laptak2003/AmazonDataScience huggingface dataset:
Both authors practicing data science professionals.  Their book outlines practical considerations, explains available tools and techniques, and shows results of many well-chosen models.The book is appropriate for all data scientists, regardless of background or education.  The math is minimal.  There are no computer programs or algorithms.
This book helps answer what the data science is about. It teaches you what kind of task and analysis you would do if you worked as a data scientist. The author emphasizes that it is rather "conceptual" than "mathematical", so it is greatly helpful to those without much math and/or statistics background. If you are into learning professional skill or knowledge, it may not be the book but it is still a good book to start if you need a general idea.
The first page was off the binding and folded in half when I removed the book from the package. This was supposedly a â€œbrand newâ€ book.
This book is for people to gain an understanding of what data science truly is and how it can help with business decisions. The authors do a great job of keeping the concepts applicable to actual business events. I am a database developer, who also builds data mining models, and I wish more of my company's business managers would read this book! Data is an asset and should be treated as such. I highly recommend this book for business users and anyone wanting to know the fundamentals of data science.Pros:Very direct and easy to read (for a data science book)Ties data science into real world business scenariosGives basic fundamentals on why and how a company can adapt data science to gain a competitive edgeCons:Not directed towards theoretical understandings of algorithmsNo exercises to practice conceptsThe cons are more disclaimers than cons, but thought you'd like to know. Take a "Look Inside" to get a feel for it.
I have read a number of books on data mining, and information processing, but this was the first very good overview of the process and it was written in clear, easy to understand language.  It talks more about the process than the technology, but ties the two together to show how a business solution would be put together.  A must read.
The book is easy to read, with sections to investigate for dept into topics.
Purchased as a text book for class, easily readable from the perspective of an average joe (non-data scientist).
It's a good, but hard read. I'd like to think of myself as above average when it comes to stats and math, but I had to reread several chapters to ensure I had the concept down. More of a book around machine learning. Still a good read, just take your time with it.
Great book to gift to your business stakeholders
If you are one of those that is not going to wait till some sits down and teaches you daw science concepts then book is not for you. If you like get your hands dirty and get into meaningful conversations with your data scientists then this book is for you.
Very well organized, easy to follow and full of real life examples. I truly recommend this ebook to everyone who's learning how to deal with a big data and big data analytics projects.
Insightful and suitable for beginners interested in joining the data science field. Concepts were also well explained and easy to understand.
Extremely useful and thorough, from the perspective of a business person whose business involves overseeing and delivering analytic products.  Gives you the details, insights, underlying principles, and a glimpse but not the full set of math that would be necessary to actually be a practitioner.
Nice intro book on Data Science. Audience business manager without too much jargon.Topics are organized logically and book could be use as good supplementary textin not too technical university course.
I've read this book at least three times.  DS4B breaks down data science concepts into terms business leaders can understand and apply.  It carefully separates the concepts from the math theory in a way that engages both the casual and technical readers.
This book strikes the right balance between providing practical advice for the aspiring data scientist and giving a solid foundation in the theory that can be generalized beyond the contents of the book.  A lot of business books claim to be appropriate for a wide audience from novice to expert but this one really hits that mark.  I've searched thoroughly for a book that is a comprehensive accounting of the techniques and possibilities of data analytics and been disappointed until I found this one.  I've read the entire thing and I find myself referring back to it frequently.  Whether your planning to perform data mining work yourself or you just want to really understand what it's all about, you won't be disappointed.  There are some additional topics that I had wished were included in more detail but I've discovered that this is because this is still a young discipline and those things (e.g., formal detailed methodologies) just don't exist yet.  Don't let the low price fool you, this is the most comprehensive book of usable data mining available right now.
Good overview of Data Science for a manager or other non-data scientist.I would recommend it for individuals wanting to understand the capabilities and high level process of data science.  It is however limited in its depth if you want to really dig into the nuts and bolts of how data science is done.
...speaking as someone who dove in without testing the water first.You'll be left with skills yet to develop, but enough confidence and competence to do so.
If you know what you're doing as a data scientist you will learn so many ideas that you'll for sure increase your models predictive power.
I needed this book for a class, but I would tell you that it is one of the most valuable that I have purchased. A must have if you are thinking about a data analytics or data science career.
An excellent, well-written resource even for data science practitioners like myself.  The book articulates data science basic principles very clearly for executives who want to work with (and hire) data scientists.  It also helps me remind myself of the nuances between different data science methods.
When trying to learn about a new field, one of the most common difficulties is to find 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 heavy 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 little 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 perfect guide for the intelligent 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 information from huge datasets. Highly recommended.
Provides a great framework to approach analytics/machine learning with real world examples. The book is written in easy to follow language and makes a difficult subject understandable even for beginners in this area. A must read if you're exploring a career in analytics or just interested in learning more about the subject.
This book essentially systematizes data analysis methodologies, and is simply the best resource I've ever seen to accomplish that purpose.  I'm working to see if we can get this book for all the data analysts on our staff, so they can have it as a resource.
Excellent !! This book is very "direct to the point", with good real examples, it really teach us about the use of Data Science on Business.
Not at all what I expected. The book is heavy on mathematical equations with very limited business applications. Big disappointment.
Well written, a good overview for the semi-technical business reader.  Explains the critical areas of Data Science with appropriate depth to understand concepts etc.
This book addresses the topic of Big Data in a practical, understandable manner.  Some books are too technical or the authors seem to be learning as they go.  These guys know what they are talking about well enough to really be able to explain it.
Extremely useful to get your feet wet and learn about the principles of data science.  Clearly written and a book I'll likely reference again when I need a refresher.
As a starting point for anyone interested in getting to know what Data Science is all about this book is a great resource. This field is huge and the authors do a great job painting the picture of what it is all about.
Thank you for a brilliantly written text that allows the reader to navigate the field of data science and for lifting up the mystery cover for many potential data analysts who don't go into the field because they think it is voodoo.
Excellent book with the right balance of technical information and business applications
Best book on this topic I've read.  Superior in all respects.  Greatly reduces the learning curve, and demystifies the concepts.
The intuitive non-mathy approach makes this immediately accessible to a large audience. You will walk away with an understanding of the concepts, like you never thought possible. Very impressive.
very clear and easy to read. notions are introduced one at a time, at a good pace. would recommend.
Simply saying, this is my favorite book. The authors took up the challenge of writing a timeless book in a field undergoing fast technological growth.  If you want to manage successful teams in data science and data-analytic thinking this book is a must. I highly recommend it!
Informative but dry. Overall a good resource but can get dense. Should illustrate how real world tools can be used.
Bought book for introduction to data science course. Great book.
This is not a fluffy book. It helped me understand what is possible in data science. It is a good compliment to technical books on R or whatever for doing data science.
Great read with plenty of real world examples that illustrate the real world significance of data mining
Highly recommended.  It's a very good book. I like reading it. There is no complicated mathmatical formula in it which makes it very easy to read and understand.
The content of this book gets 5 stars. I especially appreciate the author including Python this time around. However, O'Reilly decided to print this book in black and white. That isn't acceptable for a $50+ book where you need to be able to distinguish between colored lines on charts.Thankfully I have an O'Reilly subscription where I can view the digital book in color, as I imagine the author intended.
Shout out to the author for embracing Python
Glad to get the python scripts for the content. I was expecting a color print pages but this is black and white.
Pictures are not clear.
good layout, a great guide for a newbie data scientist
Vanderplas provides a good overview of the standard libraries and concepts needed to work with data effectively in Python. Worthwhile purchasing for an introduction or refresher on topics. Also, he has provided the code in helpful notebook form on Github.
This is by far the best book out in market to get you started with using python for data science. You will need some basic understanding of python and machine learning to understand concepts here, but this book will definitely take you skill to next level.This is no-nonsense book and goes deep into stuff which are relevant and important to do data science in python, every page is rich in information and provides practical use case, optimization tricks and adds new dimensions to your understanding of topic.
This is one fine book.You will learn a lot.In addition to Data Science, the author also covers Ipython and ipynb (Ipython notebook).The author also provides a list of references at the end of each chapter, which I find very useful to delve into topics that goes beyond the scope of this book.
Well written, Easy to Follow, Adopts Python 3.
Very good comprehensive data analysis book. Buy, buy it now!
Great for Learning.
Awesome book.
I've read many of the python data science books out there. I found this one to be the most complete and concise of the lot. There are plenty of high quality and relevant examples.
First of all, look at this book as a REFERENCE GUIDE, not a full blown "How to" with Python and Data Science.I came to this book thinking it was a book that TAUGHT data science with Python and I foolishly ignored that title clearly has the word "HANDBOOK" in it and it is just a series of examples that show Python's Data Science capabilities for those who are well versed in Python and Data Science. There is little to NO explanation of any of the concepts or examples and the book simply just runs through example after example with little commentary on each in any meaningful way. I am on page 64 and will most likely not continue working with this book. Do not get this book if you are a beginner Python user like my looking to take your skills to the next level, this book will not help with that. Also, there are NO exercises for you to work with on your own to apply the concepts discussed throughout the book.
I received the book today and got very disappointed. The reason I gave two stars is not the content of the book, rather the quality. Everything is black and white. There are plots with different colors and since this book is grayscale, plots look nonsense to me. I am going to return the book.
awesome book...must read
I have been reading and recommending this book from the early-release stage. The first half of the book is dedicated to introduce the basic Python libraries for data analysis (and scientific computing in general). The second part deals with Machine Learning from a practical point of view using the Python library scikit-learn. The book is ideally suited to those that already know the basic Python stuff (or know how to program in a language like R/Julia, etc) and want to learn how to use Python for data-analysis. Even if you already know Python and how to use it for data analysis you could still find some gems here and there in the form of very clear examples or comments. In summary, if you want to do data-analysis and you already know Python read this book, if you do not know Python read Think Python by Allen B. Downey first and then read this book.
This book is not bad, but not great either in my opinion. The author obviously knows his stuff and covers the material in an accurate and competent manner. However there are a couple of flaws in my opinion. Firstly this book is quite dull, I've read other data science books that are more engaging. For example he might discuss matplotlib and go exhaustively through many options. That is fine, but boring, in my opinion it might be better to do things in not quite as much depth but more in the context of a realistic analysis.Secondly this book can't decide if it is a reference or a tutorial. The author gets a bit carried away showing too many features, I often found myself nodding off or losing my concentration. With so many online references, it might be better to concentrate on being a tutorial and not try to show so many features. Or perhaps separate each chapter into a tutorial and then a reference. On the other hand I realize that some readers might want this extra depth, so I'm just saying what I personally would have preferred. A related problem is that the material can quickly go out of date, I already found some options to be deprecated when running code.Thirdly, I question some of the organization of material, he often introduces some aspect, doesn't explain it properly, and then returns to it later on to explain it in more depth. An example is the Scikit-learn pipeline object, he starts using this leaving me puzzled and only later returned to explain it. This kind of issue was relatively common.In conclusion this is a decent book and certainly not a bad book, but more suited for particular audiences. This book would be good for those looking for a reference and relatively detailed information on a particular topic.Not so suited for beginners, I think they would be confused, or overwhelmed. People with some experience who are looking for more of a tutorial could be bored.It is worth noting as well that there are so many python data science books, but nearly all of them not very good. Relative to the other books, this is probably one of the best. In contrast there are a number of excellent books that use R.
This is a fantastic book.  You definitely want to read it with one hand on your keyboard, following along with the examples.
I'm and experienced R user in data analytics and I wanted to learn Python from scratch. I've just finished to study this book from cover to cover and I'm extremely happy with the book. However, as mentioned by the book, the reader is assumed to be familiar with the basics of the language. For this reason, I spent a few hours to get familiar with the language before going through the book. Even if the author works in IPython (i.e. Jupyter), I haven't found any problem working using pycharm.Pros:- This is the book for the R user.- The book adopts python 3.- The writing style of the author makes the book enjoyable to read.- Amazing chapters on numpy, pandas and matplotlib/seaborn.The section on scikit-learn assumes no experience in machine learning. If you are new to machine learning, this section becomes great for you since the author provides an excellent high-level description.Cons:- There are no explicit suggestions about how to structure a data science project in order to go beyond a simple script.- The section on scikit-learn assumes no experience in machine learning. If you are familiar with learning theory, trees, SVM, PCA etc you will not get much out of this section.
This is not a data science book.  Heck, there's only one chapter on Machine learning.  This is more of a data analysis book which you can buy from wes mckinneys book.  This is just another garbage rewrite.  Definitely disappointed at the content
How can books like this be released in justblack and white???Color is so much more engaging, visually appealing, stimulating and much easier to decipher charts and illustrations. Again, how can these publishers release these books in black and white??Itâ€™s like trying to eat a peanut butter and jelly sandwich without the bread...why would anyone even try ??
I read this book in its Safari online format and the downloaded Jupyter notebooks from Github.  I found it extremely helpful in getting up-to-speed on major Python data manipulation libraries.  The Jupyter notebook format is very effective in demonstrating the concepts and techniques covered by the book, as well as facilitating a hands-on experimentation approach where the reader can copy stuff from the online book or its notebooks, paste them on working notebooks, modify them, and get immediate feedback on what works and what doesn't.
That is a really good book for those who have a scientific mindset. It was not easy for me to understand NumPy - it's quite different from Python itself. This book will give you both a big picture (why NumPy is fast and Python is slow) and provide necessary details. The author is really passionate about scientific Python that also helps to learn.In my view, it's better than 'Python for data analysis' for learning about NumPy and matplotlib (but for learning pandas you'd better go with this the last one).You may also check his talks at various conferences. It will be very good addition to his book. I'd say those talks are much better than many video courses on this subject.
I recently completed a quite challenging ML course where we had to hard-code most of the algorithms rather than using libraries. Let's just say I regret that I did not discover this book before taking the course. The coding part of the course would've been far less painful.Chapters on Numpy and Pandas are brilliant - they focus exactly on the issues that matter in the life of a data scientist, and those that are often not explained well in the online courses. Can't say anything about the ML section of the book, because I haven't got there yet.I'd recommend going through this book (at least through Numpy, Pandas and Matplotlib sections) before the study of ML algorithms. This may make the learning path easier.Note that the book assumes the reader is familliar with Python itself!
I think the title is highly inaccurate... I was expecting to have much more in depth book on ML/DS. Instead it was a reference on rather simple concepts. And I can't believe how much time was spent into explaining how to create nice matplotlib graphs... I personally use bokeh so that whole section was useless to me and had nothing to do with data science.
I just finished reading this book cover to cover and learned a ton. Jake is a fantastic writer, teacher, thinker, coder, and data scientist. He explains things very clearly and takes a very structured approach, starting with IPython and then Numpy, building up with Pandas and Matplotlib, and finishing with an 184-page chapter on machine learning. This book has developed and cemented my understanding of many key concepts, as well as my decision to pursue Data Science and Machine Learning as a career path. It also made me a better coder. Highly recommend.
This is an excellent book for anybody who is interested in quantitative work with Python. In-depth coverage of NumPy, Pandas, MatPlotLib, and SciKit-Learn. What more could you want?
By far the best book I've looked at about the subject.  Wish I had found this earlier ( read the book by McKinney first, which I don't recommend) .
Wonderful book. The NumPy Chapter is very clear in comparison with other books. The author is very kind and helped me to clarify a couple of points. Well done!
This is excellent...I'm only half-way through but can say that this book has already been worth its price just for the chapters on Matplotlib and pandas which are a model of clarity and full of clever insights.  The author has made all the code that runs through the chapters available free as Jupyter notebooks which is a big plus, but it's handy to have the text version available to consult as a handbook.
As a handbook, it is well structured and quite convenient. However, it is a little bit easy and lack of practical cases.
Headline says it all. If you are thinking about it, buy it.
I have been searching for a book like this for months to learn the special stuff on it. Totally worth the deal, the price of a meal for an enormous amount of ML knowledge. Best purchase of the O'Reilly series.
I'm a big fan of this book. Getting started with data science, I went through page by page taking notes and practicing the techniques. Now I use it as a quick reference guide if I need to brush up on syntax. Highly recommended.
Only one thing, the PDF version online is in colors while the book is in black (not the cover of course I mean the inside pages of the book)
More for advanced python users
well explained the basics of python programming for beginners. examples are small and easily executable. you can finish this book in 15 days with 2 hours each regularly on a day. I strongly recommend this book.
This book is great, however the 4 star rating is due to their being no color images.
Nvidia compatible GPU Python for data analysis. Nvidia compatible and is still the only game in town.
Superb book.  Provides a sound foundation for learning data science.  You will always keep it within reach.
This book is an amazing resource for learning, implementing, and teaching Data Science. The author is an incredibly talented person with a gift for explaining things clearly. A true inspiration!
Good book with practical tips. Very useful in daily data wrangling, munging tasks using pandas numpy. Good coverage on visualizations.In depth coverage with examples on basic machine learning algorithms.
Full of easy to understand examples.
Just start reading, it is easy and simple to use. Will write or write an update once more.
Great introduction to data science in python.
Great introductory book!
Excelent to those want to jump in data science.
Muy buen libro
Excellent introductory book for machine learning in the Python programming language and its ubiquitous ML package scickit learn. It is a MUST read for everyone interested in the world of data science.
I've just finished this book. The author gives a well-written introduction into Machine-Learning with Python Scikit-Learn and illustrates each chapter with well-designed examples that are easy to follow and understand. I am very pleased with this book and can definitely recommend to everyone who is a beginner in the field and wants to quickly get hold on the practical approaches to ML.
Bought this book for two reasons:1. It's the best book I've found on data science related capabilities in Python, including great coverage of NumPy, Pandas, etc.2. The author has made this book available for free on the web - that generosity deserves to be supported.
The book is just great - amazing combination of details and brevity. I had programming experience but no experience with Python at all before I started reading this book. Very good fit for my qualification. Recommended for everyone who is going to start a new way into Data Science using Python.
I've given three stars not because of the content of the book which is excellent but because as others have noted, the code examples and figures are in grayscale. This greatly reduces the readability of the book as compared to the freely available online edition at author's GitHub page.
Great book for learning how to work with and manipulate data in Python!This book is an amazing resource for anyone getting into machine learning in python.  The language has great tools for working with data and it's important to know those tools to get the most out of your time and effort.  If you're familiar with computer programming in other languages this is a perfect book to learn what all is going on under the hood in python and how you can use it's tools to accomplish what you want.
Jake Vanderplas is a wealth of information (if you are coming to Python from another language, check out his Whirlwind Tour of Python). This book is a must for every data scientist.
I paid an arm and a leg for this book and it came with black and white visualizations. It's not helpful while trying to understand what a code output will look like. Very disappointed with the quality of this product. The seller should reduce the cost of this book.
Good book overall. Pretty high level material and good for reference.
This book is great and z wonderful intro to data science and Python the author has a great sense of humor too.
The figures were generated in color, but printed black and white, so they are often unintelligible. It's hard to tell the red dots from the blue when they are both grey.Apart from that major oversight, the book is ok. If you want to learn data science, this is not for you; it doesn't get into the fundamentals much at all. If you are an experienced R user looking for how to translate into python, this will get you started. The rest of my review comes from this perspective.The book spends far too much time on low-level ipython, numpy, and matplotlib functionality (chapters 1, 2, and 4). You are rarely going to use this stuff.The pandas section (chapter 3) is fine, but I was a little disappointed in the treatment of the grouping/aggregation functions. The book mentions the split-apply-combine paradigm of Hadley Wickham, but doesn't cover the topic in nearly as much detail as the paper of the same name. I was hoping to learn how to translate the dplyr verbs (group_by, filter, select, mutate, summarize, arrange) into pandas, but this book doesn't provide that. You will learn the basics of grouping and aggregation, but your code is going to be a lot more verbose than it was in R.The machine learning case studies in chapter 5 are pretty nice - probably the only reason I would recommend this book. The chapter provides a good overview of the scikit-learn API and effective patterns for machine learning problems.
I am currently taking a Machine Learning course from Udacity and this book has proven to be a great reference guide for several projects and quizes. Although it does not go in depth in regards to machine learning (although almost half of the book is dedicated to it), it does give an understanding of essential concepts. For those interested in machine learning I would recommend bying "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Geron as well as this book.There is no one book for data science, and this one is no exception. Just keep that in mind before buying it.Other than that, I am really happy with my purchase.P.S. For those complaining about black and white graphs and diagrams - check the author's GitHub.
This is an excellent reference book for people working with data science. Remember, 80% of the effort in machine learning, data analysis or data science in general is about processing data and understanding data. This book is for that purpose and I think it's the best book out there about data processing, analysis and visualization using python. If you look for hardcore machine learning, go for other books. Highly recommended!
I have used R for a few years and this was my first book that covered Python for data science. Even though it does not go into super great depth in any area, it is definitely a super book. It covers everything from Pandas, Matplotlib, and scikit-learn. I would highly recommend it for anyone that is new to Python and/or data science. The book is written with Jupyter Notebooks so it is easy to follow along and try code from the book in your own notebook.
When I first received this book, I was surprised that it didn't get to scikit-learn until the last third of the book.  The first third is about numpy and pandas, and the middle third is about matplotlib.  Now that I've been applying it at work, however, I've found that the items covered in the first two thirds were really essential.  I wouldn't be nearly as productive if I had just jumped straight to the sections on scikit-learn.  The author does an excellent job covering broad terrain with enough detail that you are able to apply it to your problems.  You will find yourself going back to use this book as a reference.
This book is well written and easy to follow.  It's saved me from spending hours searching the internet to get acquainted with the standard libraries.I have used it extensively for the intro to ML at Berkeley and for now the book belongs to my short list of desk reference books.
I really enjoyed this book.  I had not much experience with python prior to reading the book however I was able to pick it up quickly.  Before long I was plotting distributions of real time statistics and prototyped a predictive modeling micro service.  I consider this a must have book for any aspiring data scientist.
I love the presentation style and the treatment of the subject in this book. This is a must have for experienced programmers breaking into the Data Science/ Machine Learning in Python. The book could have been organized better into more chapters instead of five.
amazon's print of this great book was off poor quality. this color book was printed in black and white, rendering many of the illustrations useless. the cover of my print was off center and cut off at the top. the book is excellent and the contents are excellent, a great course with thorough and useful detail. but amazon's print of it is cheap and lazy.
The book is without updates, so that most of the examples are outdated or they don't work with new updates. This is especially true of the chapter on Pandas, where almost every other example errors out. Avoid wasting money or time on this garbage book. There are not even exercises to test your practice. Sadly, almost all books on Python from O'Reilly are also of this caliber: a bunch of contrived recipes that, even in the rare event they do work, do not translate to real world data problems at all.
I am a university professor who teaches biostatistics and I find this to be one of the best books that bridges the gap between analytics and presentation.  There are some excellent books around that show visualization (e.g.,Â The Wall Street Journal Guide to Information Graphics: The Dos and Don'ts of Presenting Data, Facts, and FiguresÂ or books by FewÂ Information Dashboard Design: Displaying Data for At-a-Glance MonitoringÂ &Show Me the Numbers: Designing Tables and Graphs to EnlightenÂ or CairoÂ The Truthful Art: Data, Charts, and Maps for Communication) and there are good books on presentation (in particular I love Duarte's booksÂ Resonate: Present Visual Stories that Transform Audiences) but this book is unique in how well it blends the two topics.  I have never seen such an excellent presentation on how to build a series of graphics.  That is, with books by Few or Cairo you will know how to make *a* great graphic and with advice 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 different parts of a dataset.  Most of the examples are spun around business but the examples are easy 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 required reading for graduate students preparing to defend a dissertation or thesis.
This book was required by my data visualization class.  I wasn't able to find a cheap used version 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 good and SO useful, that I ended up shelling out money to buy it AFTER I've read the book already and AFTER the class was over.  I think it's just such a handy guide 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.
The author, Cole Nussbaumer Knaflic, uses simple examples and explains to every reader about how to communicate to audience using data.You could implement these concepts by hand. Learning how to use PowerPoint well doesnâ€™t mean you could give an excellent presentation to the audience. You must display your idea like a well-trained designer and tell a story like a Hollywood script director.So, the author borrows some professional elements from design and script-writing such as affordance, acceptance and storyboarding. In the chapter of â€œcase studiesâ€, the author demonstrates how she would fix the not-so-good graphs by the concepts covering in this book.Telling an emotional or persuasive story using data is a hard work. If we donâ€™t consciously recognize that this takes time to do well. We run the risk of losing the potential opportunity to drive change and action.This is the final step the audience will see. We should devote our time to storytelling with data.
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 order not see that the book is malformed?? Really? I'll admit, I did get a nice laugh. But the joke's on me. Now I have waste my time returning this monstrosity.
I work in the project controls arena of large projects that have hundreds, if not thousands of people working on them. A key requirement for project controls is to keep 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 data.For the last 10 years or so, I have developed methods for getting the project story down to a single graphic. It's usually a large graphic, but a single one. It has the effect 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 order to understand it. This was a key point for me.Before I finished the book, I started making changes in my work products. They were small changes, but the feedback was very positive. One example, do you ever note information in page footers like date, time and maybe filename and path? Does anyone think to put them in the background by using a shade of gray instead of the default black? No! Try it. Then ask for opinions It doesn't sound like much, but it's reducing the competition on people's focus.This book is great! It's fairly short to read and has a lot of examples making it easy to follow the author's intent. She obviously is very good at her profession. If I had to pick one book as a recommendation to someone who wants to learn about making great 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.
My office was raving about this book, but I found this book to be extremely disappointing.The book was so basic as to be insulting. I literally GROANED as the author explained, as if this were a groundbreaking concept, to know your audience. I rolled my eyes as she delivered Pro-TipsÂ® like: "don't clutter your slides!" And "don't read off your slides when delivering a presentation".Do you remember that introduction to public speaking class you took in high school/ college? Yup. This is that without the benefit of having in-class practice time.Pro-TipÂ® Skip it!
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.
An inspiring quick read to help break free of the tyranny of Excel's default chart styles. The book is packed with practical advice aimed at making your charts and accompanying stories more visually pleasing and impactful. There are many "before and after" examples with explanations of the "why" beyond the suggested improvements. The book provides a good foundation for the reader to then build upon with more advanced books covering (for example) interactive dashboards and the technical details of using various dedicated software data visualization tools -- topics that this book doesn't touch.After reading some of the negative reviews I almost didn't purchase the book; however, I took the chance based on the quality of the author's blog and podcast. Rereading these reviews now, the complaints regarding poor print and binding are likely from a since-resolved bootleg copy issue 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 world 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.
Very rarely do I find a business book that so fully meets my needs and expectations...and then surpasses them.As 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 software to express your point, you need this book!
I bought this book to learn more data visualization tricks. However, when I first opened it, the glue in the binding cracked and a page fell out. "Arrived broken" is not a problem I expect to have with books!I hope this is a rare manufacturing defect and that the seller is able to exchange this for a copy with better glue.
Great book on how to use correctly data visualization in order to fill the last mile gap in (almost) all corporate data science teams.  Communicate clear and personal to the audience is not a natural skill for data experts, but it can be developed, and more quickly with this concepts introduced by Cole.
Clear and engaging and examples that the author walks you through. I've already applied what I've learned to my job.
Buy this book! I've read a few visualization books as well as taken a visualization gradate course. This book does a great job explaining the interesting and useful parts of visualization. She has helpful examples of poor visualizations and how to make them more efficient and communicative.
This easy to read and articulately written book moves from start to finish through the storytellling with data process. We are using Cole's book with about 30-40 child welfare organizations serving children and families in Texas. Everyone loves the book! The blog on their website is also helping our professionals get feedback on projects and provides examples of projects to improve their work. We highly recommend this text for novices telling stories with data.
This easy to read book gives example after example on how to create  graphs that explain whatâ€™s happening and are easy to read. I feel that my graphs will be so much better after reading this book.
The reviewers being critical of this book were expecting too much out of it. This book opens your eyes to some of the dumb ways we try to report out information to business leaders and does a great job of providing tips and also pushing us to think about the multiple ways we can present data. I would recommend to anyone that needs to report out certain metrics to client or the business leads in your organization!
Cole takes a difficult subject and makes it understandable. By itself that would be good and worthy of praise. Clarity is always appreciated. But she does this by employing the skills, traits, and tricks that she so clearly explains. Cole implores the reader to eliminate clutter, visually demonstrates why it is important and its potential impact, then strips away at difficult concepts with clear and concise prose again and again. We readily grasp all this because of her use of context, visual display, simplicity, focus, design, and in so doing she tells a story about dealing with data and information, so that we can inform and not confuse. Perfect. But she achieves even more because through the use of these concepts she makes them useful for everyone and not only those dealing with large data sets; she demonstrates that which she illustrates. I'm a trial lawyer. I found her chapters on thinking like a designer and lessons in storytelling to be more than helpful. From the importance of aesthetics, audience, controlling narrative flow, to repetition she provides lessons in telling stories that can benefit anyone. Recommended for everyone!
This book helps to synthesize analytical findings into a series of pressure-tested and convincing graphics.  It also helps to structure and deliver a very compelling data-driven story for your audience.I recommend this book for business professionals who have to deliver compelling and convincing presentations on a daily basis,like investment bankers, PE professionals or management consultants.
I have a number of data visualization reference books because I process a lot of information.  This book is definitely the most approachable and in my opinion sits at the top of the stack.  Not only does it illustrate effective ways to develop charts/graphs, it helps you conceptualize the presentation strategy as well.  Very well done.
This is an extremely insightful book on storytelling with data. I've been leading software development teams for 25+ years (Java, .NET, Mainframe, Mini, Client/Server, WEB, etc.) and with the emergence of BI, big data and analytics, visualization represents a key element to the discipline. This book is extremely well laid out, concise, provide the right amount information and above all, fun to read.  The information is logically presented and well detailed.  I've read many many books in my career, this one being by far the most insightful, well presented and easiest to read. It will definitely be my go to reference in the future.  One thing is sure, I will never again look at charts the same way.  Kudos!!!
As a statistician who already makes a huge number of graphics everyday, I was pleasantly surprised about how much I learnt from this book. Although I use R not excel, the principles that Cole walks the reader through are very applicable and although they might seem obvious, often she said things that made me go 'ooooooh I'd never though if it that way!'
+25 year-long finance professional here and as I say to my team â€œthis is what keeps the Robots (AI) from doing our job.â€ The â€œthisâ€ is telling a story that SHOWS company leaders and investors the key drivers of performance. This book really hammered home to me the idea that my job in FP&A is not to bombard my leadership with chart after chart of historical trends, but to distill down all the data available to me to show them MY CONCLUSIONS - what moved the needle (and you can count them on one hand). If you just want to make prettier charts, then this is definitely the book for you (stop using every color under the rainbow, if everything is bright and pretty, then nothing is important). If you want to take your career to the next level, this book will change what you report to your bosses and will make you an all-star.
Uh, the book is a trapazoid. At first I thought it was going to be something clever about visualization... But no. A production mistake led to a crooked book cutting and binding.To get a refund, being to told to return this book. Hoping the pictures attached will let you resolve the issue on your end by sending a replacement book.Cheers!
I never thought a book about data could be such a page turner. The information is so valuable and Cole provides plenty of practical examples. After 20 years of communicating with data, Iâ€™ve now learned that Iâ€™ve been making some huge - and easily correctable - mistakes. I highly recommend this to everyone except my firmâ€™s competitors.
This book is an excellent guide for working with and improving your presentation of data. It's written in a conversational, easy style, which means you cover a lot of ground quickly. The step by step approach provides an excellent framework for moving from the theory to your own practical challenges.I'm a CFO and present data regularly in my job. I have already benefited from this book as has, no doubt, my audience. I'd recommend it to anyone that needs to work with data visualisation on a regular basis. The time spent reading this book is a good investment.
This book helped me put words to things I had been struggling with my entire career.Management had noticed I had a knack for exploring and finding key data points but I failed to deliver effective results when asked to present findings.For example proof of the authorâ€™s effectiveness, it only took two charts side by side for me to understand why I was struggling, the rest of the book was a page turner of transformational, easy to adopt strategies & tactics.
This doesn't feel like a whole book of material, and some sections are stronger than others.In particular, the concrete examples showing evolution from default to optimized graphic are really instructive. I work in a quantitative academic field and I still learned something here about designing effective graphics to showcase an actionable point.