was amazed as to how he is able to make dry mathematical concepts be accessible, as well as intuitive.I purchased the book as a companion to the course, and I think that Nathan, should write other books in a similar way, to cover other aspects of mathematics in the same way.  For example, optimal estimation, and Kalman filtering, and others.Kudos Nathan, good job.
If you are trying to make use of advanced mathematical tools to analyze your data but you are particularly from engineering background then this is the book. The author did a terrific job in explaining the methods like POD, DMD. There is a short chapter on Finite element analysis too. If you have background on finite volume or other CFD methods but would like to know the core of the Finite Element method this will server that purpose too. Over all I am very pleased to have such a book and I heartily congratulate the author for being successful in his goal of making the mathematical methods accessible to general audience.
This is an unusually interesting textbook and professional reference, full of innovative examples of important mathematical modelling techniques. It is the textbook for the excellent free graduate-level MOOC courses offered by the author and the University of Washington through Coursera.
The book arrived right at my doorstep before I was even expecting it! It has been tremendously helpful to have this book to go back and review material not thoroughly covered in class on MATLAB. I would highly recommend this textbook to those interested in MATLAB or who are enrolled in a university class.
Great reference book. Totally recommend.
Both this text and the class it relates to are peerless.  The class is available as videos on the web.A+
Intuitive book and good reading. Not very rigorous but definitely helpful to new comers.
This book contains an impressive collection of advanced mathematical techniques explained in a clear and accessible way.  Great examples with tons of Matlab code make this book a must have for engineers or scientists that work with data.  The connection between PDEs and methods from  image analysis are very interesting!
This book is half of what it could have been. Mr Kutz did NOT make an attempt to keep reader aware of his vicinity in big landscape of the numerical methods.I know Mr Kutz is cool an casual (from coursera), but it would have been nice if the book had some formal big picture talk here and there, as where this method or that method fit in greater scheme of things. Please Mr Kutz fix this in next edition.
The subject is not easy, therefore the book is surely not for a primer on graphical programming, even more for a primer on computation and algorithms. If you need some very advanced algorithms to solve then you'll find it here. The very latest advances on subject are not present here.
Vershynin's book covers a set of topics that is likely to become central in the education for "modern" mathematicians, statisticians, physicists, and (electrical) engineers. He discusses ideas, techniques, and tools that arise across fields, and he conceptually unifies them under the brand name of "high-dimensional probability".His choice of topics (e.g., concentration/deviation inequalities, random vectors/matrices, stochastic processes, etc.) and applications (e.g., sparse recovery, dimension-reduction, covariance estimation, optimization bounds, etc.) delivers a necessary (and timely) addition to the growing body of data-science-related literatureâ€”more on this below.Vershynin writes in a conversational, reader-friendly manner. He weaves theorems, lemmas, corollaries, and proofs into his dialogue with the reader without getting caught in an endless theorem-proof loop. In addition, the book's integrated exercises and its prompts to "check!" or think about "why?" are strong components of the book. My copy of the book is already full of notes to myself where Iâ€™m â€œcheckingâ€ something or explaining â€œwhyâ€ something is true/false. (Also, as an aside, I love that coffee cups are used to signal the difficulty of a problemâ€”good style.)I want to highlight a few examples where Vershyninâ€™s choice of topics and his prose shine brightly. In section 4.4.1, he guides us through an example that clearly illustrates the usefulness of Îµ-nets for bounding matrix norms. Iâ€™d seen Îµ-nets and covering numbers before, but never had good intuition for why they showed up in a proof.Similarly, Iâ€™d struggled to gain intuition about why/how Gaussian widths and Vapnikâ€“Chervonekis dimension capture/measure the complexity of a set. After reading sections 7.5 and 8.3 and working through some exercises, the two concepts are much clearer. Moreover, Vershynin connects these ideas back to covering numbers, which helped me better my understanding of all three concepts.Finally, I found the discussions on chaining and generic chaining in chapter 8 to be excellent. Following them up with Talagrandâ€™s comparison inequality, which becomes the hammer of choice for the matrix deviation inequality (in chapter 9), rounds out a long, but very valuable/useful chapterâ€”and one that Iâ€™ll certainly re-study and reference.I would recommend this book for those interested in (high-dimensional) statistics, randomized numerical linear algebra, and electrical engineering (particularly, signal processing). As I'm coming to realize, the "concentration of measure" and â€œdeviation inequalityâ€ toolbox is essential to these areas. Lastly, I believe that this book makes a great companion to â€œConcentration Inequalitiesâ€ by Boucheron, Lugosi, Massart.
As has been covered in the other reviews, this is a really excellent text and I almost feel guilty breaking the string of five-star reviews. That being said, something nobody has commented on is the print quality of the book. In regular lighting conditions, you can clearly see the text on the other side of the page. I found this distracting enough to switch to reading a pdf copy from the university library. I thought I may have gotten a knock-off copy, but I ordered it directly from Amazon not a third-party seller. It's a little puzzling because I also have Wainwright's High-Dimensional Statistics from the same publisher and it does not have this issue. I've attached a couple of pictures so you can judge for yourself before purchasing.
This is definitely the best math book I read this year. It is at the same time a very well-written textbook as well as a great reference in the area. Excellent choice of topics, a joy to read, and especially valuable were the exercises throughout the book which makes it perfect for self-study since you can solve the exercises to internalize the ideas.
Thus far, I understand the book is the best one of making sense of the fundamentals of high dimensional probability, particularly of help to beginners.
This is a very well-written book for people who are interested in understanding the geometric aspects of modern data science. Personally I would say I am very much influenced by this book as well as many other papers by the author. He is a great and inspiring mathematician.
Useful for my thesis
A useful book but not easy to read.
Great book for learning concentration of measure/probability with lots of nice (non-trivial) ML applications included.
I strongly recommend this book to anyone interested in high-dimensional probability, concentration of measure, etc.
This book is an excellent source for high-dimensional probability. It has both been educational and also useful in research projects as a handy reference. I would highly recommend this book for any researcher who has to deal with probability.
I particularly dislike the "embedded exercises" approach. Some of these exercises are not easy to solve even with the hint. Solutions are nowhere to be found. So I found myself spending huge amount of time doing the exercises. It is not a smooth experience for self-learning. I'm not against doing exercises but it would be nice if there is a solution manual available. That being said, I do appreciate the text presents some hard stuff in a somewhat elementary way. For example, some of the papers I found online discussing same topics as in the book turns out to be way harder to read.
Worthless -- Utterly useless.  All examples are oversimplified generalities.  The method the author uses to approach the "4th Industrial Revolution" is nothing new, but rather just a regurgitation of identify the problem, determine whether solving it is worth the effort, magically solve the problem, implement the magic solution and evaluate.  Yawn.  Worse because it's tedious as he just says the same thing over and over.  There is no discussion on how to strategically approach data collection, just understand what you have and maybe look to get more -- nothing insightful.  Machine learning is not new, but the author fails to explain what is different today.  No thought-provoking ideas on how leveraging machine intelligence can transform any (every) aspect of your business.  It's actually worse than I have described.
it's written for a A - B student, probably Sophomore/Junior in high school.if you are in the real world, (executive) it talks about a lot you likely (or should) already know.
The book is easy to read and a must buy for any one wanting to develop a strong sense of data-literacy and what the author calls machine-intelligence mindest. The first half of the book provides the fundamental knowledge necessary to understand the data science workflow, especially around what to expect, executives involvement, and how to develop critical thinking for each step. I felt that the case studies walked me through these ideas very well and the data strategy workshop at the appendix is a powerful tool to start acting on this initiative.
I have seen so many books talking about Pythons, R languages, and the methodologies of many tools. However, I believe the fundamental of building a true smart application is not just about mastering all technics in Data Science. This book explains radically what we should adapt to a digital world and how executives should perceive and strategically execute it. As an advocate to emerging data technologies, I highly recommend this book to everyone who is considering taking a share of the Fourth Industrial Revolution!
Mr. Kaldero provides a fair amount of background on the field of data science and follows it up with a comprehensive and practical framework to get any business started down the path of leveraging machine intelligence. The case studies at the end are very helpful in cementing the bookâ€™s concepts.