100 Reviews Found
This book is a amazing overview data visualization including when to use various types and why. It also covers strong tools to create greater impacts. I personally loved the section on information graphic resumes and use of information graphics in improving internal communications. I work as a strategist and use visuals to support lead transformational change- this is a amazing resource for this growing field.
Text is too much waffle - it takes up 55% of book. Would have been better if he just created his points in point is has also meant that the infographics are shrunk hugely.A lot of the examples I don't think are that cool - mainly because they are vertheless it is thick enough that it has quite a few perfect pieces in there with most able to be looked up onthe web.If you are tight on money I would think twice - maybe check out his blog of the same name.
I give it four stars because it is simple to use to obtain ideas and use the concepts. I work in business development and I got this book to obtain my folks thinking more graphically. I don't fully into infographics but this book is a useful tool as there are lots of examples of how to take info and show it in ways that communicate more than the words.
This book is informative and able to provide you with all of the tools you need to achieve your goals whatever it may be. Learning from this book gives you a amazing begin to study Java Script language. This book is intended for beginners, and it is excellently written. The chapters are simple to follow, and if you read carefully, you will easily overcome all the instructions posted here.
I'm not a full convert to the 'Big Data' religion, but Kotu and Deshpande present how to extract insight and meaning from huge data sets, in ways that create sense to me. The book is full of worked examples, is excellent for an novice and math challenged person such as myself. I'm sure experienced data analysts will also appreciate the deep knowledge and experience the authors bring to the subject, and will ;find new, various and better ways to tackle some thorny so notable are Vijay Kotu's credentials: he does this for a living at Yahoo!, is clearly a master practitioner in this fresh field.Highly recommended for anyone grappling with ecommerce, wanting to understand why and how to maximize conversions from browsing to buying.5 stars.
I was wonderfully surprised to see a lot of data mining concepts taught right a long side how to implement them in RapidMiner, complete with pictures and equations ( overviews ).If you know a small data mining, and looking for a book to support you use RapidMiner to place your knowledge into practice, this it!
This book does a nice job of explaining data mining concepts and predictive analytics. The main tool tool they use is RapidMiner. Curiously RapidMiner was only introduced in chapter 13, the latest chapter, although the authors mention you may wish to read this chapter me aspects of this book are done really nicely, like when they explain the Iris data set, which is a popular data set of flowers. There is a diagram (pg. 39) in the book showing the flower, and which labels showing which part of the flower is sepal length and which is petal length. I was familiar with the Iris data set before this book, but the diagram taught me what the sepal length meant ere are lots of diagrams throughout the book which present various ways to visualize data and most concepts have clear explanations. The balance was reasonable between explaining techniques and showing how they are used.I would have liked to see more app of some of the concepts and RapidMiner specifically. Overall, this book does a amazing job of explaining the concepts and techniques of data mining.
This book is really a amazing begin for learning both data mining and RapidMiner. With the step by step guided through the hands on in each chapters create this book much clearer picture of data mining to me. Highly recommended!
I gave up on this book after quickly reading the first 12 chapters, because it is so sloppily written. Here are a couple of typical sentences from the beginning of Chapter 13: "Before moving to this section, it is very necessary to message that a lot of browsers automatically blocks AJAX ... Whenever the web first came around, things were simpler than they are now." The first example of an array in Chapter 10 shows a confusingly incorrect example of a easy operation. It looks as if the author dashed the book off quickly and didn't take the time to read what he had written before rushing off to write his next book. This sloppiness creates an unnecessary obstacle to understanding a straightforward subject that a conscientious author could describe with excellent accuracy and clarity.
I am glad that i found two in one set! The first one has more simplistic and primary instructions while the second provides a dozens of fresh content for me. I love how i can have one book bind that i can easily obtain tip from at any time.
This is a amazing book. You can actually use it to pace your hands-on exercises. Nice structure of chapters, which provide lots of ready-made RapidMiner patterns. What needs strengthening is evaluation of each model type specifically, some info was provided within each chapter, but is rather shallow, the chapter on model evaluation is insufficient. What is missing are some insights on how to interpret the results obtained from each model and what you can do with it.
It's another primary book that explains ven that the company didn't feel it important when they started to provide and adequate documentation on the product, this is good. I only rated it 3-stars because I personally thought that the book, Data Mining for the Masses, did a better job explaining what to do overall.
I bought this book after struggling to learn rapidminer on my own and required something that would work as a manual/tutorial. The book had more emphasis on Data mining concepts and less on rapidminer than I would have liked, but I was able to successfully make my first production model after reading this book.
Author is right in that he doesn't tell you that you're going to be a data scientist over night - as data mining is a long and challenging discipline to master – but at the same time I think this book is definitely a amazing lead-in to the topic for guys and girls just starting
I bought this book cos it was cheap. After reading it I'd say its amazing value for money. Really simple to follow and well written.
Data Science is bloody complicated. I've spent 3 weeks of my holiday break on Quora and reading my textbook for next term trying to piece it all together. This book won't victory awards for advanced theories but is a amazing easy-to-follow intro to the topic for beginners and people like me.
I am reticent to give this tome a excellent score because I have only read three chapters; however, I will say that sufficient explanations are given alongside workable code. To that end, I would a recommendation based on examples in both R and potential caveat is that the links appear to point to nowhere in particular in terms of data sets and programs. But, I am confident this info is available somewhere, or will be.
I should have realized what I was buying by looking at the name of the publisher: Pearson Education. This books reads as a very primary introduction to sports analytics and sports marketing / sport as business - as a text for an entry level College course. There is no true insight into sports analytics, and at $US68 for the Kindle edition, this book is clearly very not good value for money.Specific criticisms are:- Aims to provide wide coverage, but ends up providing no depth in it's coverage.- Far too a lot of references in the text. The author provides a range of ideas that could be applied to sports analytics, but does not develop them. Instead, a lit of references is provided for the reader to investigate. For example, social network analysis is introduced, and then the reader is refered to a number of references: "small globe networks have been studied extensively in social psychology, sociology and network science (Milgram 1967, Travers and Milgram 1969, Watts 1999; Schnettler 2009) " . Just the introduction of an idea, no true development of it's use in sports analytics and a reading list. If the author wanted to retain the references in the next edition, I'd advise him to have an annotated bibliography.- A lot of unnecessary material. For example, table 1.1 is a list of 54 various sports. Using the % of book feature in the Kindle edition, 59% of the book is taken up with a glossary, baseball glossary, bibliography and index. The extent of the material is excessive; for example, the glossary defines such terms as "e-mail", "html", "internet", "IT" and "World Wide Web". For a book on sports analytics, I don't see any point in including these and other terms in a glossary.- There are a number of R and Python scripts, which interestingly are described as Exhibits. The author doesn't give any practical guidance on analysis of sports data; these R (and Python) scripts are as amazing as exhibits in a ere are a lot of far superior books available on sports analytics, and I'd recommend these instead of this me suggestions;- Analyzing Wimbleton - The Power of Statistics- Basketball Analytics - Spatial Tracking- Baseball Analytics - Objective and Efficient Tactics for Understanding How Squads Win- Analyzing Baseball Data With R- Mathletics
This book was needed by my data visualization class. I wasn't able to search a used ver of this book so I ended up reading the temporary PDF my school had at the library. Let's just say this... This book is SO amazing and SO useful, that I ended up shelling out to it AFTER I've read the book already and AFTER the class was over. I think it's just such a handy tutorial for anyone to have. I'm sick of crappy presentations overloaded with meaningless data that don't convey any useful information. If you are too, I highly recommend this book.
Like Tom Miller's other books on data science (e.g., Modeling Techniques in Predictive Analytics with Python and R: A Tutorial to Data Science (FT Press Analytics)), this is a highly accessible and exceptionally well-written tutorial to sports analytics, and one I will be recommending to my grad students. Miller's books excel in presenting a combination of real-world examples, explanation of methods, and clear data visualizations, along with code (R) and downloadable data (available at Sports Analytics and Data Science is a amazing resource for anyone interested in prediction in professional sports. Because the examples are of interest to such a wide audience, the book could also be used as a alternative text for introducting data e data are now downloadable at the github website ( and will be available at the Pearson www service in the near future ( The Pearson www service currently has Miller's example data, along with R and Python code, from three other books in his data science series. Miller has data and code from four books on the github site. I teach in a data science graduate program and am delighted he has created these available for download. This is data science at r those previous reviewers who have had problem locating the data, I am including a screenshot of what is available at the github website for this book.
The author, Cole Nussbaumer Knaflic, uses easy examples and explains to every reader about how to communicate to audience using could implement these concepts by hand. Learning how to use PowerPoint well doesn’t mean you could give an perfect presentation to the audience. You must display your idea like a well-trained designer and tell a story like a Hollywood script , the author borrows some professional elements from design and script-writing such as affordance, acceptance and storyboarding. In the chapter of “case studies”, the author demonstrates how she would fix the not-so-good graphs by the concepts covering in this ling an emotional or persuasive story using data is a hard work. If we don’t consciously recognize that this takes time to do well. We run the risk of losing the potential opportunity to drive change and is is the final step the audience will see. We should devote our time to storytelling with data.
An inspiring fast read to support break of the tyranny of Excel's default chart styles. The book is packed with practical tip aimed at making your charts and accompanying stories more visually pleasing and impactful. There are a lot of "before and after" examples with explanations of the "why" beyond the suggested improvements. The book provides a amazing foundation for the reader to then build upon with more advanced books covering (for example) interactive dashboards and the technical info of using different dedicated data visualization tools -- subjects that this book doesn't ter reading some of the negative reviews I almost didn't the book; however, I took the possibility based on the quality of the author's blog and podcast. Rereading these reviews now, the complaints regarding not good print and binding are likely from a since-resolved bootleg copy problem that the author mentioned in September 2018 a Data Stories podcast. Regarding being too basic, I can only say that I learned some valuable lessons even after several decades in the corporate globe using Excel and PowerPoint and also recently completing an online course for how to use Tableau. If you are still debating purchase, do as I did and read some of the Storytelling With Data blog and then decide.
I am 6 chapters in and the reason I am giving this book two stars is that the code for the chapter examples is still no where to be found. The book indicates it is on the publishers www service - [...] it is not there and I have attempted to contact the publisher twice but they have not gotten back to me either. This is a large swing and a miss (no pun intended) by the author and the publisher. Selling a book that code for the examples but not having the code accessible is not good form.
I am a university professor who teaches biostatistics and I search this to be one of the best books that bridges the gap between analytics and presentation. There are some perfect books around that present visualization (e.g., The Wall Road Journal Tutorial to Info Graphics: The Dos and Don'ts of Presenting Data, Facts, and Figures or books by Few Information Dashboard Design: Displaying Data for At-a-Glance Monitoring &Show Me the Numbers: Designing Tables and Graphs to Enlighten or Cairo The Truthful Art: Data, Charts, and Maps for Communication) and there are amazing books on presentation (in particular I love Duarte's books Resonate: Show Visual Stories that Transform Audiences) but this book is special in how well it blends the two topics. I have never seen such an perfect presentation on how to build a series of graphics. That is, with books by Few or Cairo you will know how to create *a* amazing graphic and with tip from Duarte, you can connect with your audience but with this book you will see how to build a series of interrelated graphics that highlight various parts of a dataset. Most of the examples are spun around business but the examples are simple to extend to any field.While I think the author wrote this for people who do presentations in any quantitative field for a living, this book should be needed reading for graduate students preparing to defend a dissertation or thesis.
I work in the project controls arena of huge projects that have hundreds, if not thousands of people working on them. A key requirement for project controls is to hold all project personnel informed about the project status. Needless to say engineering plays a major role on these projects and brings lots of data with them; pages and pages of it. As the author points out the analytical types are not necessarily trained on how to tell a story (i.e. communicate) with their r the latest 10 years or so, I have developed methods for getting the project story down to a single graphic. It's usually a huge graphic, but a single one. It has the result of getting everyone on the same page. But for people who are not used to looking at this type of presentation, it can be overwhelming or as the author points out they have to work at it in to understand it. This was a key point for fore I finished the book, I started making changes in my work products. They were little changes, but the feedback was very positive. One example, do you ever note info in page footers like date, time and maybe filename and path? Does anyone think to place them in the background by using a shade of gray instead of the default black? No! Test it. Then ask for opinions It doesn't sound like much, but it's reducing the tournament on people's is book is great! It's fairly short to read and has a lot of examples making it simple to follow the author's intent. She obviously is very amazing at her profession. If I had to pick one book as a recommendation to someone who wants to learn about making amazing presentation graphics, I will point to this book. I highly recommend it. But, the book doesn't stop there, the author has included a listing of resources (e.g. books and websites) for continued learning.
As a professional working in one-to-one marketing, I feel like too often we succumb to flashy "junk charts" or otherwise ineffective presentations of data. This book seeks to break this habit by teaching typical BI specialists about how to more effectively communicate using e depth at which the author discusses each of the basic subjects is appropriate for the majority of applications and the visuals presented are top notch. That praise aside, if you're looking to go (even) deeper about individual parts and pieces of charts and tables, I'll also recommend you pick up Stephen Few's "Show Me The Numbers," as he gives an incredibly comprehensive view on the micro-level of visualization design. In my opinion though, this book covers enough ground for the majority of readers and is a amazing method to "up your game" when it comes to presenting to a dozens of audiences. I'm going to recommend it to my colleagues as a method to lift the quality of data visualization in my department as a whole.
People wanting a primary introduction to presentation graphics would bewell served by this book by Cole Nussbaumer Knaflic. Prerequisites areminimal: there is almost no mathematical content and no use of any butthe most elementary statistical methods. Knaflic's encouraging messageis that MS Excel and PowerPoint can be quite enough for goodgraphics, but you will need to go beyond the defaults and work at most all the examples are of very little datasets already to hand withtwo-way structure. 2 variables for 12 months and 5 products for 7 yearsare typical sizes. In practice when analysing data it can be hard workdeciding what methods to use and reducing a mass of raw data to aconcise summary. These steps, sometimes most of a project, are hereassumed already e subtitle flags a focus on "business professionals"; the contenttactfully implies junior people presenting with PowerPoint totime-challenged bosses at brief meetings. Seemingly few write reports tobe read any more, or use any other presentation aflic is perfect on the need to hold things simple. She has a goodeye and sound logic on what looks and works well and what does not.Examples present how mediocre graphs can be improved by reducing clutter,killing the key, better use of color, and related standard tricks.Horizontal bar charts are usually more readable than vertical, and piecharts and a false third dimension are best avoided: these points havebeen well created a lot of times, yet do deserve forceful repetition. Variouskinds of bar and line charts are her main metimes the discussion seems a small contrived, as not good graphs areset up to be shot down, but that's often what convinces. Readers shouldbe on the author's side as she encourages us towards effective andtasteful graphics. Her combinations of blue for data deserving emphasisand grey for data providing context -- or of blue and orange for groupsto contrast -- are amazing design patterns for experienced analysts as wellas outright e closing chapters are more long-winded and repetitive, but do includesmall gems. A splendid case study on avoiding spaghetti graphics (lotsof tangled lines) stands out, and the issue and the ideas deservedmore.I always search it disappointing when datasets are fabricated orsufficiently anonymous that they might as well be. People care mostabout their own data which an author cannot provide, and confidentialityconstraints often bite, but true data examples are still generallypreferable to fake. Too a lot of examples here are variants on Products A toE or Features A to O. Unfortunately an outrageous example of a barchart from a well-known U.S. tv news network (p.50) seems alltoo real.What's not here contains Cleveland dot charts, histograms and box plotseven among the staples of amazing introductory statistics courses, letalone (say) use of logarithmic scale, always one of the first graphicaldevices for a lot of sciences. So if you wish something with morestatistical bite or depth, you need to look elsewhere. Naomi Robbins'excellent, no nonsense Creating more effective enable you to go in any first edition there are some little slips and exaggeratedclaims. 40% is not a majority (p.5). There is confusion between numberand percentage on p.39. Any rule that "bar charts must have a zerobaseline" (p.52) is simplistic. It is quite correct that bar chartsshould encode departures from some sensible reference level. (Thetelevision network responsible should have attention.) But thatreference level could easily be some value not zero, such as paritybetween men and women, or the mean of a variable, or 32 degreesFahrenheit to separate freezing and non-freezing temperatures. Idisagree that every dollar amount or percent should be labeled as such(p.90); that is repetitive clutter such as Knaflic rightly deplores. Noris it an absolute principle that every axis needs a title. If the axislabels are 2008 to 2015, no one should need "Year" to explain what ishappening. Far from being "extremely rare" (p.141), several exceptionsto that principle are included in this book!A note on style: Inside a very useful book is an even more usefulshorter book struggling to obtain out. For my taste, the motivationalwarm-ups and small anecdotes are often too spun-out or too trite. Goodgraphics should be presented as illustrations within a amazing story: a keypoint, but not one that needed a long chapter with digressions on RedRiding Hood or on Aristotle on drama, or tip from a junior highschool teacher. A tighter copy-editor would have signalled that"leverage" (used as a verb about 70 times) was too much of a personalfavorite, while "de-emphasize" for "tone down", "utilize" for "use" and"incredible" for things all too credible are among several otherrepeated tics.An simple solution is to skip and skim: if a book is on graphics, you canalways just look at the graphics. In this case, Knaflic has written aworthwhile book that, little info aside, does well what it tries todo.
This is far and away the BEST data vis book because it goes beyond the techniques for making visualizations to the ways in which you can connect with clients, co-researchers, and stakeholders. This is an essential book for effective reporting and dissemination of information. Truly exceptional.
Very rarely do I search a business book that so fully meets my needs and expectations...and then surpasses a full-time business analyst who was hoping for some practical guidance to improve his visualisations, I was delighted by how clearly the author delivered it with her easily understood concepts and step-by-step demonstrations.Whether you are already acquainted with data analytics tools like Tableau but think your presentations look boring or you're an artistic designer who can't figure out how to use to express your point, you need this book!
The "book" arrived as a trapezoidal nightmare! I'm not sure who's in charge of quality control at the printing press but this is just ridiculous. First, I'm baffled as to how this can leave the printing press in this condition. Second, how can the person fulfilling the not see that the book is malformed?? Really? I'll admit, I did obtain a nice laugh. But the joke's on me. Now I have waste my time returning this monstrosity.
Now that the data is available I will go through this book and do a proper review. But in general I do not like this book as much as this one R for Marketing Research and Analytics (Use R!)
The 14 chapters in this book cover a amazing dozens of topics, ranging from how to conduct marketing research for the purpose of customer acquisition, segmentation, and retention; product design, positioning and promotion; and how to use and analyze social networks to better understand the determinants of product promotion success, etc. The business, data collection, and model validation problems are discussed reasonably well, but the discussions tend to be a small bit jargon heavy, and some jargons tend to be explained only tersely, and sometimes not at e 14 chapters comprise only about 60 percent of the book, with the remainder being appendices and an extensive bibliography for further reading. Appendix A (Data Science Methods) is a very high level overview of the most commonly used analytic techniques such as regression and classification, machine learning, data visualization, text and sentiment analysis, and time series and shop response models. Appendix B (Marketing Data Sources) is mostly about measurement theory, sampling techniques, and how to conduct surveys, interviews, and field research, etc. Appendix C includes info about the case studies discussed in this book, and Appendix D includes some programming utility ers will obtain more out of this book if they already have working knowledge of at least the R or the Python programming language, and some or all of the analytic techniques overviewed in Appendix A because this book does not provide any introductory guides on any of these topics. Solutions to the coding part are usually in R but a lot of of the R code have corresponding Python versions. All of the code and sample data are available for from the www service mentioned in the book, and a lot of of the code are reproduced in full in the book as well.
Very amazing book, well written, and the best pas, as with all of Miller's books that I have purchased, is that it comes with true code examples in both Python and R. Amazing method to obtain up and running.
This is a difficult book to review, and I struggled with it a bit. On one hand, it is well written with amazing use of hypothetical and relevant examples (.e.g Amazon, AT&T). On the other hand, it reads like a programming class - lectures and all, which can be dense and difficult to glean info from - not exactly the rapid fire approach a lot of data scientists I work with/am use (caveat: I'm in life sciences).Pros:-Wealth of info - book is dense-Covers subjects based on marketing not programming approaches (e.g. Recommending Products with approaches rather than Building Network Diagrams with marketing examples of how to use this technique)-Uses my two favorite languages - R & Python - very common and can be applied to modeling, charting and analytics more readily than other languages - they work well together - Python for building interfaces and specific R packages for doing the deep statistical/data crunching & visualization/presentation (at least that is how I use them)-Plenty of example code that can be readily used - sample data described in text available for download-I like that there is a list of Tables in the front of the book - makes it simple to rapidly search the right examplesCons:-This book is difficult to go through - you need to be comfortable with both R and Python-Book also assumes familiarity with common statistical/analytical approachesBottom line: as this book does not cover fundamentals of any of the core topics (marketing, Python, R, Predictive Analytics) my gut is that to approach this subject you would be better served learning first predictive analytics and marketing concepts prior to this book being of full utility. That said it is incredibly informative and I found it a fascinating.
Updated my rating from 2 to 5 stars as the code has become available on FTPress. I received an email from the publisher latest week. Not sure why it took over 6 months for them to post this.
Amazing book, very well explained examples (the R and the Python codes are very well written) but if you have read other books from Prof. Miller, you would be able to remember some exacts paragraphs across some books.
amazing book, but....no data sets to work with. Seems critical for a source code massive book (ie almost every chapter has pages of code). We would prefer not to scan, then test to run the code ourselves. Read the appendix first at that seems to be where the theory is then go back to the chapters for practical work. Borrowed this book from the library....its really expensive otherwiseupdate: OK --kept reading, and paying overdue fines at the library, so I bought the book. Really worth the read if you're serious about focusing on marketing data science. Amazing starting read for technical marketers who wish to do something in this ad the code samples are now available. Will have to try and see. One thing I noticed about the content. Every time something got interesting Prof. Miller would quote a reference for further reading (ie. details). That's sort of OK, but leaves me wanting and having to go dig elsewhere. Suggestion: one more paragraph for such situations would place my curiosity at rest. A lot of content around product development, positioning, recommending, but a small light on broader examples - It might be helpful to describe a broader range of techniques (ie. list them), then drill down on one or two. It just seems too narrow, like drinking from a straw when really a funnel is required with the large alternatives. Enjoyed the book (looks like a text book but reads like a novel - that's a amazing thing)
There is a lot of amazing info in this book, but I search it poorly organized and 's hard to figure out the best audience for this book, and I think when you imagine it as a textbook in a specific degree program, where the instructor would know exactly the prerequisite classes taken by the students coming into the class, then it starts to create sense. Ideas toward the front of the book are not properly introduced and code is thrown at the reader without much in the method of explanation. In the context of a class, the instructor could smooth out these rough edges and likely deliver a coherent educational experience. But as a standalone learning experience, this is rough going.(I'm sure it's relevant what my own background is, which is completely comfortable with predictive analytics and programming, but ignorant of the specific method that "marketing" folks look at the world.)I search the book very strangely organized in that Appendix A, "Data Science Methods", is, well, relegated to an appendix, rather than being a central part of the flow of the book. It's a really amazing chapter!There is some tremendously useful code presented in this book, but again, the method that it's presented will create it of limited utility to those who do not have a tutorial in the form of a professor teaching a class or who do not have sufficient background to ramp up to the ideas presented.I am also not fond of the production qualities of the book, e.g:1. The paper is overly thin, so you can see the text on the other side of the page, which just makes reading harder to my mind.2. The code is presented with this horrible gray background. Black text on a gray background (I dunno, maybe 30% gray) is not amazing contrast. So the code ends up being unnecessarily difficult to read.3. That this was made using LaTeX is obvious to me. Not because the typesetting is problematic, but because of the front matter, with the separate listings of Figures, Tables, and Exhibits. LaTeX makes producing these listings easy, but just because you can produce them doesn't mean you should. They feel anachronistic and e fact that all the code and underlying data is available on the web is amazing, so for some folks, the difficult presentation will be worth slogging through to see the examples. But I fear that a lot of would-be readers will just give up because the path is so rough.
I started a business two years ago but without any science on data gathering. Now I know how crucial it is to really obtain the data and analyze it. The process of Analytics may seem overwhelming at first; determining what data to prioritize, how to collect the data etc. But once you have core primary data required for the business, knowing how to analyze it is the next huge step to achieving your business is book is comprehensive yet easy enough for beginners. It helps one understand the importance of data mining and analysis but without overwhelming the reader with too much mathematical concepts. A amazing book to support anyone fresh in any business.
Not too impressed. This is a book for something who has just heard the terms Huge Data and Predictive Analytics. Not for someone who knows what these e writing is not so professional, which leads to skimming and page skipping.
This book is excellent for those who wish to become their own boss and succeeding in their chosen profession. A few weeks ago, my uncle suggested me about this book and by reading this book I have learned about data analysis and predictive analytic for is book taught me about some easy and simple to understand terms. By reading this book I have understood about how to take advantage of data from my everyday operations. By the support of this book I have learned about how to conduct data analysis to enhance my business. This book is perfect and by reading this book I have understood about what techniques I need to employ for sustainable success.
What a disappointment! Amazing content however the not good spelling and translated info takes away the credibility of the subject. Did anyone even proof read the Kindle edition? I would have given it a 1 star but I didn't "hate it" -- still has a lot of relevant points - just painful to obtain through them.
Predictive analytics is actually a gathering and analyzing of data to predict future happenings — are not as dramatic as an engine malfunction thousands of feet in the sky. And the idea is not fresh — predictive analytics is as old as the Info Age itself.But what is dramatic and very fresh is how industries across the board are embracing predictive analysis as a method to revise outdated business models. This ANALYTICS book from author Daniel Covington is the best primer you'll ever need. You will learn in here the steps required to perform predictive analysis and what techniques required to employ in to achieve sustainable success. It gives the reader a full picture of what it is all about. I highly recommend this to all. Very nice one!
This is a very, very, very high-level overview of data and analytics, possibly intended for first-year students or anyone starting in the field. The description is misleading, and you will not learn much from this book other than definitions. It does not delve into any subset of analytics, and you can most likely search the same info online for free. It's unfortunate that I ordered a Kindle version, or I would have returned this item. It is totally useless to me.
I love this App, the content and design is positively impactful. I appreciate the opportunity given to me to learn (at my convinience) about Data Analysis, etc. The Squad has done a amazing job. Please hold this global postive impact up! You Rock! More wins🙌💖
I purchased this book before I had a possibility to read any sample chapter and was disappointed after I went through the book.Every chapter is dedicated to an app of a particular model of predictive analytics, where a (more or less) true issue is described and discussed, name of a model to use is mentioned, chart outputs are shown and used for a conclusion. In very much the same format and content of an article that you would see in for example Bloomberg business magazine. There is no substantial discussion of any of the models, and without a amazing understanding of such models you cannot conduct predictive e content of this book could be used in the first 2-3 weeks of an introductory course in Analytics discussing what is Analytics and what are some example applications. I ended up keeping the book mostly due to hassle of a return, and partly for using it as a list of major models to read elsewhere and learn.
I've read a few books now and been taking classes on data science but I've had problem linking theory and the practice. This book has been a large support in seeding ideas, and giving practical examples on how to execute those ideas. I appreciate the lack of downloadable source code as it has forced me to write the source by hand. Amazing book.
This is a amazing book on using R for predictive e books www service includes all the code that is used in the book.I tried all of the downloadable R files and they all worked as advertised.I admit not trying the text processing though (Chapter 7) only because I don't like R for text processing.Rather use perl or s:1. All the code works2. A amazing sample zone of topics, so you obtain a feel of predictive modeling in various situations.3. You really don't need an extensive math background, since there is virtually no math described at ns:1. If there was one thing I want was better done is the analysis of the results. Some of the results, unless you are already familiar with the statistical technique used, might seem foreign and will require you to do some extra mmary:Overall a amazing book, minus the 1-Con above.Hint: If you do the R programs, go through each one a piece at a time, to see what's going on. I found it's better than just "running the code". You'll have a better understanding of what's going on.
I had to read this for a class and in the end skipped several chapters because it didn't have that much value. It also contains a bunch of code with no connection between what you read and the code. I mean it is related, but it isn't like it walks you through the code. I don't know anyone who would read pages of code in a book (compared to downloading and running code, but then how do you charge for it). So overall not very useful and a waste of for me.
I got the Kindle edition so that I could lift code from Kindle for PC and test it. But I started reading it in my old (circa 2009) Kindle II and noticed the figures are misaligned and the links don't work. (I still like extended reading on the Kindle because it is easier on my eyes indoors-- and the best thing next to hard copy outdoors.)I decided to write to Kindle folks about the misalignment to test and exchange the digital ver for a hardcopy. I wanted to present what I was seeing in my Kindle so I opened the book in my Kindle for PC Windows 7. In the PC version, the figures align perfectly and we have the added of full color reproduction. All the links work as expected. Then I check my iPad mini and the figures are aligned. (Although I hate the weird reverse text and columns-- have to figure out how to adjust that.)So, if you wish the digital ver it's fine on current technology. Reading this book on a PC is the best of the three digital formats.
I had high hopes for the book, but it didn't enrich me in any way. The book has been outdated and outsmarted by better books in the market, and at much reasonable so, its misleading in a lot of ways . The book says `Python Edition` - and then the author uses Python wrapper scripts to call R. That's not what I thought it would mean by `Python edition`. I regret this purchase.
Amazing overview of true examples in business with nice visualizations capabilities shown. The author lays out the background of the examples and the analytic questions to be solved. Plan to spend some time reviewing the code in the files if you are not as familiar with R at the level the book speaks to
What an awesome book!! I'm a CS professor and I developed a Data Science course and chose this book as the main reference for students; So glad I did. It covers concepts clearly and concisely. It is very well written and discusses so a lot of interesting subjects in a hands-on approach. I have already heard compliments about the book from some of my students. Highly recommended.
This is an perfect guide. This is the Python book for the data scientist: already knows Python or at least OOP programming, but wants to be able to utilize the native and NumPy structures for writing machine learning algorithms. Slicing, broadcasting, tuples, pandas data frames -- all useful for applying Python's tools to data science. This is an perfect guide.
This book will support you tackle the globe of data acquisition and analysis using the power of the Python language. This book is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. My goal is to a tutorial to the parts of the Python programming language and its data-oriented library ecosystem and tools that will equip you to become an effective data analyst.
The author explained how to manipulate data, clean data and more with python and differences libraries. If you have spent any amount of time working with data from various sources you'll know that the nitty-gritty items is something you spend a lot of time on. That's why having such a book and such a framework is golden. I recommend it for the absolute beginner.
This is an astounding aide. This is the Python book for the info researcher: definitely knows Python or possibly OOP programming, however, needs to have the option to use the local and NumPy structures for composing AI calculations. Cutting, telecom, tuples, pandas info outlines - all helpful for applying Python's apparatuses to info science. This is an awesome aide.
This book is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. This book incredibly outstanding, in the wake of examining this book I am so dazzled. I have learned a lot about Python data analytics, data visualization, data manipulation by reading books.I benefited from reading the book. I suggested this book reading.
Awesome booklet - amazing overview with good, clear explanations - this was a kindle book read on my iPhone kindle app. I work with MS PowerBI Desktop and took courses in statistics in college, but still required an overview of data analysis. The author extra learning resources and started the first which is amazing too.