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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.
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 would go as far as to say that the book is rst, a drop about me from the standpoint of this book. I have been an IT professional for a lot of years specializing in programming, database, and MS Office add-ons. Part of my job entails self enrichment, that is, expand my working knowledge in locations potentially necessary for my job. I chose Foreman's book to support with this task for a number of reasons: a) Data Science is a hot zone and my company does have a Data Science group, b) I have lots of data experience under my belt - I felt that it would be nice for once to obtain some useful info from the data, and c) I have a really amazing Excel background - so I figured that Foreman's approach would be excellent for me - small did I know that I would seriously add to my Excel bag of e author makes the assumptions that: a) the reader is somewhat technical, b) he knows nothing about Data Science, and c) he is relatively comfortable working in ing the book is a joy because Foreman has a cozy, chummy style. He definitely doesn't throw all the technical items at the reader rat-tat-tat machine gun style like a lot of other authors. Instead, Foreman gently introduces his subjects and then ramps up technical info carefully. This most definitely helps the learning process.Speaking of learning, by the end of the you will have learned necessary concepts in "machine learning" and I believe that you will be ready for the next step. I sure was. I found the subjects interesting and I wanted to learn more. This is where the book's only issue zone comes into play - the next step. Foreman has 3 references - one good, but minor, one terrible, and the other is inappropriate. Allow me reman recommends a free resource as a follow-on to his Forecasting Chapter. This is a amazing reference, but I believe that Forecasting is a minor subject in Data Science, unless, of course, Forecasting becomes your reman's main reference is: "Data Mining with R" by Luis Torgo. Foreman recommends this as the next step after his book.I tried to read this several times, but couldn't. It certainly wasn't my next e other reference, "The Elements of Statistical Learning" by Trevor Hastie, et. al, is totally inappropriate for Data Science newbies. You can checkout the Amazon reviews for this book and you'll see that you need a beautiful serious background in statistics to obtain anything out of that reference. In fact, the author Hastie says as much in his next book "An Introduction to Statistical Learning- with Applications in R". This is the appropriate next step, but I'll obtain to that in a are my recommendations:A. Read Foreman's book and follow along with him in working through the Excel spreadsheets. This is a first step in getting comfortable with Machine Learning.B. Take the Coursera courses: 1) Machine Learning Foundations: A Case Study Approach, and 2) Machine Learning: Regression. The courses are free unless you wish completion certificates, in which case there is nominal cost.C. Now you are ready for: An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) This book is also available for free by the authors - check online.
Disclaimer: I served as a paid technical editor for Data Smart. I am not affiliated with the publisher, but I did keep a little fee for double-checking the book's mathematical content before it went to press. I also went to elementary school with the author. So as you read the rest of the review, hold in mind that this reviewer's judgment could be clouded by my lifelong allegiance to Lookout Mountain Elementary School, as well as the Scarface-esque pile of one dollar bills currently sitting on my kitchen table.Anyway, books about "Data" seem to fit into one of the following categories:* Extremely technical gradate-level mathematics books with lots of Greek letters and summation signs* Pie-in-the-sky business bestsellers about how "Data" is going to revolutionize the globe as we know it. (I call these "Moneyball" books)* Technical books about the hottest fresh "Big Data" technology such as R and HadoopData Intelligent is none of these. Unlike "Moneyball" books, Data Intelligent includes enough practical info to actually begin performing analyses. Unlike most textbooks, it doesn't obtain bogged down in mathematical notation. And unlike books about R or the distributed data blah-blah du jour, all the examples use amazing old Microsoft Excel. It's geared toward competent analysts who are comfortable with Excel and aren't afraid of thinking about issues in a mathematical way. It's goal isn't to "revolutionize" your business with million-dollar software, but rather to create incremental improvements to processes with accessible analytic techniques.I don't work at a huge company, so I can't attest to the number of dollars your company will save by applying the book's methods. But I can attest that the author makes difficult mathematical concepts accessible with his quirky sense of humor and bonus for metaphor. For example, I previously had not been exposed to the nitty-gritty of clustering techniques. After a couple of hours with the clustering chapters, which contain illuminating diagrams and spreadsheet formulas, I felt like I had a amazing handle on the concepts, and would feel comfortable implementing the ideas in Excel -- or any other language, for that matter.What I like most about the book is that it doesn't test to wave a magic data wand to cure all of your company's ills. Instead it focuses on a few locations where data and analytic techniques can deliver a concrete benefit, and gives you just enough to obtain started. In particular:* Optimization techniques (Ch. 4) can systematically reduce the cost of manufacturing inputs* Clustering techniques (Ch. 2 and 5) can deliver insights into customer behavior* Predictive techniques (Ch. 3, 6, and 7) can increase margins with better predictions of uncertain outcomes* Forecasting techniques (Ch. 8) can reduce waste with better demand planningIt may take some creativity to figure out how to apply the methods to your own business processes, but all of the techniques are "tried and true" in the sense of being widely deployed at huge companies with huge analytics budgets and squads of Ph.D.'s on staff. This book's contribution is to create these techniques available to anyone with a small background in applied mathematics and a copy of Excel. For that reason, despite the absence of glitter and/or Jack Welch on the book's cover, I think Data Intelligent is an necessary business book.I had a few criticisms of the book as I was reading drafts, but almost all of them were addressed before the final revision. For the sake of completeness, I'll tell you what they were. Some of the chapters ran on a bit long, but these have been split up into manageable pieces. The Optimization chapter is a bit of a doozie, and used to be at the very beginning, but the reader can now "warm up" with some easier chapters on clustering and easy Bayesian techniques. The Regression chapter originally didn't discuss Receiver Operating Characteristic curves, which are necessary for evaluating predictive models visually, but now ROC curves are y one true criticism from me remains: I would have liked to see more on quantile regression, which is only mentioned in passing. It's a amazing technique for dealing with outlier-heavy data. The book by Koenker has amazing but highly mathematical coverage, and I would have loved to see this topic given the Foreman treatment. But, you can't have everything, and I suppose John needs to leave some material for Data Intelligent 2: The Spreadsheet of sum, Data Intelligent is a well-written and engaging tutorial to getting fresh insights from data using familiar tools. The techniques aren't really cutting-edge -- in fact, most have been around for decades -- but to my knowledge this is the first time they've been presented in a method that Excel-slinging business analysts can apply the methods without needing her own squad of operations researchers and data scientists. If you're not sure whether the book's sophistication is on par with your own skills, you can download a complete sample chapter (as well as example spreadsheets) from the author's latest thing: unlike a lot of books with a technical bent, the prose is engaging and extremely clear. I think this can be traced to John's childhood. When John misbehaved, his father (who is a professor of English) would punish John by forcing him to read a novel by Charles Dickens. Minor infractions resulted in A Christmas Carol being meted out, and when he was really poor he had to read Amazing Expectations. This is a real story which you should ask John about if you see him at a book-signing event.
When I began to read the introduction for this book, after receiving it as a bonus - I was a bit disheartened. I am not one of personas listed in the 'Who Are You" section - a CEO or VP of an online startup, a beginner BI analyst. Instead, I am a software developer specializing in data visualization and data analysis.Furthermore, Excel is far from my preferred research tool of choice. I like code instead of screenshots. Python, Ruby, and R are where I turn when I wish to look at data.*Even* with this mismatch of intended audience, I found myself engrossed in this book, reading it cover to cover in a few Intelligent is a unbelievable resource. The use of Excel as a basic means for exploring data science concepts is surprisingly effective. It strips away all the code magic. You can't rely on SciKit-learn, or Weka, or even proper functions when all you have are cells and stead, it provides a method for John Foreman to break down these complex concepts into the fundamental components that create them tick. You begin to see the patterns between seemingly disparate technologies that are actually built off the same few bits of logic. Things begin to e writing and real-world situations are really what create it fun and worth reading through and enjoying the ride. John's style hits the sweet spot between clarity and comical. Each chapter is well scoped. You understand the rational behind why someone might wish to use the particular tool being described to solve the issue at hand. The whimsy and flare added by the author moves the plot along at a amazing pace. The issues are easy enough to wrap your head around - but not toys. The datasets generated for this book must have taken a while to curate. The book is really fun to read.I think for me this book provides a amazing reminder of the landscape of data science tools, as well as a story-telling process to describe and relate these tools to non-programmy non-programmers.Even if you aren't a startup CEO... yet - this book is worth having on your shelf. Check it out today!
John Foreman has an entertaining and thorough writing style that makes this a amazing book for using Excel to think through several essential, primary data inference methods. While Excel is not the best tool for data science it is very widely used software; he does an adequate job making the inferential methods explained in Excel to be understood and then, at the end of the book, explains how to use the more powerful, free R app (a standard in data science field). (Though if one wants to learn and use R this book is too basic.)What is nice about him using Excel (with data sets that are also available for download from the publishers site) is that all the additional steps Excel requires to carry out this methods is that it also helps the reader to better grasp the reasoning of these methods along the way. That he peppers the text with a humorous style also makes this a rare treasure for data science introductory books.
This is a very various kind of data science book, and so far that's a amazing the author explains in the cover matter and the introductions, you can solve a lot of true business issues with data analysis and you can do it with a strong spreadsheet. I learned some tricks with Excel from the first chapter I'm still getting a handle on, and I've been using that app for quite a few years.I'm hopeful that his model will work better for me and that I will learn some amazing analyses, and then perhaps some R ... in that order.
One of the only books I brought on my latest overseas deployment. I read the book then watched Sneakers. Disclaimer: I am often referred to as "nerdy" by other troops guys.I think this book is best suited for someone who has intermediate to advanced Excel skills, Is the person in the office everyone looks to when they need to solve issues and likes some statistics (but I guess that is unnecessary because everyone likes statistics.)
Full 5 ere is no reason why you should not buy this book, if you even are remotely connected with things like 'Data Science', 'Analytics', 'Forecasting' etc.I enjoyed all chapters and especially Chapters 4 (Optimization), 6 (Regression), 8 (Forecasting).Seriously buy this book, 's very simple read, and yet the author does not merely skimp necessary concepts, so you obtain best of both worlds, a amazing solid foundation and practical thing I like is for 90% of the time, the topic matter and the spreadsheet diagrams are on the same set of pages, so you don't have to go back and forth between pages to sync text and images.
I am really glad that John Foreman wrote this book. There are so a lot of tools here that I can use in practical situations! I do have two issues. First, since the book was written, a free tool has been created, which is an add in for Excel that does logistic regression. It is called XLminer and there is a video showing how to download it into Excel here:[...]My second problem is that I want he would write a sequel to this first book covering more fresh data analysis tools! I really like the step by step instructions in this book!
Instead of going through complex tools to begin anaylzing data, this book explains all the essentials you need to be a Data Scientist using the master tool of all tools: Excel!I consider my self a black belt Excel user. I used it in finance, accounting, software engineering, proposal writing, you name it! but this book surprised me that there is still more under the hood when it comes to Data Science.
This is a nicely written book for beginners who wish to learn about data science. When I say beginners I mean a intelligent high school upperclassman. The writing is not mathematically challenging and the examples are simple to follow.
From the beginning to the end this book about learning data science has given the best knowledge and shared with us. I found the simple steps learning so helpful. Learning data science is a challenging task but these book has created the learning enjoyable and easy, I loved learning evaluation it has given.
I highly recommended books for beginners ! The writing is clear, simple to read and to e breadth of info covered if quite wide. The choice to begin with python and data science concepts was e author has obviously a powerful grasp of a lot of varied fields within data science, and that contains all machine learning algorithms and python coding.If you wish beginners book in data science t, you can't go wrong with this one. Highly recommended!
Wickham and Grolemund have produced an perfect book that would support a beginning R user become very efficient in explanatory analysis. Unsurprisingly the approach that they expound utilises the "hadleyverse" a collection of packages (ggplot2 for visualisation, tidyr for reshaping, dplyr for selecting and filtering, purrr for functional programming, broom for linear models etc) that dramatically speed up most of the common steps involved in an analysis. One benefit of Wickham's involvement in these packages has been a coherent philosophy that sits behind them. It can be a small tricky when learning this philosophy, but the long term benefits are e book is broken up into a number of sections that effectively builds up the ability to ingest, transform, visualise and model datasets. A amazing portion of the book is available in an online version, to give you a taste of how it is written. A lot of have been following it as it was written. I have passed on copies of the book to a number of colleagues who were just starting out and the response has been uniformly positive. In my own case I was familiar with some of the these packages; ggplot2, dplyr, tidyr, but found the book taught me purrr and how to better use the packages bably my two largest caveats to readers are that there are situations where packages from outside the "hadleyverse" maybe required. The authors do a amazing job of pointing this out, but it does pay in my experience to know le and lattice for example. Both because they can occasionally fit a issue better but also because you inevitably come across other people's code where these packages are used. The other caveat is that the modelling is a small rudimentary. Most of the examples are just fitting independent regression models, whereas it seems to me that a hierarchical model would be a better fit. Still these are little things and it would be silly to expect a single book to cover all of these short this is the book I would give to someone who was keen to learn about how to use R for data science. It reads really well building up the various components whilst still being a valuable reference if you just need a reminder of a particular pack (what is the difference between tibbles and data frames again?). Even though a amazing portion of the book is available online, it is well worth it to have the full thing on your bookshelf (digital or otherwise). On a broader note with Max Kuhn (author of the perfect "Applied Predictive Modelling" with Kjell Johnson) joining Wickham and Grolemund at RStudio, it is a amazing time to begin your R journey.
Perfect introduction to the "tidy" approach to use R. It has bundled several R packages and made a user interface to R and particularly RStudio that makes is much easier to use R for statistics, graphing, and data and text mining. Strongly recommend fresh R users to adopt this approach.
Useful book for especially for data visualization. They give you a amazing trunk of knowledge in the ggplot2, tidyverse, and modelr packages. Would recommend for anyone starting out. Learning how to manipulate data is a large and often underrated part of machine learning, is useful and will be useful until the end of time or until data takes on a various form. Learning how to set up a primary system for modeling is invaluable, and will not likely ever be outdated, just updated. The book does much better at teaching data visualization than modeling. Not limited to ggplot2, tidyverse, and modelr but certainly massive in them.
A amazing introduction to the Tidyverse. Coming from SAS programming with only a small R experience this provided a amazing introduction to me to using R and the Tidyverse in a consistent manner that is focused on getting you programming and giving you useful tools.