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It's a broad high level overview of how data analytics can support businesses increase their productivity and gives guidance on the correct policies and tactics to adopt towards that end.
Data analysis is at least as much art as it is science. This book is focused on the info of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks. I concise introduction and instructions about all stages of data analysis. Each subject can be expanded into a much more deep communication but the suggestions mentioned are very practical. I think it's a amazing starting point if you're a new-comer to data analysis. And it would be helpful to frequently look it up when you're doing the process to create sure you're on the right track.
This book is well written and packs a substantial amount of info into a little number of pages. It is best used to obtain a survey and overview of a lot of of the facets of the domain of data science. This book will not teach you anything in enough depth to actually execute it well — it will teach you just enough to be risky and not realize when you've gone off the rails. I recommend it for managers who may never go into technical depth, for people considering whether or not they are interested in data science, or as a preview book to make a framework from which to hang more detailed understanding. Although this is an introductory book, it assumes you can already program in R. If you can't, either accept that you won't be able to follow the specifics of the examples, or read The Art of R Programming and/or R for Data Science.I dislike that the authors create a number of categorical statements of the form "Data Scientists do this" or "Data Scientists don't need that". I disagree with a lot of of these assertions and I think they have taken a definition of "data science" which is narrower than the prevailing consensus in the is book has some errors (see, for example, the confusion matrix on page 196) but overall the accuracy is above average relative to latest other reviewers have noted, the author's github repository for the book is currently empty. If that's necessary to you, check it under "andrewgbruce" on github and create sure it's been updated before you buy the book.
There's always that one person who is unsatisfied, but it sure as hell isn't me, because I knew what this book was going to be like the moment I saw how a lot of pages it was going to have & how the early release ver looked. I still preordered a hard copy (for sharing) & a digital copy (for carrying), because I knew this was kind of what type of book I was looking for & then e concepts are not astronomically explained, but with just enough depth that I can also individually explain to people what they are. What really stands out for me so far is after each or so concept, there is a section labeled as further reading (well, in the digital copy) that is usually at the end of the book altogether & I found myself realizing I have a lot of those books so the authors really know where to look & tutorial those who wanted more ah yeah yeah, the codes are missing (as of mid-June 2017) but if you really understood / know which packages to use, you wouldn't need the code. The first half of the book are two three liners of code concepts anyways; it's the explanations that matter the most. The second half of the book is the amazing part, which separates a white hat statistician from a grey hat data scientist, which is exactly what I wanted in a
I come from a computer engineering and machine learning background, but not so much on statistics so I decided to give this book a is very simple to read, although sometimes too easy. There are very few math equations in this book, which is amazing or poor depending on your taste. E.g., Gaussians, of course, are mentioned throughout the book but I do not recall seeing the equation for a Gaussian in any dimension in this book (maybe I missed it?). In fact, it's so simple to read that I finished the book before the github repo saw its initial commit.I'm not a large fan of their coverage on classification models, but I might be biased because evaluation of classification models is one my things. E.g., they discuss ROC curves in fair depth but don't mention DET plots, which, in my experience, have been well favored over ROC curves for a lot of years. Perhaps this was a decision based on book length, and again maybe I'm biased in this regard, ere are some careless typos in the book, e..g, the word "partiular" appears on page 51, "significiantly" appears on page 273, and there are related errors scattered throughout the book. I don't understand how the authors did not search these with a easy spell checker. Did they typeset this in notepad?Also, the github repo was pushed on June 17. This seems to be the largest complaint people have about this book so summary, a very simple yet worthwhile read for the price. A very high-level view of data science if you're unfamiliar with the stuff. For someone with a solid STEM background this is a light read which can be completed in about two or three weeks of spare time. For someone entirely fresh to the field, it is certainly accessible and worth exploring if you might be interested in the area.
Info seems plainly written and relevant. No link to datasets makes the "practical" code portion of the book unusable. Will happily modernize my review when the datasets are released.EDIT:Ok the datasets are up. There is a short R script to run to download the data, it will require some little modifications to obtain it working need to make a folder named "data".and I changed the second line in the script from:PSDS_PATH
Perfect introductory text for a comprehensive overview of statistics! The github repository augments the content very well and provides added value for the statistical subjects covered in the book. Both of the Bruce brothers are statistical gurus and this fact is evident in the writing, which is both informative and witty. Peter is the president of and is well-versed in providing statistical instruction to students of all ages and levels. He is also a proponent of resampling and one of the developers of the perfect Resampling Stats software pack for is real that the textbook does not provide in-depth coverage for all topics, but I don't think that was the intent of the authors. However, the text DOES provide an perfect introduction to subjects relevant to students and data scientists. After reading the text and working through the examples, you will be equipped to further your knowledge in whichever subject you require for you data analysis task.Highly recommended!
This book is superficial. For full disclosure I'm only on page 90 which is in chapter 3, but I don't think it will obtain sically it summarizes concepts at a very general level to the point it's not useful except to present you what you need to go buy another book to far it's style has been to give a couple paragraph overview of each concept, followed by giving us several references to check out in order to learn more. If you intended to use any of the concepts you would definitely have to read about them in more detail elsewhere.If someone is talking about stats to you and you need to be familiar with the terms this books would be helpful, or it could be amazing if you wish a high level look at how stats concepts fit together before diving in to learn concepts at a usuable depth of knowledge. That could be beautiful it's defense, after more carefully reading the back cover I think this is what the book intended to be. it doesnt intend to teach stats or intend to present how to use stats in datascience. It seems to wish to be a glossary of stats concepts from a data science perspective. It definitely could have created more clearer.
I have an MS in statistics, and work in bioinformatics, so I already have a reasonable background in a lot of of the necessary concepts in statistics and data science; nonetheless this was an enjoyable and worthwhile read. This book takes the reader through a very wide range of concepts, at a nice depth - shallow enough that the book is quite readable, but deep enough to impart understanding and a new perspective on locations already familiar to me, with suggested further reading for each e 50 concepts are split across seven sections: 1) Exploratory Data Analysis; 2) Data and Sampling Distributions; 3) Statistical Experiments and Significance Testing; 4) Regression and Prediction; 5) Classification; 6) Statistical Machine Learning; and 7) Unsupervised Learning. I was surprised how well the contents of the book fit in with what aspects of statistics I actually require for work (as opposed to what my MS taught me) since it really does cover most locations I de included is generally quite short and succinct, aiding clarity and making the Github repository (scripts were committed in June 2017) largely unnecessary. Personally I like to type out code to test as I think I learn better that way, rather than copying and ere are a few topic areas, such as neural networks, that I am aware of but don't yet understand and would like to have been discussed, but it was nice to see a text that actually does cover 'modern' concepts like boosting and bagging as well as the more traditional; on the whole I was very satisfied with the selection of subjects covered. It is clear from their approach that the authors have insights from experience into which locations are practically useful. I particularly liked that the book contains a sizable discussion of diagnostics and has frequent illustration of making true inferences from data.
This is the kind of book that every aspiring data scientist should have on their book shelf. It is also a amazing review book for data scientists with formal statistics ry clearly written and authors knew what they were writing that data scientist beginners should know. For example, inclusion of concepts like A/B Testing, Resampling e practical examples and R code makes it rich. github repo is available now. This book also includes the "Further Reading" sections which are very important. The book title says "... 50 essential concepts" but I clearly see lot more than that, which is gift and fun to read :-).I love this book and recommend it.
When trying to learn about a fresh field, one of the most common difficulties is to search books (and other materials) that have the right "depth". All too often one ends up with either a friendly but largely useless book that oversimplifies or a massive academic tome that, though authoritative and comprehensive, is condemned to sit gathering dust in one's shelves. "Data Science for Business" gets it just right.What I mean might become clearer if I point out what this book is *not*:- It is *not* a computer science textbook with a focus on theoretical derivations and algorithms.- It is *not* a "cookbook" that provides "step-by-step" guidance with small to no explanation of what one is doing.- It is *not* your standard "management" title on the cool tech du jour available at airport stands and meant to be read in one sitting (buzzwords, hype and overly enthusiastic statements making up for the dearth of actual content).Instead, it is close to being the excellent tutorial for the smart reader who -- regardless of whether s/he has a tech background -- has a sincere desire to learn how the tools and principles of data science can be used to extract meaningful info from large datasets. Highly recommended.
At it's core, Data science is the elimination of guess, intuition,hunch and decisions backed by Data .Data Science is ranked the Sexiest Job Of 21st Century by Harvard Business Review. Today there is a tremendous demand for everything "Data Science", Companies need "Data scientists", IT resources are refocusing themselves to be the "Data scientists". Contrary to famous beliefs that Marketing benefits a lot from data science, companies are finding benefits across the spectrum of their operations . Example : A leading Trucking company used Data mining skill to predict which part of the truck is going to break next instead of replacing it at specific intervals, a Leading insurer predicted those who will complete their antibiotic course based on their home ownership history. If this type of stories and scope interests you, read the book "Big Data: A Revolution That Will Transform How We Live, Work, and Think".I am an aspiring "Data Scientist" and so this review will have a slight tilt from a "Data Scientist" perspective over the business user.WHAT THIS BOOK IS ?This book is very well written ,but not for the faint heart. It is a text book and authors have taken lot of care so general audience can also benefit from it, and also not to dilute it's textbook value. To obtain the full benefit of the book, read about 50 pages ( Do not flip pages), never more than 10 -15 pages per session. The book is intense so you will need to take a break in between or will lose the thread. Once you are finished with fifteen pages, go to the first page and read , highlight the necessary locations and then go to the next page. So plan to read this book in a span of 2 -3 months. I know it is slow but if you wish to understand the inner workings of "Data science", there is not much other option. Alternative is to flip across several superficial articles that is a staple diet of every blog and magazines.WHAT THIS BOOK WILL DELIVER ?When you are finished with the book, you should have a fairly amazing understanding of data science, For example, what type of analysis that needs to be done to identify A. Will the Customer switch loyalty ? ( Yes / No ) B. What type of customers will cancel my subscription ? ( Ex : Middle Aged male from Manhattan will be 5% more likely to switch) C. What are the methodologies to identify If I can up-sell a customer ( Ex : Someone who bought this book also bought ) D. What is a supervised Segmentation and When will you use it ? ( When the target is clear, if the person will default on his loan) E. What is the significance of entropy in Data Science ? F. Exposure to several formula's ( sleep triggers as I call it). A lot of of the tools have in-built formula's but you still need some idea what these formula's are. G. Don't obtain defensive, be comfortable when your colleague sprinkles words like like Classification ,regression, Similarity Matching, Clustering, Modelling, Entropy etc.WHAT ELSE YOU WILL NEED ?Data Science does not exist in silo. It helps in decision making . So should be your learning, Here are my suggestions:1. First and foremost, you need to spend consistent time. If you are running short of time, don't even bother to start2. For those who are interested in understanding Data science, courseera dot org conducts a free 8 weeks course on "Introduction To Data Science" by an eminent Stanford Professor. It needs time and Commitment3. You can obtain true life examples to work on in coursesolve dot org ( ex: Analyze the sleep cycle)4. As a Data Scientist, you will need to understand "Big Data" . Browse an article and even experts use Data Science and Huge Data interchangeably. Hadoop is the core of Huge Data,but it is a globe of it's own.5. Read and begin experimenting with Hadoop , PIG , HIVE, HBASE and the variations it offers. I did a basics training at edureka dot in , an Indian firm, not a amazing training but enough for you to understand and then go on your own. But if time and cash permits, go to cloudera www service and sign up for training. you will not go wrong6. I signed up for Amazon elastic map reduce which has a higher level abstraction (for developers it is the difference between using sqlplus versus TOAD). It is not free but very cheap.7. Test to be the "umbilical cord that looks for a stomach to plug ", look for a mentor, look for opportunity in your firm or elsewhere to grow your Data scientist r those looking for inspiration , google for Rayid Ghani, Chief Data Scientist at Obama 2012 Campaign.
The institution tactic 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 get the data required and the short term volatility in return to the company that can result. But the authors present 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 fast read for introduction. For a person studying or practicing in this zone I highly recommend this book for both its interest and as a reference book. Foster Provost and Tom Fawcett have created a valuable contribution to the understanding of Data Science.
Perfect 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 want to go deep into the math. The coverage is broad with both supervised and unsupervised methods in data mining. Subjects 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 tutorial business decisions and strategy. You will obtain the sense that the authors are war tested veterans of the data mining business and have applied their creativity to a broad range of business, data and technical y 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 little 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 help data mining efforts. If you are interested in exploring these methods in practice, you will need to look further.
Foster Provost and Tom Fawcett are known for their work on fraud detection, among others. I have recently read their latest book, Data Science for Business – What you need to know about data mining and data-analytic thinking. No suspense: it’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 the title suggest, the book has a true focus on business with plenty of industry examples and challenges. The style is very pleasant since authors have created efforts to place 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 primary 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 perfect 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 fresh reference for data mining specialists working in industry.
This book is fantastic. it's a excellent mix of high-level explanation and technical details. There doesn't seem to be much to support 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).
An perfect resource for the Business Analyst (or the curious executive) looking for a comprehensive understanding of data analytics for business, especially the newer zone of heavy 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 present 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 amazing put to begin no matter what your job vost and Fawcett have place 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 tactic 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.
Note - I was provided an ebook ver in exchange for my review as part of the Library Thing Early Reviewers brief –This is a amazing book for any in the data science field or wanting to just understand “Big Data” or a manager/professional just trying to “get current. “ 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 rengths – Organization, having technical info in a side by side section for those who wish it, covering info from definition, through use and application, as well as doing a amazing job explaining similarities and differences on key topics.Weaknesses – there are a few little locations I wanted more. Meaning if they could have somehow had more examples for the various models, situations, etc., especially as I got into more of the predictive models.
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
Both authors practicing data science professionals. Their book outlines practical considerations, explains available tools and techniques, and shows results of a lot of well-chosen e book is appropriate for all data scientists, regardless of background or education. The math is minimal. There are no computer programs or algorithms.
I came across this book while searching for a textbook for my introductory course to DB. This book is of an extreme value. It is a comprehensive reference for traditional relational data modeling and SQL and also includes updated advanced material on data mining, natural language processing, visualization and huge data.What I also like about the book is that it blends theory and practice of data modeling and SQL. Each chapter in the first part of the book starts with a data modeling concept (i.e. single entity, one-many, many-many etc...) and then shows how to implement it and perform queries with e companion www service contains all slides, datasets, and partial solutions of the exercises. All of that for $10 with Kindle, I can't ask for more. This is a must for database students and practitioners as well.
This is the worst textbook I've ever had to use. It's unnecessarily wordy, full of grammatical and formatting errors (in one part of the book, the same paragraph was printed twice in a row; in another, the book said that there were three reasons for something and then proceeded to list four - and there are related errors splattered throughout the entire book), and extremely hard to navigate. I have better luck jumping around randomly in the book in find of a section than I do trying to actually use the find function to search it. The author jumps around from topic to topic in a method that makes it hard to understand; it's almost like he place a bunch of SQL concepts in a hat and randomly drew a couple for each chapter he wrote. Furthermore, there is some weird story about a lady named Alice mixed in with the chapters in what I perceive to be an attempt to create the book slightly more interesting. I assume that my professor is private mates with the author, because that's the only reason I could think of for any professor to choose this book as a class textbook. The reason I gave it two stars instead of one is because the book has been useful in the context of my Database Management class, and it has been genuinely entertaining to read a textbook so poorly written. However, unless you are absolutely needed to have this textbook for your class, I'd recommend versus buying it. I'm sure there are much better-written SQL books out there that will teach you a lot more.
Although the info contained is valid and helpful, I can search no logical method to locate that information. The Kindle ver is very difficult to navigate, (location values instead of page numbers). I had a classmate with the printed ver ask me for support in locating a specific exercise in a specific chapter. I couldn't do it because her ver (printed) had page numbers for reference, and my ver (Kindle) had zone values. I have been hesitant to purchase a Kindle product and after seeing how this text is presented in the Kindle application for Windows, I'll pass. The reason I purchased the Kindle ver is because it is "Required" under my educational program. In summary, the info is valid, but the presentation sucks.
I got this book for my college class as needed by the school I'm attending. It is a amazing book with valuable info inside. But the fact that it was a kindle book created it very limited in terms of how I could learn the material. It is written like a novel. A book like this needs more interactive features on a platform like Pearson. I think it would have been much better if we bought a hard copy instead.
I use this book, and have done so for years, in an MSc course for students from all sciences that wish to acquire literacy on database design and SQL. The students like it and it allows them to work quite independently, having both a lesurely pace and enough depth. The author keeps it up to date, 's not at its strongest as a reference; but since SQL versions differ and change, that might be too much to ask.
It's definitely passable, and some of the homework questions were really fun to think about and respond (they were written in such a lighthearted tone), but overall the book organized info in some counter-intuitive ways and was more verbose than it required to be.
I appreciate the info and the context in which it is written. This will support me to be a better analyst.
This is an informative book on data analytics! This is a concise introduction and instructions about all stages of data analysis. Each subject can be expanded into a much more deep communication but the suggestions mentioned are very practical. The directions are simple to follow, and for me this is one of the best books on this subject. I definitely recommend this useful guide.
Latest 4 days ago I got this book and I'm really impressed with the amount of hints that this tutorial book has. More time I am frustrated about my future for that my mate suggests me the book. In this book the info is organized in a logical method that’s simple to access, read and understand. It is indeed a amazing read and I highly recommend this book to everyone.
Best book to read and learn a lot..This book given me a decent outline of the abilities and capacities needed by an info researchers. its a magnificent is is extremely valuable and useful book for novices. I recommend this book because i like this book and i hope this book will support everyone who read this.
Its vital for me to have some info on the data analytics, even when I'm running my little business. The book may be for the Analytics professionals but it has something for the business people too, and this will change your decision making in your business.
I chose to give it five stars because it is a amazing primer for folks who are not familiar with data analytics at all. It is a fast read and written concisely.
However, I was expecting some really amazing contents in the latest few chapters because they were supposed to be the most necessary part of this book. Only a few pages each chap. Makes me wonder why?
I have a Note 8 using Android device 8.0.0 and I use the Secure Startup feature. This requires access to Accessibility and if I test to turn on access to this feature, I can no longer use Secure Startup after I reboot my device. No thanks and nice try.
cool application but needs a user manual- some thing that explain s the various colors of lines black versus greyetc and what terms mean ie null,... maybe there option to click on a line item and obtain details. if you are not savvy on these termsa lot of this application is hard to interprt. otherwise nice application
Navigation through setup was not incredibly difficult. Primary system ligging. Application does just that on Android device 7. Could be a 4/5 if the application allowed reading of the log versus just displaying that its logging everything. 🤔
Application works amazing but I have one issue.. I was messing around with the settings and I checked the box to lock the log but I never set an access code and now when I test to begin the application I just obtain the lock screen and can't obtain past it? I don't wish to just restart because I don't wish to lose what I have logged, any suggestions?
it all sounds amazing but after instslling and opening it asks me to give it permissions. i attempt to do that but it says the program isnt asking for any permissions. when i test to use the app, only the headings on top appear and cant do anything more.
Asks for permissions not listed before download: accessability and useage access. This seems unusual and misleading. Apps would be trusted and used more if developers explained in detail what permissons were used for. Google seems to have allowed shoddy standards for users having control of software. Don't know how amazing this application is, immediate uninstall.