laptak2003/AmazonDataScience huggingface dataset:
Let me say at the outset of this review, that I respect the author and the sheer guile of trying to attack what is a Ph.D. level topic, in a single book. I have been an SW engineer for the last 15 years, have an engineering degree, and have done some research into GA's and image segmentation, many years ago... and I found this book challenging.When I was in college, the joke about Comp Sci was that it was really just "applied math." That wasn't really funny, but it is gosh-darned true, doubly so when dealing with data science, AI, and ML, all of which are fancy words for "applied statistics."So, the book gives you a background in various topics such as stat, probability theory, linear algebra, programming, and touches on a few topics from calculus. If you're unfamiliar with those things, you will not be able to learn them from one book, or all at once. It's just too much.The author leaves lots of bread-crumbs for you to follow. If you're not strong any of the subjects I've mentioned, plan on reading some of the books he recommends (most are online).After that, the book is a solid tour of data science, and surprisingly, there's some material on AI/ML. All of it is well done. The only criticism I have is, I would have liked more equations alongside the code. I know equations scare people, but this is "Data Science."TLDR? This is a serious-minded book for serious people. There aren't any easy answers, but it's a great education.
Its Capitalism at its best. I read the book from my nearby bookstore and like any other reviewer, the book is grayscale. Which I think is totally unacceptable given the price. I bought R for Data Science before which is 3 times cheaper than this book and its colored for charts and the codes. I mean, come on OReilly, why all the latest version is priced at $69.99? Oh I know, its because Amazon pricing algorithm will certainly reduce the price to the original intended price they want which is around $30.Its not wrong to make profit, but when its grayscale, and the paper feels cheap. Its certainly Cost savings and selling at premium price, no doubt the content and the author did a great job, but hey, people post pirated stuff all the time, if you make the book so expensive, dont blame people for going on piracy. Its not that they dont want to buy, its because the price is too high for them to even support the author. Again, its because of the cost savings, and idk what kind of pricing Strategy OReilly trying to do. But I'm upset at the book quality.
If you're looking to get started with data science but you're confused on which material to use...videos, books, a specific online class, etc...start with this book and see where it takes you. This book will give you a great place to start and afterwards, you will have enough knowledge to make an educated choice on what educational resource to use next.
In general, this is a good book. It covers a wide variety of topics, and it goes into enough detail that the reader should have no trouble implementing the basic concepts, without going into so much detail that the reader is bogged down in minutiae.That said, there certainly is a presumption that the reader is ready to tackle the subject matter. It's assumed that the reader has functional knowledge of Python, statistics, and data concepts, generally. I don't necessarily agree with the other reviewer that it's PhD-level material, but the reader probably should have an undergrad degree in math or CSci to fully appreciate this book.
I was an academic data scientist decades before the term was coined and I love this book.  I started with SAS and S (R's mother) 25+ years ago and along the way I picked up a little Python (as a general programming language) but I never used it for data science before this book.  As somewhat of an expert on statistics, data management and visualization, I am impressed by both the breadth and depth of coverage, especially given that this is under 400 pages.  The writing is clear from start to finish but what really shines through are the links to other resources.  There are places where I would have loved to see more references to major statistics books but what is suggested is good.  If you are starting down the path to data science and you want to do Python instead of R, this is an excellent place to start.
The book has a nice approach to covering basic statistics encountered in an introductory data science course and includes Python exercises (and you really should know some Python to start) to help you write functions to apply these methods.  By covering both statistics and providing programming exercises, a reader will find it easier to move to more advanced statistics and programming.Highly recommended! (and black and white is just fine!!)
This might be the most important data science book I have ever read.  I picked it up to go alongside a (very expensive) course I enrolled in.  The course's information ended around Chapter 4 in this book.  Great book, thanks Joel Grus!
This book is suitable for people with basic python programming skills. It is very good for beginners and advanced users alike. The codes are very clear and without errors. This book teaches you the basics and introduce some expert level topics for you to explore further if keen. If you are a novice data analyst and some harder topics throw you off, you should probably revisit the topics after you have gain more knowledge on data science.I highly recommend this book as your first book into data science because the codes and thought processes are very clear. 70-80% of the book are data science foundation and basics for you to tackle harder topics later.
Specifically, the probability notation is a mess. Varies in Point size for no reason.Concepts covered arenâ€™t complete, even for an introduction.
With all the books out about Data Science this is a great fundamental book. It surveys the material broadly and in detail. It really helps the non-Python coders.
I teach intermediate business analytics and this book is a perfect way to introduce people to data science starting from the math.
I began reading this from the library, but I quickly realized that I needed a copy for myself as a long term resource. Data science is a growing field and it serves any developer or analyst well to have knowledge of the resources and workflows available. Excellent!
this book touched very little about widely used python machine learning libraries like scikit-learn, tensorflow etc.besides, this book mainly use list or tuple in the demo code, instead of using the NumPy's array is a drawback to me.
Before picking up one of the tomes on scikit-learning or tensorflow and if you're new to data science principles, then this is the right book.  There's some math but, if you made it through engineering school it won't be terribly difficult.  Really good on the concepts.
This is an excellent book for anyone interested in data science or machine learning. It covers a broad range of topics, has very clear explanations, and examples.
A good book for starters. It should definitely have color images.
Apologies to the author for the low-rating.  After purchasing this book, I expected color, not gray-scale, used in the (many) visualizations, especially based on the content.  Color is important to understanding the material.I'm not sure if the author had a say in this, or this is O'Reilly's doing, but I would respectfully suggest that if leaving out color in the visualizations was intentional, it's not the way to go.Based on this, I regretfully suggest that you DO NOT purchase the book.  In my view, the book is significantly diminished because of this omission, and I think you'll agree, especially if you purchased at full price as I did.Does the Kindle version have color?  Not sure.  I wish I could still purchase PDF's, but that was closed some time ago.I'm starting to think this is my last O'Reilly book purchase.  Perhaps the intent is to move us all to Safari subscriptions.
I was shocked to see that the new edition of this book had a one-star rating--but it turns out to be a single guy whining about the book being in monochrome (as almost all physical books are). As for the actual content of this book, it is fascinating and useful. Instead of teaching you how to use high-level libraries like Pandas and Scikit-Learn, his approach is to teach you fundamental data science concepts by having you write simple functions yourself so you can see what is happening "under the hood." I found this to be pedagogically brilliant and walked away with a much stronger understanding of fundamental concepts necessary to understand data science and machine learning.
The author is clearly technically competent but not a great teacher.  His instinct is to write code as concisely as possible at all times, the coder equivalent of throwing lots of SAT words into their sentences to impress everyone with how smart they are. I bought this book to learn data science, not unpack one line code snippets that do 8 things at once.A better book would focus on the student's learning, with less emphasis on how clever the author is at writing code.
If you need a good broad brush to learn from, the second revision (in monochrome)  is the book for you!Yes, there is numpy, pandas, and a host of other packages and frameworks available to perform many of the examples of what is explained in the book.  But you need to broaden your knowledge with this material that touches the "bare metal" of Data Science.Excellent use is made of clear, concise verbiage to make things "black and white".  (save the color images and other crutches for the board room stakeholders!).
This is the first book to finally succeed Segaran's Programming Collective Intelligence. It always teaches concepts with real world and plausible examples. The author's humor is subtlety felt through out the book to keep things entertaining.
Let me start this review by explaining clearly who this book is for: anyone who has had some form of introduction (even if concise) to programming in Python, algebra, statistics, and probability will find this book a great introduction to Data Science. While the author does a great job at having a crash course on these topics (and I even learned a thing or two here and there), I can see the contents being a bit overwhelming if this is your first point of contact with these subjects. However, should you meet the requirements I mentioned above, you'll find this book a breeze! Joel does a good job at explaining the topics using his signature brand of humor, keeping the read entertaining even in the most advanced areas.  I'd even say that this is a must read if you are considering going into machine learning, since it teaches you a thing or two in the topic as well.  Please keep in mind that the book is monochrome. If that bothers you, consider viewing the electronic version.TLDR: If you're looking for a concise introduction to data science and have a bit of knowledge of basic Python, algebra, statistics and probability, look no further than this book! Otherwise, come back once you've picked up those tools and you'll feel right at home :)
It appears to be a good book. I believe if the author clearly mentions that he is assuming the reader already has some basics in Python, the reader/customer will be able to make a informed purchase decision.
Bought book today to realize that it's all grey scaled and not even a single colored page. Besides, print quality is poor and text reads blurry. I should have read other reviewers comments before buying this book. I like author's writing but publisher should have been clear about b/w print on poor paper quality. Wish there is an updated version.
In my personal opinion, this is a book to bridge the gap between an experienced mathematician/statician and practical machine learning. The book is more focused on describing how to implement mathematical formulas in Python than to actually explain the math behind it.I am a proficient Python Engineer and I can read the code and understand what is being done, but the author makes no effort to explain how it reached to that conclusion, or why it matters. The author does implement the mathematical formulas in Python skillfully, it misses the point of the book though.Another big problem with this book is that it assumes you can learn mathematics by just doing mathematics without understanding the why. It is frustrating to read and follow the author implement mathematical formulas without explaining why. I believe this is the case because the author DOES assume you have the required math to follow the book. I believe the author should add a section in the preface that list the prerequisites for this book:- Linear Algebra- Statistics- Probability- Vector Calculus- Continuous OptimizationAbove all, it is a good book if used as an index on where to start to understand Data Science, but it definitely doesn't fulfill the promise of being "from scratch". From scratch IMHO means you dive into the internals of Data Science algorithms. I had the expectation that this book was going to be more like "Designing Data-Intensive Applications" for Data Science where the "why" is as important as the "how". Data Science from scratch is a book about the how, with no effort to dive into the why. The book does provide the vocabulary for me to discuss Data Science with practitioners, but I didn't feel it got me any closer to becoming a practitioner myself.BTW, the fact that book is monochrome doesn't matter the font and figures are very clear and readable.
The Python review highlights those features important to data science principles.  Later in the book he covers topics important to data science. The review on statistics and probability was helpful for me.  His disciplined approach may be the best lesson.  There is great power in data science.  With that comes great responsibility (quote from Uncle Ben).
Best overview of the topic that I have read.
Simple and in depth
Very good overview of a broad field.
Good DS overview for beginners
The book is a beginner review of the main tasks of data scientists.
I do research in the area of information technology applied to knowledge management. I needed a frame of reference to better place my particular efforts.within a wider and unifying whole. This book has given me an excellent view of the state of the art and a complete taxonomy of Data Science components and processes. This book is a must for anyone dwelling in the post analytical post transactional use of data.
This book is reasonably well put together, and provides a mostly non-technical view of the field of data science.Who would I recommend this to?If you're in the field looking for reading material, this probably isn't going to be a go-to. If you work with others who have roles in Data Science, this will give you a decent understanding of the broader aspects.How's the writing?From a linguistic perspective, this book has relatively few hard errors, most of which do not take away from the content, but exclusively uses the word data in the archaic plural form, as in "the data are". If you prefer the colloquial use of the word (As a collective singular), the text might irk you a bit to get into.Extra notes?Towards the end of the book, it presents a pretty well intriguing set of ethical concepts, and does a solid job of making you think about it. I'm not convinced this will ever be a classic work in the industry, but it at least keeps up with it.
Fantastic book that operates in the semi-technical realm to give a reasonable approximation of the main ideas in Data Science
This is an introductory book. Short and with overall information about the past, present, and future of Data Science.
I bought this book for understanding the term 'Data Science' and its concept. This book provides a perfect and complete understanding of the topic. Must read for someone who is going into the field of Data Science or for someone who wants to explore the area and know what it consists. The book provides a simple and deeper understanding.
This is a good book to understand the map of data science before jumping into the field and learn. For me it gives a map where to go and what skills are required to learn
This book is a great place to start for anyone interested in the field of data science. Good summary of the basics.
As a developing science, this book provides an essential introduction to Data Science, in very simple way, not much of technical or theoretical, but scientifically precise, everyone has to read it
Simple, concise and highly informative.
This presented at a very non-technical level.  Not very satisfying for a technical professional.  I have the CD, the reader is very dull.
Easy to read with accessible and clear examples throughout. Well organised with an easy to follow structure throughout which is well tied together at the end
It is just fundamental knowledge.
This book has everything: it is complete, it is well structured, it is very easy to read ... and it is concise.
I liked it as an intro to the subject, but I expected bigger depth.
Data science is put in excellent perspective in this book.  I think the book is especially oriented toward giving people interested in "specializing" in this field or utilizing data science some good, basic information.  As a multidisciplinary field, and one oriented toward business, government and surveillance interests, generally, it is a field that encompasses and extends into practical areas that its associated traditional area, namely statistics, has not in the past much-addressed.  Data science is an extremely interesting, technical field with broad social and ethical implications explored in this book.  Statistics is only one tool.  The authors lucidly discuss the focus on the huge amounts of valuable, unstructured data.  They point out that to make all of this useful for the goals and purposes of business, surveillance, medicine, government, etc. requires an enormous time investment in putting appropriate data together and extracting information in a usable form.  The discussion of mathematical modeling, machine learning, and the overall use of algorithms is very insightful.  The authors make it clear that data science is not merely "deep learning", despite the fact that the extraordinary advances in using neural nets represented by deep learning is largely responsible for much of the importance of data science today.  There are excellent perspectives of data science available on the Internet, but I think the authors of this book have provided a good supplement for this information in a deeper way.  One of the real problems in picking information out from the Internet is escaping  the "hype" surrounding a subject that is currently "hot" like data science.  This book definitely allows the interested person to separate some of the solid pieces of knowledge about what the field involves from the huge amount of "noise" surrounding the entire area of "weak" AI and machine learning.  I would recommend this book strongly to anyone seriously considering going into this field.  A point the authors stress is that weak AI, namely specialized applications, rather than broadly "intelligent" systems competitive with general human intelligence, has opened up a world of opportunity, promise, progress, as well as ethical dilemmas.  I personally think that data science is a great field for an enormous spectrum of technicians at all educational levels.  The book opens a window a bit on the enormous implications for our future.  It is a good start on the climb to a satisfactory knowledge of this field and its potential.  I especially recommend the book to business executives and entrepreneurs as a useful and insightful view, for developing a strategic picture of this field, that does not get into unnecessarily technical details, and is not subject to the "hype" and "noise" from the Internet.
This book provided an overview of the fundamental ideas and concepts about data science. It is an excellent intellectual tour to the data science. The only problem I have is that without full understanding of a statistical concept of the differential privacy, it is difficult to understand its application to any algorithm.
This book covers core concepts in data science in an easy to read manner. Infrastructure for handling big data and the data science ecosystem are introduced along with Machine Learning basics and some useful concepts at a high level(like CRISP-DM, clustering, anomaly detection etc.). A chapter on the privacy and ethics covers GDPR and biases in algorithms. Overall, a good general introduction.
Well-written and easy-to-understand, this book gives a new-comer like me a conceptual framework to think about problems in data science. It helps me to understand what the field really is and what the workflow of a data science project looks like. Particularly interesting is the chapter on data ethics and regulation. I think it is an area that is often overlooked by technical textbook, but should really be emphasized to readers who might someday become a data practitioner. Overall, itâ€™s a very good book and worths your effort to delve into.
This book is an impressive overview of the DS field. It showcases the history of the fields, then breaks everything down so you can understand growing trends and the practical applications that are taking place. It's a must read for anyone interested in DS.
I am a relatively experienced programmer and have been involved in all parts of Data Science projects (ranging from leading big data processing parts to being an observant in ML parts also actively working with business to understand problems, justify investments and calculate ROIs for multiple proposed ML solutions) and come from a mostly practical background. This book provided a very good overall picture as well as a lot of good references to dig further into.
Comprehensive yet succinct discussion of Data Science . The book is appropriate for those in tech-related businesses, newcomers to the field, and anyone with a curiosity for the subject.
Good overview for the moderately-informed. On to deeper texts!
Book provides excellent overview of subject, offers details where needed and applies corresponding platforms from chapter to chapter to communicate both.
Great book for an executive over view!  Well written and good packaging!
Provost and Fawcett's book is one of the very few in the field that neither condescends nor patronizes the reader as it explores the motivations and machinery behind the most commonly used data analysis techniques in the analytics professional's toolbox. While it stops short of providing detailed instruction on how to use these techniques, it provides the reader a solid foundation for taking this next hands-on step. And for those who are not working directly with data, but are otherwise stakeholders in the use of analytics to drive better organizational outcomes, this book will greatly enable you to understand and add value to the analytical process.---Zain KhandwalaExecutive Director,Institute for Advanced AnalyticsBellarmine UniversityLouisville, KY
This book is ideal for anyone looking to understand data science, and especially those who might interact with data scientists at work.  Roughly half the book deals with the essential data mining algorithms. The focus is on understanding what the algorithms do, not the details of how they do it, so implementation details are omitted. The math is certainly discussed, but kept to a minimum, and coupled with comprehensible, plain English explanations of each algorithm. Each chapter includes a case study illustrating how the algorithm can be used for a real-world problem.The other half of the book (interspersed between the algorithms) deals with issues relating to design, implementation, evaluation, and deployment of models. Without understanding these crucial ideas, the algorithmic knowledge is useless. For example, the right and wrong techniques for evaluating model performance are discussed at length. A businessperson without adequate background could easily be misled by certain evaluation metrics, and the reader is taught to evaluate model performance with a critical eye.  There is also a chapter on evaluating and critiquing data mining proposals, which nicely ties together the algorithmic, business, and practical concepts discussed earlier in the book. Some case studies are revisited in several chapters at increasing levels of sophistication, making the book feel like a cohesive whole rather than a mere compilation of chapters. If youâ€™re coming from a technical background, you will learn a great deal about the business and practical/implementation aspects of analytics. If youâ€™re coming from a business background, you will gain an understanding of what your data can do for you, and how to use it to your benefit. The book is an intense but very pleasant read, even funny at times. Highly recommended!
An excellent resource for the Business Analyst (or the curious executive) looking for a comprehensive understanding of data analytics for business, especially the newer area of massive data/big data for business. This book gives a really solid grounding in both the business (strategic) and data (analytic, technical) aspects of modern data analytics. The authors clearly show that data is the next wave of change and that it will require a mindset change across all business functions--a mindset they call data-analytic thinking. If you need to master/improve this thinking skill set--here is a great place to start no matter what your job title.Provost and Fawcett have put a lot of work into the instructional design of this book--you can follow it down to the technical/mathematical level of algorithm design or just read the content concerning business strategy and general data design and use. Either way, you will achieve a satisfactory understanding that serves your purpose--the authors maintain a conceptual continuity at two or three levels of discourse. Very nicely done and very engaging. Five stars.
This is a great book that gives a top level view of machine learning and data science. It is particularly useful for non-technical readers who need to have a need to know the lingo for being able to follow conversations around the topic. It is also a great introduction for technical people who have never worked in data science before.
The best book I've found so far at explaining the theory of Data Science.  If you are looking to get into data science and don't want to just start plugging away with Python or R then this is a great starter book that will teach you the lingo and the concepts that surround data science.
Excellent overview of many data science concepts! I recommend making a list of key words (concepts, terms, algorithms, etc.) that are mentioned. These can be very useful for making flashcards and identifying topics for further research. After reading the book you of course won't fully understand all of these concepts and terms, but there is significant utility in being aware of these concepts and ready to connect them while working on problems.
This book was good! It shows how to use different machine learning techniques and how to interpret them, how to comunicate and how to measure the value that it could cause in the business. I thinks this book could be better if it had more examples with other techniques that usually are called "Black boxes".
Haven't read it yet, just noticed as you can see in the video that some of the pages are upside down
I read the book in parallel with the coursera course of applied data science with Python of Michigan University.This book  is an excellent complement in order to understand the real power and limitations of this kind of analysis.I really enjoyed the math explanations that are scarce but important in order to understand what is really happening under the hood without being too technical.
Great book for providing a high level summary of data science techniques to people that may not care about the mathematical details. I tend to recommend this to people more worried about building up a working knowledge of classic problems and approaches in data science rather than for those wanting to look at page after page of detailed math related to probability distributions.
This is an excellent textbook on data science. The text itself explains concepts and theories well and provides definitions, examples, and formulas that help the reader understand and apply these concepts. The information presented is well-organized, and the visual aids include ample graphs and charts. Section breaks are obvious with well-designed titles. Chapters are easy enough to read but don't over-simplify important concepts. Inclusion of Glossary, Bibliography, and index, as well as a detailed table of contents, makes it easy to navigate. The only exception our instructor took with the text during my course was their insistence that only the best data scientists should be considered. Removing this bias, the information provided was clear, concise, and helpful for anyone working with big data or in data analytics.
Little to zero explanation of the basic concepts such as objective functions and the terminology for machine learning. The appendix doesn't even match up to pages where you can find this information. Read 3 chapters so far and I have no understanding of the material. Would give 0 stars if I could.
After letting this book sit on my shelf for more than 2 years, I finally cracked it open, expecting to struggle through it. To my surprise, everything in here just clicked â€” as much as Iâ€™d like to think itâ€™s because I have a natural affinity for this topic, Iâ€™m pretty sure itâ€™s due to the authors doing a fantastic job breaking down the concepts into really clear, easy to understand language. Seriously, to all the other business/tech/data science writers out there, you have no excuse now for writing unintelligible, confusing, jargon-y books. Data science really doesnâ€™t have to be this mysterious dark art that some others try to present it asâ€”thank you Foster Provost and Tom Fawcett for cutting through the crap and  dispelling that myth with this book.
Excellent discussion of data science methods without excessive focus on mathematical elements.  These are included at a level that can be understood for the skilled marketer who has background but does not wish to go deep into the math.  The coverage is broad with both supervised and unsupervised methods in data mining.  Topics cover tree models to logistic regression, to scoring.  A discussion of holdout model tests, prediction & validation.  Particular emphasis is placed on how to frame questions to apply to the business case so suitable conclusions can guide business decisions and strategy.  You will get the sense that the authors are battle tested veterans of the data mining business and have applied their creativity to a broad range of business, data and technical challenges.Only two caveats to this book.  First, as purchaser of the kindle edition, I found the equations included in the text were sometimes very readable and sometimes the type was so small as not to be legible at all.  Be warned.  If you intend to follow the math that is included, perhaps the paper edition would be best.  Second, this book does not dwell on the statistical packages that can be used to support data mining efforts.  If you are interested in exploring these methods in practice, you will need to look further.
This book is a great crash course in advanced analytics. The field is extremely wide but this book is an excellent survey of the basics and areas for further exploration. It will not make you an expert but at least you will know what the experts are talking about. It is written with just enough math for the non-PhD manager to understand.
This book is fantastic. it's a perfect mix of high-level explanation and technical details. There doesn't seem to be much to help one actually execute the methods described, but that does not appear to be the author's intent (which is why there is no negative impact on my rating).I appreciated the accessibility and plain English - albeit thorough - writing (from the perspective of a person who is self-taught in data science and sometimes less acquainted with the terminology).
If you are an analyst professionally or academically, this book profoundly and practically relates statistics to relevant business processes and data.  If you are a professional data analyst this book will help you reorder and consolidate your work experience and knowledge while reinforcing and focusing your analytic abilities.  If you are a student of data analytics, this book will accelerate your understanding of the required methods to become a practicing data analyst...and your "customers" will see this competence in your work and your presentation.
Note - I was provided an ebook version in exchange for my review as part of the Library Thing Early Reviewers program.In brief â€“This is a great book for any in the data science field or wanting to just understand â€œBig Dataâ€ or a manager/professional just trying to â€œget current. â€œ  I have a masters degree in software engineering with a data science background and three years experience in a prior job in Data warehousing.  It was a long read, especially with the holidays, but well worth it, and more enjoyable than almost every technical book I have every read.Strengths â€“ Organization, having technical details in a side by side section for those who want it, covering details from definition, through use and application, as well as doing a good job explaining similarities and differences on key topics.Weaknesses â€“ there are a few small places I wanted more.  Meaning if they could have somehow had more examples for the different models, situations, etc., especially as I got into more of the predictive models.
I am a Product Manager, and read 25% of the book. It gave me some new concepts, but the flow is quite haphazard. I don't think this is a book for business people at all. Whatever I learnt, it didn't feel that I can apply that meaningfully anywhere. Rather going through the rest of the book felt as a waste of time. Not sure, if I am missing anything?
My friends recommended this book to me.  I first purchased the e-version, and then purchased a paper one.  Indeed useful.  I especially love the examples.
Data Science for Business is an ideal book for introducing someone to Data Science. The authors have tried to break down their knowledge into simple explanations. I am skeptical of non-technical Data Science books, but this one works well.In the beginning we are shown the motivations for Data Science and what fields they apply to. Some examples include movie recommendations, credit card charges, telecom churn rate, and automated analysis of stock market news. The book avoids going into the highly technical parts of creating the system but gives you links for where to go.They do not really reveal the whole Data Science stack. For example Hadoop was mentioned as an implementation of MapReduce but they said going into Hadoop configuration would be too detailed for this type of book. I tended to agree, and even being a progammer myself, I thought they made the right choice to leave that out.Where the book shines is in the explanations. I am very familiar with expected value calculations and there was a chapter on this. It was a much better high level discussion than I have seen elsewhere, and they mentioned possible pitfalls of the expected value framework.I liked that the emphasis was on deciding what problem to solve in Data Science. The title of the book is appropriate as it is not just about analyzing data, but figuring out the business case. If you are new to Data Science or looking to get a high level overview this book is an great place to start.
I recently made a career transition from academia as a Professor of IT Management to industry as Senior Data Scientist at one of the Big Four consulting firms.  The research skills that I use for academic research are the same ones that I use to help my firm discover data-driven insights that are actionable and transformative for restructuring strategies and operations to drive business value.  I thought this would be an easy argument to make during the interview process, but in the end I had to develop a less academic-sounding argument for the interviews.  Provost and Fawcettsâ€™ Data Science for Business was ideally suited for this purpose.Provost and Fawcett is THE text if you want to learn advanced statistical methods in business-related problem-solving contexts separate from any specific programming language like R.  Itâ€™s also the right choice if you want to understand data science from a strategic perspective and its process characteristics.  Provost and Fawcett is extremely useful for anyone who is trying to get up to speed and demonstrate knowledge in business analytics or data science in relatively short manner.  This text is extremely well writtenâ€”the authors use non-technical language for the most partâ€”and itâ€™s interesting!
Let me first say the content of this book is great - my apologies to the authors for docking a star. That said, I should have docked more based on how the kindle version displays. Look at the size of the equation examples on the bottom half of the image with the pink highlight. Even when I enlarge the text (see the other image without a page number) the equations are horribly tiny. The image with a page number at the bottom shows you how it is displayed in a PDF version (which I had to get AFTER I purchased this from amazon). (Apologizes for not being able to give image numbers, but the pics uploaded in a different order than I submitted them).Terrible quality on amazon's part and this book is frequently used as a college text book, so knowing the equations are essential.
This isn't really a book about the business applications of data science.  Instead it has some businessy sounding bits and the start and end which feel like an afterthought.  The middle seems like it was taken from a data mining textbook (or perhaps previously was one).  Particularly strangely, it presents some math for machine learning but in a dumbed down way using notation the author invented (the strangest was a replacement for sigma as sum notation).Rather than reading this you're probably better off reading a book about how business might be impacted by machine learning and related things (The Second Machine Age or Average is Over).  Alternatively, if you want to know more about data science / data mining (now fairly deprecated term this book uses) or machine learning you'd be better off picking up Hastie's or Mitchell's book or taking Andrew Ng's course on Coursera.
Provost and Fawcett do a good job of explaining how a particular technique can be used, but the book has some serious flaws. The book's main defect is that it's disorganized. While explaining a concept, the authors will add additional information with the qualifier- 'we'll discuss this more in Chapter XX,.' making it difficult to grasp the nuance. I also didn't feel that there were enough examples and what examples they did have was explained quickly and poorly. Some basic statistical concepts are explained in excruciating detail, which more complex concepts and mathematical formulas specific to data science are glossed over. Data Science for Business cannot stand alone, but could be useful to a reader studying another text who would like supplementary content.
Needless to say, it's the best book I've ever read that perfectly combines the technical details and high level intuition."Big data" might sound daunting recent days since AI, machine learning, deep learning based applications are in wide spread whenever you open your browse, turn on your cell phone or etc. But you will feel much less whelmed by reading this book. It provides you with a unique experience in that it bridges the practical business problems and machine learning models.  Roughly speaking, most of books I've read are short in either of two domains: interpretability and rigorousness.  This is fills in the hole pretty well.If you are a data science manager and want to better understand what your team members are doing, this book gives you a snapshot. If you are a data scientist with years' training in statistics and computer science, this book can help you develop your understanding of the business problems in practice and offer you a different angle of analyzing them.In conclusion, 5/5 star, a must-have book that should be on the shelf of each other wants to work in the data related field.
Context: I'm an MD, needing to communicate with data scientist to build a product.I've this far only read two chapters. My pattern-recognition ;) this far however, with an assessment that this will be applicable to the rest of the book is two-fold:1) Too verbose!Too much stuff on explaining the structure and purpose of the book. Could've been said way more succinctly, and therefore more clearly. The effect is that I start skimming.2) Not 'sharp' enough.The best non-fiction written for non-expert manages to reduce the complex into explaining the essence. Not making it simpler, and reducing crucial comprehension. But reducing the complex into its crucial essence.When going over different types of tasks; classification, regression, similarity matching, clustering, co-occurence grouping - the way they are described, there is essentially no difference between i.e. clustering, similarity matching and clustering; they're all classifications - yes, there is a difference between regression.In order for this to be truly helpful even for an absolute layman as myself, it needs to add enough crucial, essential distinctions to make the categories mutually exclusive. I can think about it, I can look it up. The book would however been better if the information was more 'sharply' communicated.So why 4-star?Because it is a beautiful balance for the amateur. Explaining basic concepts instead of trendy-applications.For future versions though, correcting for verbosity and greater specificity (essence) will make it a true winner.
The institution strategy and goals need to be reflected in the procedures used to analyse the data base of the institution and the determination as to what data is relevant. The book discusses ways to obtain the data needed and the short term volatility in return to the company that can result. But the authors show that this can eventually lead to improved efficiency focus and profitability for the company. The book requires  a background in a number of  supportive academics for full understanding . The discipline has defined its own language much like most of the technolgical disciplines and is best appreciated by those familiar with the vocabulary. It is a book that  warrants study not just as a quick read for  introduction. For a person studying or practicing in this area I highly recommend this book for both its interest and as a reference book. Foster Provost and Tom Fawcett have made a valuable contribution to the understanding of Data Science.
although billed, at least in part, as aimed at "business people who will be working with data scientists, managing data science-oriented projects, or investing in data science ventures" (p xiii), the book never points out that all analytic techniques make assumptions and that the data scientist needs to be questioned about that (when they don't mention it upfront) and questioned about what happens when assumptions are violated; in addition, many, maybe most, techniques have biases and these are never mentioned either; there is also no discussion of bootstrap (the authors use cross-validation instead thus, generally, wasting information) or of external validation and no warnings about what to beware of when using surrogates; at a lower level, the book is generally readable and generally well-informed but needs to be supplemented with something that covers how to, at least, question the technical people about assumptions and biases
Foster Provost and Tom Fawcett are known for their work on fraud detection, among others. I have recently read their last book, Data Science for Business â€“ What you need to know about data mining and data-analytic thinking. No suspense: itâ€™s one of the best data mining book I have ever read. Its style allows the book to be read by beginners, but its wide coverage and detailed case studies makes it a reference for experts as well.As the title suggest, the book has a real focus on business with plenty of industry examples and challenges. The style is very pleasant since authors have made efforts to put the reader in specific situations to better understand a problem. To be noted the very interesting discussion of data mining leaks as well as data mining automation. The book is divided by concepts and provides a focus on them (instead of techniques). Although no exercice is present, the book could easily be used as a resource for a course.Each chapter is clearly divided into basic and advanced topics. The evaluation phase of the data mining standard process is deeply discussed. The section about Bayes rule is very well written. Data Science for Business is also an excellent resource to avoid data mining pitfalls. Chapter 13 is a must-read in order to understand success factor for implementing data mining in a company. To conclude, targeted at both beginners and experts, Data Science for Business is the new reference for data mining professionals working in industry.
This book will definitely open your mind to the possibilities and capabilities that business data provides.If you have a close relationship to your marketing manager or executives, this book will make you shine when speaking at meetings or when you are setting up business plans.It gives you the ability to look at data differently and make better decisions on what might have otherwise been difficult hurdles in the way of progress and innovation.
As someone without a strong DS background I was surprised how accessible this was. Really good overview in the first 25 pages and then goes into plenty of detail should you need it on specific techniques. For me: I really concentrated on the intro and the section summaries and will keep on the shelf for reference for the more detailed stuff, as and when those topics come up.
This is by far the most accessible data science book out there. If you have any interaction with data scientists/statisticians as part of work, this book is a way to understand the burgeoning field better.It does not go in depth into mathematics or computer science concepts, rather it grounds the material into a digestible format that anyone can understand.
This book is an excellent introduction for data science for shaping how one would think about information. Went over many different methodologies and explained in such a way that was concise and relatively easy to follow.
A well structured and well written inteoduction to this important subject.  Clear examples are used and the fundÃ mentals reinforced in many places. Of special interest is the example proposal and evaluation in the appendix. A warning, this will really only make sense by reading the book.
Good book but kind of a dry read
Ok read...to start with to get into DS gamut...
This book really help me understand major data science concepts as I work in tech and constantly interface with a technical data scient product + data business contacts.
Used for school. Decent book and good for starting conversations in class.
Returned. Not what I thought it w
This book is used in Northwestern University's course on machine learning. We've got textbooks that provide the technical definitions on data mining techniques, but if you want good knowledge without the itty bitty details, this is a great book. I use this book for the explanation of concepts in a way someone with a business background (vs a comp-sci backgorund) can understand.
I'm trying to learn data science, and this book is written incredibly well.  It gives an overview at a level which gets you a decent technical understanding, plus it points very clearly the way to dive deeper in any particular area that you would like to explore.
Reading this while at the same time learning Python.  Challenging, but interesting as it gives me insight to an area I thought I knew more about.
I don't know if it's that I was in big need of a book like this one, or is simply that the book is excellent. Finally a book about data science in which the use of the word 'science' is justified: questions, exploration, models, heavy testing and tuning. This is not a book about technology -it barely mentions Hadoop!. Instead, it introduces the thinking and the fundamental ways to solve problems using data (and intelligence).