laptak2003/AmazonDataScience huggingface dataset:
If you want to get started with Data Science and donâ€™t like learning a new language such as R or Python, then this book is a perfect fit for you. Entertaining, Data Smart: Using Data Science to Transform Information into Insight approaches data science from a unusual angle. John W. Foreman has written a book for those who wants to apply data mining without using advanced programming (R, Python, etc.). It doesnâ€™t mean you donâ€™t need to understand what data science is, and this book is very good at explaining it to non-practitioners.Foremanâ€™s book is written in a nice and funny stile, which makes it an easy read. Data mining algorithms are described with the minimum equations needed. Foreman has written a practical book and thus decided to use Excel as a tool for data science. The book starts with an introduction to Excel and its most famous functions. For data scientists using SAS, R, Python or Matlab, you may discover how powerful Excel is. But you will also see how clumsy it is to use Excel for data science. Whereas you would need a few lines in R, the book will take you through a dozen pages of step by step actions you need to perform to obtain the same in Excel. Not only is it more time consuming but also more prone to errors.Donâ€™t get me wrong: Data Smart is excellent at explaining how to perform data science in Excel. I just think Excel is not the right tool for it. The book is also a journey into MailChimp, the authorâ€™s company. This is nice and provides plenty of examples related to e-mail marketing. The book thus provides quick and high-level description of the problem, followed by Excel steps to solve it. In conclusion, Data Smart is a must read to get a fresh perspective on data science with a â€œData Science using Excelâ€ user manual. And for the experts? You can just skip the Excel parts and get insights into the field, with a focus on MailChimp use cases.
The best single book on Data Science today. I handle the data analysis and BI for the delivery side of a huge internet-based retail company, and have been a fan of Foreman's since his "Analytics Made Skeezy" blog days.  His explanations are clear, his examples are to the point, and throughout it all, he is results-oriented. That is an important distinction; if you want theory, if you want to follow the development of algorithms, then attend a lecture by Moses Charikar (actually everyone should attend a Charikar lecture, just for the utter joy of the intellectual insights.) If you want a hands-on, practical guide to the processes involved in Data Science, then Foreman is your guy. Unlike many writers in the field, he is not an "every problem is a nail" guru, touting one magic approach to all problems. Neither is he a database guy or business stats guy who recently found it sexier and more lucrative to describe himself as a data scientist. Foreman is the real deal and short of hiring him as a consultant (if he still takes those kinds of jobs now that he's the brains behind the analytics at Mail Chimp) the next best thing you can do is read this book.
Hilarious. Relevant. Accessible. Those are the only words necessary to describe this book. John breaks down really complex concepts in data science so those who *consume* the information understand what the heck is going on.Too often I'm in a meeting where the data scientist is incapable of articulating what it is they did or found, and the stakeholders in the room nod in agreement despite their obvious incomprehension. This book helps both: the stakeholders understand what's going on with the major themes in data science, and the scientist can learn ways to pragmatically describe their art. Plus, both sides get to laugh. Win-win.
John Foreman has an entertaining and thorough writing style that makes this a great book for using Excel to think through several essential, basic 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 application (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 extra 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.
I was a little worried about buying a non-programming book about data science (since I am solely a programmer and consider spreadsheets PM or Admin Assistant tools), but I'm glad that my worries were for nothing, this is a splendid way to dip into data science. I feel like normally, books teach you the algorithms that have been found and leave you to get the hard work of intuiting what's really going on. Foreman takes the opposite approach here, trying to show you what's going on, and demonstrating it with a simple, concrete spreadsheet, but I feel I've really gained an understanding of what's going on here, and there has been so little work on my part, I'm in disbelief.
This is an excellent book and incredibly powerful if you're in any sort of field that requires data analysis. I've read many books on the subject of statistical analysis and most seem to fall in the category of:1.) A very technical "how-to" on using Excel/R/whatever to run certain functions but there is a huge assumption that you understand what the data output means. These are more like user manuals for their respective programs.2.) A more high-level view of stats that is similar to stuff you learned in Statistics 101 but with far less examples and it can be difficult to see how this will apply in your position.Foreman's book was recommended to me by Chandoo of Excel Guru fame and it is the book I've been searching for to step up my game. He does a great job of breaking down core concepts and introducing new tools(all in Excel!) that I can actually apply to my current position. That being said, there are a couple of caveats...1.) I would recommend at least 6 months of solid Excel usage under your belt. The author goes over the formulas and you can download the files online, however there is plenty of OFFSET and Array formulas that can be intimidating to more casual users. I highly recommend downloading the files and doing everything from scratch, and then using the author's version as reference when you get stuck.2.) This is not really a book you can breeze through(and I mean this as a compliment). The core concepts are broken down well but you will need to reread chapters to truly understand how the formulas and underlying concepts fit. I personally read Chapter 2 around 5-6 times so I can grasp k-means clustering and how I can apply it to my current project. I've only been in the data analysis world for a couple of years, so perhaps this is just my noviceness showing.This is a fantastic book and one I cannot recommend enough. I hope that Foreman continues to expand on this series as I really enjoy his writing style and his ability to confer what's truly important in data science.
This is a very different kind of data science book, and so far that's a good thing.As the author explains in the cover matter and the introductions, you can solve a lot of real business problems with data analysis and you can do it with a powerful spreadsheet. I learned some tricks with Excel from the first chapter I'm still getting a handle on, and I've been using that application for quite a few years.I'm hopeful that his model will work better for me and that I will learn some good analyses, and then perhaps some R ... in that order.
The book is easily understandable.  It leaves you feeling like you starting down the path of data science is very achievable.  If you are at all interested in data analysis and you are only familiar with spread sheets, this is a fantastic introductory book to help understand the concepts and theories, as well as give you some practical guidance to begin analysis.  I'd highly reccomend this book
Bad read. I returned the book. If you are not into marketing and advance data analysis, don't buy This
The book was very helpful to enhance my excel skills to the next level. Easy to follow and a great learning tool.
Get the e-book, download the sample files, do the examples as John walks you through them. You'll seriously come out the other end (6 months later in my case) a budding data scientist who can talk the talk and walk the walk.
Love this book so far. He mixes historical humor effortlessly into a book jam packed with info and data
Not just theories, this book has very practical and relevant examples to work on which goes a long way in understanding concepts.
I loved the way the author introduces Data Science. It is very easy to understand/follow. I was able to apply the concepts to some of the projects that I got involved in!
Smart book that gave me new perspectives on data analytics!
If you are not a programming guru, but are worried about what your business is missing by not having access to Big Data or Artificial Intelligence  or Neural Networks of a Fortune 500, Jon Foreman is your man. You can do a lot using Excel Spreadsheets if you have this book and take a little time to work through the examples. And if you are going to try to do machine learning in R, please, please start here. More than just learning the "how" to do basic AI, you will learn "what" to do and, most importantly, "when." If you are a manager, work through this book and become a brilliant one. While most of the programming and statistics and analytics and management books I have read are like chewing aluminum foil, this one is straight to the point, easy to navigate, and God dare I say it, even interesting. A. Bartel wrote a 3-star rating because he felt it jumped around. I can  see that to a point. This book is a balanced combination of practical highlights and careful navigation of technique. There isn't a lot of transitional philosophical or historical or theoretical material. But for me, that is exactly why I love this book.
It's really great book. I loves and enjoy it several months alreadyI read some books about Data analysis and all of them is boring, but this book is very logic and fundamentalIn fact, if you are not familiar with Excel and you already forgot all Math so it would be quite difficult to remember and practice, but it's worth
Excellent approach to data analysis. I am working thru it and thus far I have not been disappointed.
Iâ€™ve been involve in digital marketing and this book helps me a lot to make understand more about big data and data analytics
Used book for Data Management class and the step by step instructions in this book made it so easy to follow along and amazed at the work I created with this book.
Great book, data science from a different perspective. I would love more chapters bases on R language (as the last in this book). Data working in an engaging and entertaining way.
I originally purchased this book for a graduate class in data analytics.  I'm going to adopt this as the textbook for an undergrad class in data analytics next spring.  The reason I am here writing this review is that this is the second time in two days I have needed to look up the specifics of this book, to urge students to purchase it.
Not only is abundantly practical but also gives you enough theory to get on with the book, or take a little detour of your own and go deep on obscure stat subjects (or even history) which this reviewer may or may not have done.It is practical to the level of not getting lost on excel when parsing through gobs of text.  And now, I find myself telling others what the author states at the opening of the book, half of big data is about data cleaning.  Amen!
Foreman is an engaging writer.This book is aimed at someone like me, someone with a long-ago background in college statistics and a current general familiarity with Microsoft Excel.  Foreman uses Excel skillfully as a teaching tool. He demonstrates repeatedly how, using Excel, it is possible to piece together hundreds (or thousands) of data micro-bits to create useful statistical predictions.Foreman does not claim that Excel is an optimal tool for large data science projects. Data science professionals will prefer other tools (like the R language) for large-scale projects involving millions of records. Still, all by itself, Excel is serviceable for smaller-scale data science projects. And, as Foreman shows, Excel is useful as a teaching tool, to illustrate otherwise-difficult statistical concepts.The book is well-written and useful. Five stars.
If your dangerous with pivot tables, this will take you to the next level for sure. Nicely written and easy to understand.
At last a book that drops the entry-barrier to data science!  Foreman wrote a easy-to-follow book that enables you to do data science using Microsoft Excel. The book is rich with screenshots and step-by-step explanations on how to apply data analysis techniques.
The directions in this book are very helpful and informative if you are not already well educated in Linux based computing.  The concepts in Data Smart can easily (if you are a bit clever) be applied to other computing environments, but it is worth mentioning that all of the directions are given specifically for Microsoft Excel and/or Open Office users.
Appreciate that each chapter was organized around a realistic business problem. It made the content more approachable. And the first chapter is a great crash course in the Excel skills you pick up as a management consultant.
If you are a beginner to Data Science then read this book first.  This will give you a solid foundation in data science using python
Great book. I like the teaching style.
Very good book with lots of helpful tips to any data scientist. The writer did a good job in presenting complex algorithms in a simple pleasant way with lots of examples.
Great book, bought it in digital form several months ago, but had to have a hard copy for my desk at work.
The flow for the topics is well chosen and easy to follow. Focus on Excel, conveys the message- its about the through process and not the programming tools.Augmenting the book with necessary appendix of basic equations for each of the chapter would have made this book comprehensive.
Hits the spot - anyone who *needs* to understand at least some of the methods available to gain insight in large or small data sets should read this. Even those that know data (like myself) will learn something. Also just having it on my desk has sparked so many deep conversations - even my CEO stopped by for a chat. Big tick from me - nice work John.
Extremely engaging and well-written. Focuses on the concepts, and drives home a thorough understanding with detailed Excel examples. A perfect practical introduction to data science techniques!
Very practical and helpful text - lots of good examples and practical tips.
Excellent and practical overview of Data Sciences. The author's practical applications make this a must have book for Data Sciences.
This is an excellent hands on introduction. Keeps you busy and hungry for more. Also gives you ideas about where to go further.Been reading about 10-15 books on the subject and this is the first one that really got me on the right track. Wish there was a follow up course on coursara =D
Simple, but thorough examples in Excel with a final progression to R.  Excellent primer before diving deeper into other coding languages and machine learning techniques.
I'm going through the book for the second time, great guidance, easy to follow.
This book provides an excellent plain-english introduction to lots of complicated,  but very practical, data analysis techniques.  Great for novices or more advanced analysts looking for a more complete understanding of fundamentals.
I have to admit when I first started reading the book, I almost stopped reading since I felt like he was reviewing Excel basics. I'm GLAD I didn't. John does an amazing job of explaining data analysis from the ground up. If you are already an Excel guru just skip ahead like I did the first few chapters and dive in. The explanations, examples the John provides are actionable, well explained, and helpful. If you are looking for something in Python or R, this isn't the book for you. However, if you need to understand a P-value, Logistic Regression, Cluster Analysis, or Random Forest and how to get the analysis done in a basic tool like Excel, then read on.  You won't be disappointed.
This should be mandatory reading for everyone. Very well written and entertaining. Follow the instructions given in the book and see the magic happen. There is Excel before this book and Excel after
When I began to read the introduction for this book, after receiving it as a gift - 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 want 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 days.Data Smart is a wonderful resource. The use of Excel as a primary 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 sheets.Instead, it provides a way for John Foreman to break down these complex concepts into the fundamental components that make them tick. You start to see the patterns between seemingly disparate technologies that are actually built off the same few bits of logic. Things start to click.The writing and real-world situations are really what make 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 want to use the particular tool being described to solve the problem at hand. The whimsy and flare added by the author moves the plot along at a good pace. The problems are simple 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 great 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!
This is a very practical book. He uses worksheets to explain the methods. It is a good introduction to data analysis.
To date, this book is the most friendly and accessible intro to predictive analytics that includes fully detailed hands-on "how to".Eric Siegel, Ph.D.Founder, Predictive Analytics WorldAuthor,Â Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
Great book. Gave me insights on how to do simple Analytics to advanced Analytics. Guided by detailed examples, this book will bring the closet statistician in you to the next level.
Very easy to read book and great examples. Thank you for this book.
Very simple to follow examples...Step by Step Excel description force the writer to give up details very slowly...which works great for me....
Very interesting approach, but can someone please point me to the samples?  The wiley.com URL is not working anymore.
As a ex-Consultant with good Excel skills this is the book I needed to learn Machine Learning. J. 4man is one of us, and is hilariously smart.
Exactly what I was looking for considering my company is limited to excel.
Great book on analytics, should be required reading for all business analysts. If you are reading about big data and wondering what it is all about, read this book first!
Excellent and easy way of learning
Practically Teaches you the techniques of data science rather than baffling you with the buzz words and technology ...
When I emailed the authors about receiving supplemental information (see quoted book description below) I was told that since I was not an instructor they would not provide additional resources. Therefore, the statement below that was presented in the book description is false advertising as the supplemental information would not be provided to students or practitioners, just faculty.  Also, the book did not come with the CD or any data sets, etc."The book contains many real business cases from the authors consulting experience. There are Powerpoint presentation slides for lecture material, a solution manual and other instructor resources available that can be obtained by contacting the authors directly ( dbertsim@mit.edu , rfreund@mit.edu )".I would not have purchased this book if I had known that they would not provide supplemental learning information.Please note: Looks like the authors have changed their wording in the description since my communication to them, now saying they will only provide supplemental information to faculty.  This does not change the fact they did not provide the information as was indicated in the book description.  Its too bad as the only thing I wanted was to supplement my learning.
It is a good book but there are no solutions. I am disappointed because I want additional practice other than the examples. How do I know if I have understood the topic if there are no solutions to the end-of-chapter problems? How do I improve?
This book is written for mathematicians or statisticians, it is very abstract and difficult to understand
For anyone looking for an introduction to the basics of data models this is one of the best in a long line of books in this area and the main book you will need. The presentation covers this important theory very thoroughly, including all the major topics. The presentation is crisp  and while it will be best appreciated by graduate students and the industrial community, most of the presentation can be easily understood by an undergraduate audience with a strong background in probability.
Book for MBA class, was useful but difficult to model this info outside of a software to some extent, used the info on the PC more than the book
I used this textbook in my DMD class at MIT and I have to say it saved my butt many times when the lecture material was not entirely clear to me.  The book is well written and the practical examples they use are interesting.  With very little explanation, you can sit down with the book and learn or relearn the material.  Unlike most of my textbooks, I intend to keep this one after the class is over.  Very nice guide to a useful subject.
This book is well focused in the quantitative methods for decisiÃ³n taking process.I consider very attractive the presentation of the book and the examples are really interesting.I think that the depth in the topics is appropiate for students with some background in statistics and OR.
Dense creative examples! Really informative and challenging to learning these complex examples!
Great book to begin understanding management and forecasting decisions for you or your business.
Product damaged! It is like a second handed book!
Taking care of very deep topics, it is clear every explanation, making it easy to understand, allowing me to have very good results in the university.
Completely worth for the price... The actual price is really really expensive and this was a steal for what i bought.. Would recommend.
Thanks for the good purchasing experience - I received the book in the state mentioned when sold and whithin the time frame that I had expected
Got this because school professor required it for our python class. Was not happy for the price but honestly very pleasantly surprised by how good and in depth this goes. Will have to say the online videos, and Jupyter codes (our professor provided to us, maybe available on author's website?) are crucial to success in understanding of python. The Jupyter notes contains all the codes of the book so you can test run them and see how those outputs actually come out. Goes super fast in content, but feel I have a good grasp on the content and feel confident in even some more advanced python coding utilizing packages
This book covers some very basic Python and ML concepts at the level of middle or high school. It falls into the tutorial trap of many of today's computer programming books, though with exercises and projects, it neither discussed in greater detail nor provided a comprehensive reference of some very important characteristics of the Python language. I had expected more from a book at this price. It is fun to read the chapters on IBM Watson, ML classification, regression, clustering, neural nets, the MNIST experiments, but the coverage is way too shallow, more like a cursory review. I felt much like reading magazine articles. Do not expect to learn much about the concepts covered in the posted Table of Contents, if you are into ML. This book is NOT a college-level text.
I purchased your book "Intro to Python for Computer Science and Data Science" for the needs of my MSc program in Data Science. I must say that, despite my little experience in programming (I am a medical doctor seeking to expand my knowledge in this expanding, cutting-edge field), I am truly fascinated by the comprehensive character and didactic value of this masterpiece.
Both Data science and machine learning can be demanding subjects, this book is great to smooth that learning curve while gaining working knowledge.  I personally would like to add a whole chapter dedicated solely to Exploratory Data Analisis.
This is the best introduction to Python that I have found.
Great book for beginners.Has lots of free examples on author's Git Hub to follow along the book.
Easy to read and follow
Helpful examples and assignments.
What an amazing 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 many interesting topics in a hands-on approach. I have already heard compliments about the book from some of my students. Highly recommended.
Fantastic book by the Deitels. This book is perfect for someone who is just beginning to learn any programming language. The content is thorough and well organized. As someone who was assigned another book for my grad school program, I used this book as a supplemental resource. Highly recommend.
This textbook is a wonderful introduction to Python for learners of all backgrounds.  I use this textbook for my business analytics course with students who have little to no previous coding experience.  The chapters are clearly written with detailed explanations of the example code - which makes it easy for students without a computer science background to understand.  The modular structure, wide range of contemporary data science topics, and companion Jupyter notebooks make this a fantastic resource for instructors,  students, and Python enthusiasts.
No reply from the author. I can not read the book in my Kindle Paper White or via the CloudThe listing said it is a Kindle Book. I bought. I am not able to download/read in my Kindle Book Reader. Please help.
Terrible waste of money
Data science is a broad field and much of the material required to crack interviews is spread out all over the place. Cracking the Data Science Interview pulls all of the important concepts into one place, along with relevant interview questions. This book should be added to every interviewing data scientistsâ€™ bookshelf but should not replace textbooks in statistics (First Course in Probability, Introduction to Probability), data mining/machine learning (ISLR, Pattern Recognition, AIMA), and computer science (Intro to Algorithms, Algorithm Design Manual).
I doubt I'll buy another self-published book from Amazon again after being burned by two recent purchases.  If ever a book needed an editor, this is it.  Items quoted and not referenced, statements presented as facts and again not referenced.  At first I thought the author failed to run a grammar check, then found that there were also typos.  It felt like this was a transcription of some lectures without any of the supporting materials.  There was only one website with the URL listed, the rest of the references had no links.  If you want to just sound like you know what you're talking about, then this book might be useful.  The only upside was the cursory explanation of linear algebra, but considering how poorly this book was reviewed, I don't think I'd trust that introduction.
It's ok. The history and motivation sections are very nice. A lot of the math equations are blurry pictures, but the linear algebra section was written at a nice introductory level. There are grammatical errors. Some of the example algorithms are listed without much depth or consequence. Once or twice conclusions are drawn without fully making the argument.It's alright, but it could use an editor and a second edition.
It's extremely informative for the beginner. I would recommend this to anyone. Every person on earth should read this! After reading this Data Science book I am actually experiencing! This book has helped me greatly. Was pleased to learn more!
This book contains lots of helpful information. It's well written and doesn't cost too much. The author will remember that a beautiful book is hidden for us. Many thanks to the author for giving us such a beautiful book.
Complete guide to learning the data science. All information described in detail special for beginners. If you want to know all about data science from  this book for you.
As the other review remarked. If you've been using R daily for 4 years, this might be the book for you. If your are trying to learn R, this is not the book for you.This book is mediocre in it presentation of R and poor in it teaching of probability and statistic. It takes a special gift to teach those topics. They are exceptionally confusing to learn because of the plethora of seemingly random formulas. There are a good textbooks on this, but sadly, this is not one of them. I read the book online as part of a course and quite frankly, I'm surprised it was able to get published.The explanations are not particularly clear, the examples are glossed over and the organizations is a bit random.. The author is probably a whiz at stats, but that is a whole different set of skills to be a teacher of R, stats, and data science.To learn R,  Hadley Wickham and Garrett Grolemund are simply superb. Excellent communicators with a clear pedagogical strategy. R for Data Science and Hand on Programming with R are truly excellent books.
The book is helpful, providing hands-on learning. This, however, is the last book I will be buying from IBM Press. Many of the examples in the book are coded in R, but in the book is a link to "the book's accompanying website [which] carries additional, detailed documentation on how to perform the same analysis in other software." That link simply takes you to the product page on the IBM Press site, and there are no links to any additional documentation. When I contacted the publisher, they sent me the link to the sample chapter and a terse explanation of "the link provided is the only content that has been made available." So don't purchase this book expecting to get documentation for performing the analyses in other software packages as promised.
I have to say I do respect the words of Professor Haider, and I really wish he would continue on with his writing and write more literature. His book is one of the more Human Data Science books you can use everyday and in a corriculum. Your students will keep and cherish the book forever and could potentially use it in the office/on site. Real world issues, real job solutions backed by data science methods. Absolutely loved the book and I cannot wait to share with others who are developing a love for data.
Provides the necessary information and concrete examples needed to start with data science.
Very helpful to me as a novice attempting to learn more about the subject in my own
I bought the pdf version of this book at informIT.com. Since I have some background in probability - a key concept in statistics -, I've started my learning from chapter 6, which is well written and explained - Illustrations are a plus. Each topic is introduced by using a nice context - for instance, when it says how the t-distribution was conceived. Codes are written in the R language - version 3.1 - and are available for free at informIT. I am using this book along with Applied predictive analytics from O'reilly - Currently, I'm reading chapter 3, Data understand. Both are well complementary.
Great book and an excellent introduction to the field.
I like this new book a lot, for all sorts of reasons, and will probably use it as as a textbook for my graduate level introduction to coding for scientists the next time I teach it. The book is only incidentally about coding, but learning to code can be awfully dull if one isn't also learning, or at least reviewing, something else at the same time, and this book painlessly reviews the statistical concepts and principles needed for data analysis in any field of science. The exercises at the end of each chapter are separated into math problems and computation/data problems, making  it easy to assign a few of each type for homework. In over 400 pages I noticed only 40 typos, nearly all of them very minor, which is excellent for a first printing.
Great book to grasp basic probability concepts.Examples sometimes are a little bit detached, but otherwise a great book
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 not a beginner book, but it's exactly what I needed to learn the details for translating equations to code.
This book falls somewhere between a manual page providing one example per function and a cookbook, tending more toward the former. Examples are dry and most are constructed using random data. There is very little in the way of practical use cases. I bought the book hoping to get some inspiration for using numpy and/or pandas for some types of analyses I find myself doing, but that didn't happen. Probably I've gathered enough overview that I now can put together useful queries that will provide useful hits on Stack Exchange. I wish I had better to say.
Wes is the creator of Pandas but he is not an effective writer. This has left a bad taste of pandas in my mind. A lot of examples created in this book are using random numbers and this is a poor way of teaching someone as it's too abstract. Random number generated examples rarely have anything to do with data encountered in real life.This book's problem is the classic curse of knowledge. The author does not know what it's like to get started with pandas and what are the difficulties users will have.
As others have said, this book provides a good manual. If you have a project in mind and some programming background, you can adapt the examples in the book to complete the task.  That said, a lot of the book reads more as documentation than instruction, and the documentation is more sparse than the official pandas documentation. Furthermore, some of the examples are rather opaque in understanding the main point, and the use of random number generators for example data manipulation sometimes makes it difficult to understand what a specific block of code is doing.Overall, this book provides a jumping off point in understanding the capabilities of pandas as well as its strengths, but it wasn't terribly useful in even basic data science workflow and concepts.  For that, I highly recommend something like Hadley Wickham's "R for Data Science," which is much more approachable and rewarding in its use of example datasets, its more personable writing style, and its outlining of good practices for data science.
This book has been my foundation of using python as a data analyst.This book primarily focuses on the pandas Python library, which is awesome at processing and organizing data (Python pandas is like MS Excel times 100. This is not an exaggeration). It also introduces the reader into numpy (lower level number crunching and arrays), matplotlib (data visualizations), scikitlearn (machine learning), and other useful data science libraries. The book contains other book recommendations for continuing education.Although this would be a challenging book for a brand new Python user, I would still recommend it, especially if you are currently doing a lot of work in MS Excel and/ or exporting data from databases. I had a few false starts learning Python, and my biggest stumbling block was lack of application in what I was learning. This book puts practical tools in the reader's hands very quickly. I personally don't have time to make goofy games etc. that other books have used as practice examples. Despite other reviews criticizing the use of random data throughout the book, I found the examples easy to follow and useful. I would also argue that learning how to generate random data is useful in itself (thus the purpose of the numpy random library), and that there are practical examples throughout the book. Chapter 14 devoted to real-world data analysis examples.I am almost finished with my second time through the book, this time working through every example. This book has been well worth the hours spent in it. For context, I previously relied on Excel, SQL, and some AutoHotKey. This book has significantly improved how I work.Thanks, Wes and team.
This book covers all of the basics that you would want to know to get started in programming in Python for data analysis, as the title implies, but it doesn't really offer compelling real-world examples. The data seem to be made up and the analyses don't go into enough detail to help you really learn how pandas and numpy work. Overall this is a decent starter book but you will have to bookmark the python and pandas documentation online if you want to have a reference to all of the functionality those tools have, and there are many places online where you can get better examples to learn from. If you haven't made your mind up about which tool to use for data analysis, I highly recommend checking out dplyr in R, which has an excellent free book online (R for data science, hadley wickham). I find it very easy to learn and it is much easier to set up R and RStudio than it is to set up Python, even though I love Python and Pandas.
First of all, if you've never used python before find an intro to python. This book is more for people who are familiar to intermediate python programmers. Meaning this is not for the complete beginner.The book mainly deals with introducing you to Numpy and Pandas libraries used for data analysis, such cleaning, manipulating wrangling, processing and visualisation.Its a great book to have as a reference and learning data analysis techniques. There are plenty of code examples.  So worth the purchase.Only negative I wish there were mini projects to learn from.
This book gave me my first job. And I am still learning it. It is simple, talks some general idea why functions design like this, and introduces some practical functions.  Because in real life real job you always need to look up documentation or to google certain functions, I think the idea why Wes makes functions/variables like this, and what he wants to develop in the future is very important. anyway, I think this book is for data analysis beginner and some intermediate users. I learned Python first so I recommend beginners who want to use Python for Data Analyst/Scientist to learn Python Programming first/simultaneously. At least understand lambda and python expressions, otherwise, you can't feel the full magic.
The book is mostly about the capabilities of Pandas + Jupyter notebook (and a little on Numpy). The book is structured to be best as a reference book instead a narrative from start to finish. The problem with this approach is for kindle readers like myself, I find myself using stack overflow to look for answers more than searching for it in this book.It was useful to go through the book once, but i won't be using it often from here onward. I suspect that it will become outdated quickly and getting answers to specific questions from Google is usually much faster and more reliable.
Pros:You will see some unique functions or techniques introduced in the book. This gives you the ability to do advanced data manipulation or an advanced programming technique that you can come up with.Cons:This has to be one of the worst book I've ever encountered. The coding examples doesn't even work.Please be advised, this book is not for college students. It doesn't go in depth enough. The book is more geared toward basic beginner level for someone in middle school or high school.Info about myself:Graduated with a bachelor degree in Decision Science. This major has an emphasis in data analytics.Final Statement:I know a good book when I see one, therefore, another book I would highly suggest isData Mining for Business Analytics: Concepts, Techniques and Applications in Python 1st Edition (published by Wiley).
promotes python more than explaining ai
Great book. Well written & informative.
Awesome primer into Data Analytics. I followed all the tutorials and really feel like I started to grasp many of the concepts. Awesome preparation for Machine Learning!
Why would i buy a textbook rather than kindle ? I expect it to be high quality paper, with colorful graphs and beautiful paper layout but actually kindle version is much better than the paper pack. The paper pack is like i printer the book from a home printer. Shame on the company
Thank you for giving us a superior alternative to this R garbage
Good chapters on handling time series
The book is great. I find it to be more a reference (or a handbook) for pandas than a generic Data Science guide but I guess thatâ€™s its purpose. I find myself browsing it occasionally to see how to do something with pandas.I think itâ€™s a must for anyone using Python for Data Science or related tasks.
this book is really good for any beginner who wants to learn more in python coding. Everything is easy to understand. Able to develop your python scripting skills from beginner to pro
This books helps me to understand how to use Python for Data Analysis purpose. The topics are very well explained and it covers most topics which is essential for analyzing the data using Python.
Good to have one for reading
I have to complain about how the book came to me. The cover has broken corners and a couple of pages are in bad state. The back of the book also comes in bad state. Bad bad bad
Covers the topics that are complex and timely, while starting with a section to get beginners up to speed.
The text is dense and opaque.  It's not a good learning resource.  It's probably better as a reference book for someone who is already somewhat fluent with Python, pandas, and NumPy.
Really?????? Got it for no more than 5 hours and splits????????
Fantastic book if you are just starting in the industry like me
Excellent step-by-step instructions. Interesting examples.
I'm and avid R user. This book, in my view will help you to transfer your R skills to Python in an efficient manner. Overall it's a great book!
I bought the book about a year ago and its been sitting on my shelf for a while, I haven't read through it as it usually takes shorter time ask uncle google for an answer.last month I was stuck on a project that involved some serious data wrangling and no amount of stack exchange questions or online help seemed to be able to remedy that, so out of shear desperation I turned to this book for help, I wanted to read just a couple of pages to look for an answer, I did not set the book aside for 3 hours straight!!!.this book worked wonders, the authors exposition of the different tools and subjects was crystal clear, it gives each tool its right amount of attention striking the right balance of attention avoiding being not too lengthy-to be a documentation-, or too short - so you don't have a clue whats going on-.I ended up reading the whole thing over a weekend, I've read it twice since then, and I regret not reading it earlier.that being said, this book is not for everyone, if you're new to data science, and you're trying to learn the field, this book may bore you, as most of this book deals with data wrangling - the boring, but the inescapable aspect of dealing with data-, so if you're new, do some online tutorials or watch some Pycon videos, play around with some data sets, then read this book, its a great second read.
I know its hard to make a dry topic such as coding fun and exciting especially in a textbook but you really get the feel that the author didn't care if the message was conveyed well...as long as it was conveyed.
Comprehensive book about quite a few topics but managed to give us insight into all of them.
The first chapter is entirely missing from the book.
This is the best book on basic python I have yet seen ... Thanks Wes.Jim Osborne
A comprehensive, concise coverage of dissecting data efficiently with Python...
Python is wonderful for my job and the Pandas package are perfect to work with big data files!
I was looking for a book that could help me to learn python. I gave this book a try and I realized that the data analysis that I learnt from this book is pretty good from a pandas viewpoint (mostly).It does explain about numpy, matplotlib and seaborn libraries, but most of the time is oriented from the pandas perspective.Nevertheless, if you want to learn machine learning, numpy and other libraries, consider buying another book.All in all, I liked the book because it teaches you and really well how to wrangle data. I only had wish it had more numpy and other libraries.
The second edition is a worthy upgrade. Not only does Wes explain pandas but covers essential numpy, ipython etc. This is a must buy for anyone using Python foe data wrangling.
This is one of the best Python books I've used. All of the examples worked which is rare in programming books. Some of them had slightly different output, but I believe that may be due to time lapsed since the book was published and my working through it in Mac OS. Highly recommend, but be sure you have some previous Python experience.
I am not a programmer, but have been trying to learn python for data analysis for a while.  This book does a great job of explaining some basics that other books/programs tend to skip over.  Also seems like python is even easier to work with now than it was just a few years ago.  If you have tried to pick up these skills without success before, this book might be a good way to re-enter.
It's really comprehensive, and covers almost everything you might want to know in mainstream Python data analysis. I like that the examples are complete so you don't have to go and play with the code to understand. Yes, its good to play with the code, but I prefer this style. It's not exhaustive on Pandas, but it covers a lot more than that so you best go to a specialised Pandas book if that is what you want.
Overall, I liked the book. Between the first and second edition, however, the author reorganized the book that made it harder for me to absorb. In the first edition, he starts with some real world examples with the idea of leading you into the nuts and bolts. Python and the packages have changed so much, however, that you can't really use that edition. The second edition starts with the nuts and bolts, which is some pretty heavy slogging.
Great content. Five star content. But, pages started coming off the binding one day after I got this in the mail. Loved the first edition and bought this just for the updates, so I can definitely recommend the content. However I canâ€™t recommend the book itself.
Hard to follow, problematic explanations, and one needs simply a manual for this book. What a waste of time!