laptak2003/AmazonDataScience huggingface dataset:
This book provided a great review for me as I headed into a Data Science role after years of working as a Software Engineer. I didnâ€™t need a review of the actual math behind each model type, so it was perfect finding a book that would remind me of the methods and use cases without requiring hundreds of pages of reviewing the statistical underpinnings.
This is a very short and concise introduction to the very basic concept. Perfect for those who want to learn ML but have no idea of what it is.
This is a tremendous resource for anyone looking for a refresher or a basic introduction to machine learning.  The examples were easy to understand and the lessons are heavy on theory.
Good book but not worth the high price tag.
The data science profession is being sought after over other data professions.  This was a great read to understand the data science job role in relation to other data jobs such as BI.
Excelente libro introductorio al mundo de data science con la descripciÃ³n de los principales algoritmos.
Thank you for this quick overview of this important topic.This read is helpful to break down ML concepts for business and industry...Thanks again.
Amazing book! Must read!
Easy read for people without data analysis background.
I really liked this book because it gives a broad overview of data science and machine learning without overwhelming the reader.
Excellent source for students who studied data mining and is looking at either a refresher or how to bring everything together.
So easy (and fun) to read!
I am teaching myself python and wanted to know more about the packages within the numpy package.  This was a very good resource for me, but, I have taught descriptive and inferential statistics for a while.  I would recommend this book to anyone learning python and data science techniques.
I can see why this is so popular. This book is mostly machine learning - no data engineering, munging, code (or math) here.
Although the whole concept is keep it simple, sometimes it feels too light and that something is missing to connect the dots. Topics such as Neural Networks should be explained a bit deeper or removed at all.
This is great as last minute interview prep or to explain the subject to a colleague. Good comparison of different techniques.
Itâ€™s a great quick introduction to the most common ML & Data Science methods and the intuitions behind them. While not very detailed, it does a good job for what it sets out to do.
This is an excellent book both in depth and breadth ofthe topics covered. It gives descriptions, analyses, and insightsabout the most popular algorithms on various topics, and it coversmany more areas than most books. The book is well integrated acrossthe broad diversity of topics that are covered, and connections betweenmethods and topics are pointed out throughout the book.  For most of the important topics, a lot of detail is provided in terms of algorithm description and pseudo-code. In some cases, interesting analyses are also provided.For instance, in the case of frequent pattern mining algorithms,not only are more algorithms discussedthan most of the other books, but a discussion of multiple choicesof data structures for the same algorithm is provided,along with their relative trade-offs. The relationships among variousalgorithms are also discussed. I have seen quite afew textbooks on data mining, and I have not seen anything close to thislevel of detail in any of the other books. Overall, my impression isthat the authors have done an excellent job of calibrating detail levelto topic importance. Therefore, it can serve both as a textbook andas a reference book. On the other hand, this is certainlynot an implementation or programming-centric book. The book is good atteaching principles and concepts.
Blowing away the fog of mystery around Data Science and its various techniques. A recommended read for the inquisitive S
No math, no jargons, easy examples to make you grasp the concept. You would appreciate reading it before diving deeper into technical and mathematical details.
I know nuts about data science but this book is surprisingly easy to understand!  I particularly love the examples that accompany each chapter. The examples relate to everyday life and effectively narrate the concepts in each chapter.  I would recommend this to anyone who wants to have a quick but yet in depth overview of what Data Science is about.
I knew very little about Data Science. Now I know a lot more. I need to check the appendix for DS exercises to explore more. Four ðŸŒŸ because it ended too soon.
Fantastic primer on data analytics. Highly recommend to anyone interested in learning more about this fascinating subject area. Introduces the major methods in simple terms.
The book presented hard-to-understand topic in the friendly way. I would recommend this book to people who need to have an overview of algorithm used in data mining field.
It's a very good read. Insightful. It's suitable for readers without data analysis background.
5 sort of ended abruptly however. Wish there was a review of concepts discussed. Otherwise excellent.Hello I cannot submit
Love this book! Amazingly written and very easy to digest even the most complex and confusing algorithms. Good job!!
The author has kept the book simple deliberately. I am about halfway through and so far it is well worth the time and money.I would have loved and rationale of why we use one or the other algorithm...but may be that is coming in next chapters. (
Short and to the point.Requires more material with more examples.
A helpful introduction to data science for business. The author spells out every step so if you follow along you can go from nothing to a strong starting foundation in data analytics to machine learning.
As the title accurately indicates this book gives a very informative overview where I felt Iâ€™ve learned something very meaningful at the laymanâ€™s level. And an excellent starting point for someone who would be interested in pursuing the topic on a more technical basis.
Great introduction to predictive analytics, without relying on math, it explains the most used models for business. If anything, it would need some introduction to optimizing, but other than that it's a great read.
Numsense is an excellent book that I categorically recommend to everyone. It explains the fundamental concepts of data science in a way that is concise, fun, and well explained. As this field increasingly influences our ability to make better predictions and ripples across more and more areas of our economy, these are concepts that are important for all laypeople to understand.
Gives you a good sense of what different data science methods are, how they work, and why people use them. Presentation is qualitative and pictorial, driven by examples, rather than deductive or overly mathematical.
I've been trying to teach myself data science for a while now using a combination of textbooks and online tutorials, but more often than not I have been frustrated by a lack of 'true' understanding of the logic underlying the algorithms. While I could maximize the Lagrangians, find the eigenvectors and reproduce most of the proofs in the textbooks, I never really 'got' how the algorithms worked. Ng and Soo's  book is thus a perfect complement to the math-heavy textbooks: it omits all the equations (there are already many other books for that) and presents just the intuition in crisp prose and thoughtfully designed graphics. Highly recommended for any beginner in data science.
This book is very easy to read , Perfect match for those who doesn't want a book with all the math fluffI would recommend this book to anyone ( beginner) who wants to start his journey in Data Science
Never seen a book as amazing as this! Concise, easy to read and sum it up on data science!
If you are looking for a overview and how to use each algorithm in certain scenarios, then grab this book. It is a quick read and the ROI (return on investment) is high.
Very clear, straightforward explanations make this book easy to follow for the beginner. Would recommend for anyone wanting to get an overview of machine learning without being buried by overly-technical explanations. Great value!
Well written, well designed book. Read it if you don't even know the names of standard methods in data science.
This is probably a great book for those who are frightened of any math at all.  However, some topics just cannot be easily covered without any math at all.  I found the book to be more a collection of categories and buzz words than anything else.  I only give this a 3 star because the book may be helpful to some folks, but for my purpose, it simply did not provide enough detail or depth.
I am in marketing and not a math person and even I find this book an fairly easy read.I may update this review but today Facebook is all over the news for exactly what is mentioned in this book. Here is an excerpt from the book:Personality traits are also a good way to group customersâ€”as done in the following survey of Facebook users. Users were invited to complete a questionnaire in order to check how they scored on four personality traits: extraversion (how much they enjoyed social interactions), conscientiousness (how hardworking they were), emotionality (how often they got stressed) and openness (how receptive they were to novelty). Initial analysis revealed positive associations among these personality traits. Highly conscientious people were likely to be more extraverted, and, to a smaller extent, highly emotional people tended to be more open.Hence, to better visualize these personality traits, we paired themâ€”conscientiousness with extraversion, and emotionality with opennessâ€”and aggregated scores within each pair".
This is a very good introductory level machine learning book, especially for those without very strong math background. It tells the algorithms in a very clear and simple way. I will recommend this book for machine learning beginners.
Have been struggling to find a suitable book on data science I can understand - all the others are so technical! This one hits just the right spot. Everything is explained for a someone with no math background but wants to apply data science to my business. Highly recommended for beginners.
Great intro to data science with all the major statistical topic explained in a way that newcomers can understand. A must read for anyone just getting started!
"Best book I've ever come across for laymen trying to learn data science. Explained simply, no frills. Authors are very considerate of readers, never a moment whether a jargon/concept is left unexplained. Even the pictures are colorblind friendly."
This gave me a clear picture of what algorithms to be used and when. Best book and I would suggest it to all those who want to learn machine learning!!
The book is a well written introduction to data science. The explanations are logical and easy to understand. The figures are simple and easy to understand.Compared to other introductory books, this book is superb.
Useful intro that touches on advanced concepts in a very clear and concise way. I'd love to see a sequel focusing on machine learning, model building and iteration and going to the next level of detail on that.
All of these terms I've heard floating around my head have finally concretely been defined in easily digestible format. Buy this book and start your journey on the right foot into the world of data science!
Easy to read. It gives a good grasp of different machine learning algorithms. It explains complex techniques in a very comprehensive way.
This is a very informative book.  I have been studying statistics and machine learning for a couple years, so I was already familiar with some of the topics covered in the book, such as decision trees, random forests, and neural networks.  However the problem with studying things on your own, as I have done, is that you can frequently miss whole fields of topics.  That is why this book was useful to me.  I had previously studied a few topics deeply (depth first learning), and this book was a good opportunity to study a number of topics more quickly (breadth first learning)This book gives an overview of how machine learning works, and then tells how 11 different machine learning algorithms work.  For me, I was very familiar with 4 of the algorithms, had a passing understanding of 4 more of them, and little to no knowledge of 3 of them before reading the book.  After reading the book, I got some useful analogies and real life use cases for the 4 topics that I already knew well.  I deepened my understanding and spotted some errors in what I thought I knew about the 4 topics I had seen before, and I kicked the other 3 up from a level where I had no idea what they where to one where I would be comfortable using them in a machine learning toolkit such as Python's Sklearn, or could as intelligent questions if I'm in a meeting where someone is discussing them.
As its title suggests, the book offers a light-hearted introduction to the world of Data Science. It is by no means exhaustive, but it covers an essential spread of topics that are foundational to most Data Science problems. Focusing on a highly conceptual delivery, readers will be able to understand the concepts with ease, even for the non-technical layman without a relevant background. Certainly, this book gives you a good starting foot toward your plunge into Data Science.
Reading this as an accompaniment to a deep dive In to the study of Data Science has proven to be a great choice. Reading book has made it so much easier to follow along with the sometimes confusing explanations by other authors in this field. Excellent book!
Data Science / Analytics is a rather complex subject matter. It is tough to describe to a layperson what it entails. Annalyn and Kenneth did a good job in "translating" the difficult subject matter to bring across the key essence of each part of the entire data science / analytics work flow. A very good piece of work. A must share, especially with my staff and bosses.
Authors had tried to present the concepts of ML in a concise manner with examples and they have succeeded. Would recommend this for all Data Science beginners.
For one without any knowledge and background in data science, this book provided me with a very clear and digestible understanding of the topic. Absolutely love the diagrams and figures included that gave visual representations of each concept. Would definitely recommend this to anyone who are looking to learn more about this field of emerging importance and emphasis!
This book explains key data science concepts in easy to understand terms. No technical terms to clog your reading so it all just flows very well. Even as a professional data scientist, it pays to have ways to explain data science topics to non-technical people and this book provides plenty such ways.
This was a great introduction into data science and artificial intelligence principles and how you can apply them to optimize different outcomes. Fantastic reference material.
For someone with a math or science background itâ€™s a good concept review. Also gives non-mathematicians a great introduction to data science.
Excellent intro to ML. Goes into enough plain English description of relevant techniques to help you understand the mathematics which you read about in more technical books.
Does not even try to go in depth. Very cursory!
Very good introduction to the algorithms. Easy to understand and choose the correct one. Loved random forest, a collection of trees
Great high level overview of techniques and their capability/limitations comparison. This is a gentle introduction to Data Science.
The book title exactly describes its content.  And no math involved.
The book is really what it's described as: data science for the layman, without any math! I'm pleasantly surprised at how accessible the concepts in the book are, as the authors have done a great job in condensing the wealth of information into very easily understood ideas.The book is well-written and edited, and the illustrations look amazing and work well together with the text. The examples chosen made it easier for me to understand the concepts.As someone without any data science background, this book was definitely a great read for me! Even for someone who has experience in data science, I feel that after reading the book, it'll be easier for you to share the subject with your friends through the simplified concepts and relatable examples. Would definitely recommend the book!
Love this book they made things a lot easier.I hate writing reviews. Have a good day to all.Mick
Very, very informative.
Easy read for non-experts, but too superficial.
Able to get a top quality background within a short period of time
I used the book in a class taught by one of the author's J. Saltz. The book builds on basic concepts about working with information, and adds tools and concepts. By the end, as promised, the reader is able to perform introductory data science on approachable datasets.
Loved the conversational tone, the step by step approach, and all the examples. This was a great introduction to data science.
Easy to follow and well written, develops content through practical R examples.
This is a nicely written book for beginners who want to learn about data science.  When I say beginners I mean a smart high school upperclassman.  The writing is not mathematically challenging and the examples are easy to follow.  That said, the content left me scratching my head.  The problem was not that the writing was bad or the topics were hard but rather I don't know why the authors included it.  For example, early on the authors talk about binary representation.  That is sorta-kinda useful but not as a core feature for a data scientist.  Other oddities include the coverage of the central limit theorem and law of large numbers.  I teach university level data science and biostatistics and I thought the explanation was nicely done but again I found myself thinking why are they taking the space to cover this as the core part of a book on data science.  There are many interesting chapters (like the coverage of database connections and shiny) but so much of the material is barely an appetizer not even a light snack.  While it is good to make beginners aware of things that can be done, the reader is only able to break the surface of some very deep waters.The code is not bad but it could be better.  The typesetting on the code blocks could use work.  The large font makes it easy to read but the way it wraps makes it rough to study and it causes it to not really conform to the popular R styles (like Google's).  The choice of R packages is okay but it could be better.  In particular, *many* data scientists work extensively with the tidyverse packages and they do data manipulation using dplyr.  While bits of the tidyverse are presented it does not get enough attention and the lack of dplyr support is a very bad oversight.  Basically the code looks closer to 2014/2015 then 2017/2018.If you are a reader starting from zero then this is not a bad buy but if you have any data manipulation experience start withÂ R for Data Science: Import, Tidy, Transform, Visualize, and Model Data.  It is superb and free on the web.
I've read four Data Science books this year, and this is my favorite so far. I particularly enjoy that each chapter starts with a brief philosophical lesson, story and then ties them together thematically with the lesson (Saltz may have invented the Harold of technical writing).R is a bit of a mixed bag. If you're not a programmer, you'll probably love it. If you are, well my internal dialog went something like, "@*#(&*$KHUSDFKJ@!!!! Another 'language' to learn!" Luckily, R is more of an overgrown statistical package than an actual language.This book is short, precise and to the point, and I wouldn't hesitate to recommend it.
This is definitely an intro book - it's not poorly written but also not the best on the subject.It uses R, which is an open-source, free statistical software - which is great - but R has some nuances that can be difficult to pick up, and those are not really covered here. As a practicing erstwhile data scientist, it's really hard to leave out statistics from the work (without fully understanding the data, the biases in the data collection and reporting, what's missing and what's an outlier: valid or not?) people can make some really misleading and wrong conclusions while exploring data in the name of "data science." So it would be good for some practical tips of how to approach new datasets for exploration, recognizing that not all datasets are going to be cleaned and curated for the data scientist. Having recently mentored a student who didn't know how to recode variables, that's a key component that should also be covered. It would improve also by following some of the O'Reilly format approaches for presenting code in a textbook.So expect to dip toes into the water but this book is very solidly in the shallow end of data understanding, exploration, and data science.
Stats is hard to learn and most introductory courses and books aren't good at teaching it.  As a data scientist it's taken me many years to understand it.  I have taken it as an undergraduate, taken it as a graduate student, and audited a class taught by a friend.  I have several books on introductory stats, introductory data science, introductory stats with R, etc.I could have skipped a HUGE amount of that effort had I had this a decade ago!  The authors clearly have experience with stats and with the challenges students face in learning it.  Rather than simply teach statistical theory, book starts with the practical process for exploring and analyzing data.  It then implements that process with simulation based statistics explained with practical examples readers could potentially repeat at home.  Finally, how to do all the calculations including a modern (tidyverse) process is shown step by step.Just that would be enough to make this the go to stats 101 book, but it goes beyond.  The book is written at an incredibly accessible level.  The book regularly repeats and references itself, helping ensure readers learn the statistics terms within rather than being confused as the term is only defined once and repeatedly used without re-definition, (a common occurrence in other books and classes).  It provides multiple practical examples at each step.  It even provides theory-based frequentist examples to help people understand the relationship between such theory based approaches and the main simulation based approach.  I can say I better understand theory-based statistics now, even though I focus on simulation-based statistics.Finally, the authors have created excellent tools to go along with the book. the `infer` Rstats package in particular provides a simple and clear way for readers to take what they've learned and implement statistics in the their own tasks.  The package is designed to enforce statistics concepts simply through it's process so that the effect of the book will live far beyond reading it.All in all, this is the stats book everyone should start with.
I got into this book primarily to beef up my R and Tidyverse skills - and the book delivers. It's a very solid introduction to exploratory data analysis. Statistics education should be very closely intertwined with what a data scientist / analyst does day-to-day which is data wrangling and preparing for further analysis.Emphasizing the Tidyverse upfront for quick wins was a smart choice by the authors. Anyone who has used ggplot2 can attest to its visualizations capabilities over a point-and-click GUI like SPSS.The book simplifies the various statistical tests possible by presenting it under the general linear model focusing on regression, with the implication that t-tests and ANOVA are special cases. The ModernDive and Infer packages, of which the authors have developed and use extensively in the text, significantly streamline bootstrap re-sampling and subsequent creation and visualization of confidence intervals (and p-values). I appreciated the equal airtime for confidence intervals given the known problems around p-hacking.Sampling, Bootstrapping and Confidence Intervals are where the book really shines as it introduces simulations available with a statistical programming lang like R - - this was simply not available my first go around with SPSS.In the next edition, I'm hoping for an expanded discussion of trade-offs, if any, between re-sampling with vs without replacement and a discussion of bootstrapping strategies within the context of other sampling methodologies.All in all, I think this should be everyone's first course in statistical inference.
After having run the gamut of tech interviews in the San Francisco Bay Area for a couple of months, I became intimately familiar with the most frequently asked software engineering interview questions.  Those questions (and many more) are outline in Kal's book in a very easy-to-understand and sensible way.  I highly recommend it for anyone who is studying seriously to be competitive in technical interviews..
It has been useful to me, but it definitely could be better. The solutions are very terse and don't help you much if you're too dumb to understand.
First, I'll say I've only looked at the first chapter (CS fundamentals) and second chapter (prob/stats) up to this point. There are seven chapters in total.==== PROS ====You can use this book as a way of sampling different concepts to recognize whether you're familiar with them. Some questions essentially serve as flashcards, e.g., "What is X concept in [comp sci / stats / ML]?" This can be useful. Even if it isn't in "flashcard" format, you can treat it that way (e.g., "okay, this is asking about the binomial distribution...maybe I'll review that later").==== CONS ====The main issue is that *many* answers to the math-y questions are incorrect.Here's the hit rate as I'm working through chapter 2:2.1: This question is famously ambiguous and deceptive (there's a full Wikipedia about it being a "paradox"), so the answer key giving a one-liner for the answer without addressing its ambiguity is strange.2.2. Fine2.3. Fine2.4. Incorrect...and it's also a brainteaser that isn't related to probability or stats. The question is well-known, and other information needed to solve the problem. This isn't caught because only a rough sketch of how to solve the problem is given as the solution.2.5. Good. Well-explained.2.6. This asks for a generalization of the previous question and is much more complex, but the answer (if it's correct) is given as a one-liner. Explanations can be found online.2.7. So simple it doesn't seem to fit with the previous questions.2.8. Good.2.9. Brainteaser that has nothing to do with probability or stats.2.10. The answer is correct, but the math getting to it is incorrect. There's a missing term on the left side of the only explanatory equation.2.11. Incorrect. You can google the answer. (It was used as a question on a midterm exam at CMU and is also correctly solved elsewhere on the internet.)2.12. Has missing information in the question that isn't addressed as explicitly as it should be in the answer.2.13. Incorrect. A Bayesian stats question where some of the information needed is not given. The answer therefore doesn't make sense.Also, the computer science question quality is inconsistent, and code answers are given in C++ instead of a more common (and less verbose) data science language. I'd definitely use "Cracking the Coding Interview" instead of this book for coding stuff---although the flashcard-y questions still might be useful.
The book goes through all the topics that might be covered during a data science interview. Topics ranges from basic computer science and algorithms to statistics. An ideal book for computer-science guys that want to get into the data-science business, or for math-guys that want to know about the kind of programming skills a data-scientist is required to have. Definitively recommended!
Good prep materials for tech interview, but some answers seems wrong.
An awesome collection of typical interview questions and topics. Itâ€™s a wonderful book to go through the night before your big day. I especially liked the section on statistics, for many of the often asked trick questions are included. Itâ€™s a great boon for anyone preparing towards a career in data science.
The book consists of all interview questions in the beginning and has solutions in the back. I can't comment on interview questions, but the solutions are sometimes very short without much explanation or incorrect. It may be a good reference book for the night before the interview.
I found this book very useful for gauging the breadth of my knowledge and finding holes. However, as many other reviewers mention, the book is full of mistakes. There are lots of grammar mistakes, but more importantly many of the answers are incorrect (conceptually wrong, not just typos/careless errors). Personally, I find it fun because I can play spot the error, but you definitely need other sources of information to actually learn topics you haven't seen before.There are also a couple bad questions (either unclear or demonstrating the questioner doesn't really understand), but I think you'd expect that from an interview as well, so that can actually be useful in practicing how to respond.
As Data Scientist interview covers a broad spectrum of topics (and most of us don't work in all at any given point), I was looking for a book that briefly covers all topics for interviews.This book turned out to be a great find.I found the book well written, easy to digest and informative. Answers are clear and helpful.The book covers most popular data/AI topics.Questions on Computer Science Fundamentals (Chapter 1), Machine Learning (Chapter 3)and NLP (Chapter 5) turned out to be the most relevant for my interest. Unlike other similar books, this one has no gotcha questions. Highly recommended.
The book provides a decently organized but VERY comprehensive collection of the majority of the DS questions that you'd get in an interview. The book does a good job of covering MOST things, which is the greatest value of the book -the comprehensiveness, hence my 4 stars. One downside is that the answers are inconsistent in how helpful they are, and some of them are flat out wrong (as others have noted). I often find myself using the internet to further supplement the answer section to bolster my understanding. Also, the CS coding answers are in C++, if that matters to you. Python would have been preferable for me.
Many inaccurate answers
I don't find this book by no means sufficient enough to prepare you for data science interviews.There is no depth in the answers. Often the answer to a probability problem, for example, is just a number without any details about the thought process.I also find some answers to be at least vague, if not wrong. If for every question I have to stack overflow the answer in order to understand it or verify it's correct, then definitely doesn't worth my 50$.Finally, just a heads up, all CS examples are in C++ (not sure if it's mentioned somewhere in the description, but was expecting Python given that it is specifically for data science).
Heard in Data Science Interviews is a must have resource for data scientist worldwide. It's well structured, with samples of questions you will get in almost any interview. Having the answers in the appendices helps the reader think about the answer before looking at the correct answer.
Good book for quick reference
The book has errorsI need a refund ðŸ˜¡How can one write a book for interview qns with so many errors?
Might be a good selection of questions, but the solutions are inaccurate.
I find this book useful for data science interviews.
Great content and the largest collection of Q&A from Data Scientist/MLÂ engineer interviews I have ever seen. It seems Kal has carefullyÂ selected questions that are popular among interviewers today. The bookÂ covers the field in sufficient breadth. The answers are concise andÂ to-the-point, so you may need to study additional material fromÂ references for deep-dive into specific topics. I found code snippet inÂ the answers very helpful. I would say this is a must-have book toÂ prepare for your Data Science interviews.
It is a great book for Data Science and Machine Learning interview. It collects lots of Q&A for interview preparation, covers from CS foundation to Big Data, Machine Learning, NLP, etc. And it puts the questions into the different categories and makes it very efficient to prepare the interview according to the job responsibilities.Another feature that I like is it separate the questions and answers. It does not put them at same page, it can help me to think about what I will answer the questions before I check the answers given by the book. In this way, it helps me to find out what is lacking from my mind set for the interview and even helps to polish the thinking of interview in my mind.Great book, highly recommended, it worths every penny of it.
This book is an excellent reference manual for anyone doing technical software interviews, even if data science is not the reader's speciality.  The author gives excellent questions that are relevant to just about all programming interviews.  The questions really do challenge the reader and the author's answers are clear and to the point.  I would highly recommend this book be read by anyone seeking a role in a computer-related field.  I have had many of these questions asked of me in interviews and I've never seen such a thorough compilation of questions all in one place.
This book is well worth the $50 and your time. If you're applying forÂ Data Science jobs, it really distills all of the uncertainty aroundÂ interviewing at the top technical companies. It has excellent overviewÂ of what to expect in the interview. The problem sets are invaluable. InÂ my experience, even if you do just a handful of exercises it gives youÂ some confidence going into the interview. This is a serious book withÂ rigorousness in details and answers that you wouldnâ€™t find online. Two thumbs up!
Mindbogglingly diverse collection of questions. This book can be your go to thing to brush up all your concepts a night before your interview. The section of the book having questions from computer science might throw a non computer background folk out of context, but then if you are interviewing for a non coding heavy role, you always have the option to skip this section. The rest of the sections are very useful and cover questions from different styles of interview ranging from behavioral to technical.
Great book. I purchased to study for the Dell EMC DECA-DS certification, formerly â€œEMCDSAâ€. The book uses mostly R code examples which are great and understandable, but it did require me learning a little bit of R to fully comprehend. I come from a Python background and would have preferred more Python examples. There is very little Python based examples, mostly just for text mining.  The book does get into Hadoop and related tools, but really at more of a theoretical level or basic overview.
Some pages, where it has code, the print is not as clear as it should be, maybe I got a bad print but its hard to read the codes. The first picture shows code font that has shadows (it looks better on camera than in front of you). the second is a picture that although is for an overview its just messy. the third pic is from a code line that has no problemContent wise it seems like a good book 3.5/5
Good but this stuff gets depricated so quickly unless this books gets updated every six months it is better to follow online courses and material. Most basic stuff of course still holds but rest is outdated.
I like this book
One of the best entry level books on big data analytics.
Excellent book for completing the Data Science and Big Data Analytics Dell EMC Proven Professional Course. It is an excellent reference for any Data Analytics project
This book is a great one. I found it easy to read.
This is a pretty decent book that covers a broad spectrum of topics including the the analytics life cycle, a brief but concise intro to R, to covering most of the advanced methods for analysis (k-means, linear regression, Naive Bayes, etc). It doesn't shy away from the math - but doesn't overwhelm either and, for the most part, is a fairly easy read. It should be treated as a reference and as a guide for the EMCDSA (EMC Data Science Associate) certification. There are probably better books for those just wading into data science but definitely worth adding to your library. For those who are complaining about no companion code examples it was an easy download from the Wiley website.
I bought the book as soon as it was published. At that time I was working on a Documentum project ( an EMC software)  and I was struggling with their documentation. I hoped that the book will much better , but not the case. Nevertheless, I started reading it and of course I wanted to test the code, some of the data sets were the ones in R, so it was easy to replicate, But for the others I checked the web site of the publisher and what a surprise....they aren't no where to find. I wrote to EMC, but they were no in mood to provide them. They were on the opinion that it will be provided only to associated something.... On my opinion a tech book without code and in this case no data sets is just useless. So I just left the book on my book shelf. A year later I just checked the book here and what a surprise you have the code and the data sets! Good! Let's check them. This year I going to teach R and I really hope that the book will be much helpful for my students.
Excellent book. I have used it for teaching a graduate course in Analytics.  I wish there were more examples on big data.
bought this used and its in great shape. its a typical tech book so kinda dry but informative. Will be taking the certification exam after this.
good book and fast delivery
It is a great book. If you are new in data science this book is a 'must have'.
The content is clear and easy readable. Although I was hoping for some more topics on Big data
good book, give overview and comprehensive
You may bring a book and find it disappointing, it is not a problem for most of us since we may find others very helpful.This Book is very disappointing in many points among them a strange point that I will draw your attention to.I am not happy to give a low rank to a Wiley publication like this book, since I appreciate Wiley  so much.This book is about Data Science and analytic and It has examples that make use of data files and it is applied science not theoretical only.WHERE ARE THE DATA FILES? I went to the web-site to find the companion files like any normal book -some free books have companion sites that they put related code and data , and this book doesn't have?!!!!- but I didn't find anything except an instructors only area , God knows what does it contains.Assume it contains the relevant materials and you reveal it to instructors not to the public, why you didn't say so?If this Book for Instructors Only, Why you didnot say so?
This buy was amazing.
This book has really helped me delve into core competencies during my Data Analytics degree program. It seems like that whenever I run into conceptual issues (life cycle, etc) there are some great tips and information in this book to reference. I've used it many times on various projects and assignments to get my head around things. The exploration of the DAL is really in depth and worth the cost of the book alone.
Great Book and details are easy to comprehend. However, can anyone let me know how to get the answers to the questions at the end of each chapter?
Up to date summary that covers a broad spectrum of topics at the right depth. a Great primer
I spent about 30 minutes trying to locate the data files for the examples so that I could run them in R while I was reading.  After these 30 minutes, I gave up - very discouraging, although I liked what I was reading.  I have the feeling that this book is used for EMC certification training and if you pony up the money, they will supply the data sets.
I agree with the reviewer. I could not find the data sets to practice. Without practicing the information in the book is useless.
Can I pass the Data Science Associate (EMCDS) Exam just by religiously reading and practicing the contents of this book?
Concise and easy to read. Simple enough examples.
Just finished this wonderful book and would definitely recommend to intermediate R or Data Science professionals. This book provided me with new insights on how to common data science tasks.
This is the exact practical R & ML book I have been looking for! It was written by two renowned data scientists in industry. Best in the class! Besides, it has been used as training material for internal ML training classes in tech companies in the Silicon Valley.
The book is great. the kindle edition i received doesn't have any figures and all formatting is off. At least check before selling.
Unicorn project is about agility, discipline for agility, focusing on value proposition, data, and constant learning.  Having read books such as 'work rules', 'excellence dividend', 'high output management', and 'complications', most of the fixes mentioned in the book did not seem novel.  Nevertheless, it was a fun read that talks about highs and lows of technology and management in the modern day workplace that is being devoured by software. The highlight for me was the politicking and the rebellion cos imagine if these did not exist :-O
Yet again, Gene Kim challenges how we perceive legacy IT - in this case from the perspective of developers.  Written as a narrative rather than a technical novel, it conveys the concepts of The Five Ways.  Written from the viewpoint of application development, it speaks more to the product and application side of IT than The Phoenix Project.  This may be more challenging to some within IT, but that is a good thing - this book should help us share the challenges almost any organization has, and how to look at them in ways that create solutions.  This book is filled with more concepts and stories to challenge thinking and should inspire many about ways to evolve their organizations.
This story is so packed with golden nuggets that every IT shop can learn from. In order to get the most from this book, look up the references and read them over and over again. There is a growing body of knowledge in the areas that are covered in this book like team structure, communications, automation, and business strategy. All of it comes together masterfully through the fable of the unicorn project.
Definitely a required read for both ops and developers on how software can transform a company, the book recommendation list at the end is also great, like the Phoenix project it was a very engaging story.
Even more than the Phoenix Project, the Unicorn Project immerses us into the pockets of greatness that exist within every company and organization.As has been stated for TPP, I read this and I asked multiple times â€œhow did you know? How did you know thatâ€™s what weâ€™re dealing with here?â€  Well done Gene Kim.
There are not many books capable to give a near-real experience of the business of the development software.Well written, kind of catchy.Like the Phoenix Project (from the same author) it describe many typical problem to design software and possible solutions.The book of course is fiction but the solutions proposed can be real.
The author does a good job laying out the five principles of running a progressive organization; useful stuff. However, some of the engineering details work out far too conveniently.
Nicely written. Total fiction. Too many non-fiction books that blow this away
A great story and frighteningly close to so many organization I have seen. If I didnâ€™t know better Iâ€™d say he was familiar with several team members Iâ€™ve worked with through the years. I suspect people are the same the world over though. But not just a great story, the real life organizational processes he weaves into the story apply to many organizations today.
Having just finished the unicorn project and having read the Phoenix project I have to say that I am not thrilled with the Unicorn project. It bounces around naming technology a lot but I don't get the real take away. I will definitely read the Phoenix project again, as every time I do I get inspired. But I cannot say the same for this. It just was possibly too unbelievable, entire databases dreamed up and implemented in no time, apps rewritten in hours, just pure fantasy. Not that it had to be realistic, but everything going perfect all of the time... It just lost me.  And I understand most of the problems described.I will not be recommending this book to my team.
Really good book on improving the DevOps experience. I think the first book, the Phoenix Project, was slightly better, so Iâ€™ve given this book one less star, but itâ€™s still a great read!
Fantastic book that is eerily close to real-life situations. They should also classify this book as a â€œthrillerâ€ as I found myself scared to read what happens next in Maxineâ€™s plight to complete a Phoenix build. You do not need to read Project Phoenix for this to make sense, but it is nice to have that as background information to understand the parallel efforts from IT. In my opinion, there needs to be one more book to complete the story. Gene, feel free to hit me up as Iâ€™m happy to help tell the third and final saga to this epic tale. Kudos to everyone involved in this effort as I can imagine massive research went into creating this timeless classic.
First disappointment with this book is when you realize that the plot is actually based on the Phoenix project. Yes, this is the same plot as in the previous book, but from the perspective of a developer. The previous book was full of twists and turns, while this one is mostly linear. If you have at least a superficial experience in software development/IT you will find the events highly predictable. The Phoenix project had quite a few Aha! moments, this one has none. The best part of the book was a joke about a QA manager: QA engineer walks into a bar. Orders a beer. Orders 0 beers. Orders 99999999999 beers. Orders a lizard. Orders -1 beers. Orders a ueicbksjdhd.
I have been a Developer, Engineer and Project Manager in IT for the last 20 odd years. I have been fortunate enough to have worked with and for several Tech Giants. The names and places change; but, the story remains the same. Why is it that most everyone can relate to "Dilbert?" Alas, I rest my case. The Unicorn Project, is stellar on a variety of fronts; unlike the "Phoenix Projects" it covers the whole gamut of a Corporation struggling for a foot hold in an ever decreasing market.The author did an amazing job of capturing the different "political" motives, even to the level of the Board and really demonstrated how little input CEO's have when Board Members go rouge. The book is not an easy read and I would say that it was almost frantic; yet it had to be. I could relate personally with most of the cast (Dev, Eng, PM) and the solutions were more than Ideals or Steps. We've all seen these things and been told they hold the key to success. (If only the whole organization would follow) As stated, this is an amazing book and both the "Phoenix Project" and the "Unicorn Project" should be mandatory reading for everyone within an organization, regardless the size. From Peons, to Managers, to CEOs, CFO's, COO's to Board Members. Dilbert will live forever if the lessons in this book are not learned and learned well.
The power of story in teaching technical concepts is highly underutilized. Gene Kim & friends achieved a great feat in introducing new people to DevOps in The Phoenix Project, which is a great book about business struggles in digital transformation (from the business' perspective). They achieved even more in The Unicorn Project when they flip the story on its head and tell it from IT's viewpoint. The characters are infinitely relatable and compelling and probably most importantly, tied to real world events/IT stories so that they are grounded in reality.This is a super fast read in part because it is a fictional story. But don't let that fool you. The book is worth reading again to digest some of the great wisdom imparted at key moments. The book is so instructive that I plan on including it in the curriculum for the software architecture course that I teach at a local university. Well done, Gene!