laptak2003/AmazonDataScience huggingface dataset:
Though the book was 800 odd pages, it never felt like that when reading this. The illustrations with complete Java code, and explanation of the underlying concepts through the applet was so easy to understand and also remember. I would really like to treasure this book for ever.
This is an excellent book for either a beginner or an intermediate developer. It explains various data structures clearly and gives detailed code sample, without nonsense. I really love it.
Great! Exactly what I was looking for. Excellent for someone with no formal training in computer science, but want to master the internals of data structures.Robert Lafore rocks !!
GOODï¼ï¼ï¼ï¼ï¼ï¼ï¼ï¼ï¼ï¼ï¼
This is a great book for beginners seeking to learn data structures and algorithms. I started this book as an introduction to data structures for Java programming but quickly realized that the algorithms and concepts in this book apply to all OOP languages. The structure of the book is suited for my learning style and hopefully yours (conceptual overview followed by examples and then a summary and analysis section). It also covers a vast array of topics with comparative analysis of different data structures throughout. The text is also a quick read, which isn't a knock on the depth of coverage because it is ample, which will allow you to use it as a jumping off point for more detailed and technical texts. Overall the conceptual understanding is really where this book strives as a teaching text for the beginner. I would recommend this book for anyone interested in the data structures and algorithms fields from one newcommer to the next.
I bought this book looking for sweeping coverage on the most common data structures and algorithms. I was not disappointed. Robert Lafore is clear in his explanations and his easy to follow style makes it so that I have a good, good grasp of the concepts presented. Most importantly, I can translate those concepts into code!I have tried reading a few other DS&A books, none of whom have been as great as this one.My gripe is that I wish that the book covered more, more, more! I can't seem to find a book that goes over more advanced concepts without handwaving over important ideas or explaining things at such a high level that I have trouble grasping what's really going on and how to implement it in code. If anyone has any suggestions for a book similar in teaching style to Robert's text, I'm all ears.Robert, please expand this book to include more. The world's present and future computer scientists need you!
Bought this book since my college degree was not in Computer Science. I do object oriented coding (Python and .NET), for IT automation at work. I wanted to understand data structures better due to the usage of arrays and hash tables for most of my projects. So far i've covered the first few chapters. Even though i have no practical exposure to authoring Java code, the author Robert Lafore, does a great job in explaining data structures/algorithms in the Java language. He uses simple everyday examples, we can relate to, so that non-CS graduates (like myself) can quickly grasp what is going on. I will say the book is not a quick read (728 pages), since there is plenty of code/concepts I have to ingest and understand, so i feel that it'll take me several months to actually finish this as i'm able to only allocate time to read this after work. This will be my go to book for many years, that is certain. Great introductory book for folks wanting to get into DS&A.
A great java book if your first or your hundreth on java data structures.  Well written book about data structures from arrays to Red-Black trees.
Has been very helpful. It's lacking on general t
This is a easy and fast read book on data structures and algorithms. The sample code is easy to understand. Very useful to quickly brush up on data structures for interviews.
Simple and effective, easy to understand with good examples and exercise at the end.
Great book and easy to read and understand. Great if you are getting ready for an interview
No pretentious wording, very clearly written.
A classic book for data structures!
It is a great book to learn basic knowledge about data structure and algorithms. And because the code is based on Java, so you can try them easily with an eclipse project.
This is book is ok. I use it as a reference. I have flipped through it and am now about a third of the way reading it. The reason for three stars? On kindle you may want to know that rather than 5 licenses it is limited to two. So plan on deciding which two you plan to use. This book is very difficult to relicense. Amazon had to reset the book by canceling the purchase and let me repurchase to get the licensing back.
This is poorly translated from analog to digital. The pages are not numbered, which makes following along in class harder than it has to be, and the example programs are blurry and do not change font or size with the rest of the text in the book. As far as digital texts books are concerned I would consider this an early attempt, which considering the book was originally published in 2003 it could be, but it should have been refined by this point in it's life cycle.
Ordered 4 of these, 100% Satisfied with the books. Brand new and high quality.
Great breakdown of highly technical details.
This book uses entire examples of code that would compile successfully so that I can follow and understand what is happening.  It is much better than the other book I have to use in class which only shows snippets of code examples.  I have to use this book so I can understand the other book.  What does that tell you.
Nice thorough explanation of the fundamentals of data structures.  This book makes a good reference book for when you forget something.
This is a great introduction to data structures and algorithms. But I bought the Kindle version and all the code examples are **images** rather than formatted text, so the are a) grainy as all get out, b) broken up into non-logical chunks and (worst of all) c) completely impossible to copy/paste. :( :( :(
very quick delivery and good value for money.
I still use this book here and there to refresh. I think it does fair balance in teaching the algorithms, and how to code, and why certain code needs to be in certain ways. Great book. Great seller. Thank you.
The focus is on data structures which seems to be overlooked more and more in undergraduate studies.  However, keep in mind, this book is about data structures, the Java is because they had to pick a language.
Its hard to find Data Structures books that written primarily focusing Java Language. The book doesn't have a great deal of Data Structure and Algorithm analysis but it has very detail implementation of the Data Structure and its algorithm. As a grad student there are times that I refer to this book to get an idea of come implementations. The book doesn't have too much mathematical information of the algorithm but sometimes I find that is helpful since most of the times I am just using it as an reference to see some implementations.I personally believe best way to start teaching data structures and algorithm is to use Java language because unnecessary complexity can be minimized drastically. This book can be a great asset to any programmer and can use as a great reference.Data Structures and Algorithms in Java (2nd Edition)
It is a good book for those who have vague memories of the little details of the data structure class in sophmore year.  The applet that comes with the book (i mean you need to download yourself) is very useful to refresh the concept.
I purchased this book when I was just not able to understand the basics of data structures but had some previous experience with that. I must say this book just changed the way I thought about the data structures and algorithms. I was able to understand more and more details of every algorithm as well as how they are implemented. The online applets that were available for this book were just so damn awesome. I must say, if anyone out there is struggling to understand the concept of data structures and algorithm, they must try this book. It is a very nice book, I highly recommend this book.
I've only been using the book for about a month. However, it's straightforward explanations and well-organized structure, as well as somewhat informal diction presents a welcome juxtaposition between rigor and clarity. One of the better data structure books out there. More on the algebra and calculus of Big-O notation would be helpful though, because that is one of the areas in which I've been struggling.
I am so happy to tell that I can read and understand most of complex data structures and algorithm concepts from this book.The book author explain all complex things very simply by using real world examples,I have no words to say,only I have to tell you is "This book is Awesome".Quality of the book is very good,This is the first time I'm reading a big book which has 500-600 pages,believe me the book is like a very much interesting story for me.
Only been through the first 2 chapters but the book seems pretty good so far, There are no page numbers in the eBook and this is very frustrating, especially dealing with the professor I have for this class, she does not want to help me on specifics on the assignments at all. Her assignment instructions just give page numbers for exercises but of course there are no page numbers in the eBook, although there could have been if the design of the ebook was better, I will now have to buy the paperback so I can try to pass this class without bumping heads with my professor and so that I'm not going back and fourth with her trying to find out if I'm working on the correct exercise or not. Thanks a lot.. not.My frustration is more dealing with the professor and the ebook not having page numbers, but the content in the reading seems to be good, everything is pretty understandable and I am pretty much a beginner in java.
Great, but intense brush up on Java for those interviewing with Google, Microsoft, Amazon or any of the other "rigorous interview" tech companies.
This is a gentle introduction to data structure for absolute beginners who only have a little bit background in programming. There's no math rigor at all, and materials covered are pretty basic. The code in the book isn't that bad compared with many other DS&A books, there are some minor errors but the readability is pretty decent, most of the time you don't have to make great efforts to decipher the code. If all you want is some basic ideas about data structure and you can't wait to get started by implementing them in Java code, this book is a great start.However, for more advanced topics and rigorous math, you need to read other DS&A books. Unlike CLRS, this is by no means a complete guide to DS&A, but I doubt how many beginners feel comfortable with way things are explained in CLRS.
The author did a great job of explaining concepts like a rational human being, which was very much appreciated.  It was instrumental in getting me through my algorithms class.
Awesome book, helped me a lot for my class. The explanations are easy to understand and the analogies make sense. The coding examples are through and are well explained/commented. I didn't use any of java workshop applets in my studies, but they seem useful for a visual representation of each data structure/algorithm.
great product got exactly what I ordered when it was scheduled
Absolute must read for software developers that do not have a CS undergrad.  You won't learn everything you should know, but you'll get a good start and will get an idea of what you should read and learn next.This book is easy to read, has good examples, and is way more approachable than the free PDFs that can be found.
Definitely a good data structure book.
Must have Datastructures and Algorithm Book!
I love this book. I have been reading books by Lafore since the '80s.
The best Data Structures book I've ever had! And I love the applets which visualize all these data structures and algorithms!
This is a great book for beginning data structures and clearly shows advantages and disadvantages of each. I would not recommend this for someone who already knows most basic data structures and basic search/sort algorithms unless they want a review book.
This book exceeded my expectations.  It is very well written and easy to pick up on the concepts of even the more complex data structures.  The chapters are laid out in a progressive manner so that the simple ones are explained, the downfalls shown, and then the next chapter explains a solution to it.  I really like the problem-solution orientation.  You will get more than programming knowledge from this book.  There are problem solving skills involved.The Stream API takes care a lot of these details in Java, but understanding how they actually work will make your use of it that much better.
Good examples of network analysis
This book goes over six Python projects related to acquiring and processing large scale data.  Of the 121 pages, if you take out the pages used to present snippets of Python code, there is probably about 30 to 40 pages of text left which contain brief theoretical expositions relevant to the project. The projects presented are interesting; but not unique in any way nor hard to find in other places.If you got this book expecting to find some original results in complex networks, given the authors' background, you will be disappointed.The best features of the book are the accuracy of the technical material presented and the extensive references.  Unfortunately those do not justify the high price!
Pretty good explanations. I found this a rather helpful text.The only major issue is that the code sections of the Kindle version are sometimes (very) hard to read. This can hinder comprehension.
Great service and book.
Rather dry as far as Computer Science books go, but it has many useful examples and very in depth explanations of the subject matter.
As others have mentioned, the formatting of the kindle version of this book leaves a LOT to be desired. The book contains tons of java code, all of which is presented in low resolution images, which can be difficult to read depending on your device and is obviously impossible to copy into a compiler. This book also has only two licenses (instead of the usual five)! This is a huge caveat for someone like me who reads on several devices. I was shocked to find that I couldn't download this onto my phone without paying for the book again after downloading it on my PC and iPad. Amazon needs to do a better job of surfacing this information.The book also contains several typos and a few wrong answers to the questions at the end of chapters.
I would highly recommend this book
The name is a bit of a misnomer, it does not through luck explore algorithms, aside from Dijkstra's algorithm most others relate to ADT's, such as red and black or AVL trees.  That being said, it's still a good book for introducing the basics for data and abstract data types.
Picked this up thinking that it was required reading for the class. It's useful, but not particularly clear. The Kindle version has the code samples as images and they are chopped up and quite blurry.
An excellent book
I did not end up finishing the class, but the book was recommended by some one I respected. It came in good condition.
Extremely obnoxious "license limit" for the kindle version.  Apparently after downloading the book to two devices you won't be able to view it anywhere else, and unless you can guess what devices you have it downloaded on (so you can go on and delete it) there is no way around it.  Also opening it in the cloud reader counts as a device- complete joke.  I had apparently downloaded it to a kindle app that I had uninstalled yet it still counted against the limit.
Absolute ripoff.  Apparently, the authors have restricted the book so much that if you access it from more than one of your devices, you can no longer read it.  I'm supposed to go back and deregister it from one of all my other devices, some of which I don't have access to right now.It's as if they literally stole my money from me and failed to deliver this product.  Who are they to control what device I'm allowed to read a Kindle book on, or that I'm only allowed to pick one of my devices to ever try to read this book with.Since I only ever just glanced through it, previously, but do need the book this semester, it's as if I never even got any use out of it.
Helped me get all of the females and now doctors are furious with me.
Book was in excellent condition and arrived even sooner than I expected. Will do business with again.
This book is simply unbeliavable! I have read 5 algorithms book before this one, and it beats them all hands down and eyes closed! It makes the topic fully intuitive and practical, and the exercices and summary on the end of the chapter are very effective for learning.Before this book, I have read:- Introduction to algorithms by CLRS- Algorithms by Sedgewick- The algorithms design manual by Skiena- Introduction to algorithms by Manber- Algorithm design by Kleinberg and TardosLafore make those other 5 books author look like as if they had no idea of how a human person learn. Those other 5 books focus too much on mathematics and forget give you any intuition about the topic. This happens consistently in any chapter you open on these other books.It seems like mathematics has became the charlatanism of didacticism nowadays: the more mathematical language the authors fill their books with, the more they shine as incopetent communicators. This book by Lafore is totally in the opposite side. His clear explanations in english and pictures makes each of his phrases more clarifying than many pages of mathematical description of algorithms (as you see in other books), theorem proofs and ridiculous 'rigorous' definitions of theorems.Of course, once you finish Lafore you will be able not to get lost in CLRS, and enjoy some,of the math there, but DO NOT READ ANY OTHER BOOK before reading Lafore.He is the only competent author (in term of didactics) on the market, and you will surely notice it as soon as you open any page of this book.
The reason I'm giving this book 3 stars: the theoretical information it contains is great. The explanations are easy to follow and the author does a great job for beginners who need to grasp these concepts. Really, the written(non-code) sections of the book are golden and, actually, I really enjoy reading it. With that out of the way, the code examples are not very good. You should, and will end up, referencing other material for coding examples, which is a bit of a shame because there's a lot of code in this book(which I was originally excited for). You can not rely on this book as a stand alone source of information for this reason. If this weren't the case, it'd be a superb book. Small things count and naturally assist with learning; proper variable names and basic java programming conventions. I think grammar; sure i can understand broken English but it'll probably be a little harder to analyze, understand, or worse, slow you down. There were mistakes in the code examples as soon as chapter 2. I thought maybe it was me, but even copying the examples, compiling them and running the programs resulted in compile errors? How is this? I have to say, this has been the most upsetting thing about this book and it's even more so because the written theoretical knowledge is so great. I'm a college student, I don't have much. I spent the few bucks I had to buy this source and I don't feel I'm getting the full experience and enlightenment I should, fully ruined by the code examples. It has slowed me down a bit. Amazon gives samples of the first few chapters sometimes, go through them and analyze them for yourself before purchasing. I'll go through the rest of the book because I want to get its worth and I'll come back to edit. I'm usually easy on ratings. I respect this author and am grateful this information has been compiled. It's definitely a good source of theoretical information.  There are also applets the author produced to go through of data structures and algorithms. It's said that visual representation has a lasting effect on picking up new information and concepts. There was a lot of work put into this book. Maybe I'm too harsh on the code examples when I shouldn't be. I've changed my rating to 4 stars because the rest of the book is freaking great.
Solid book. Clear and easy-to-understand examples. Although in Java, the book emphasizes concepts and theory more than anything, so it's a great place to begin learning the fundamentals of data structures and algorithms. The Java itself is readable so you can def understand what's going on as long as you know an OOP language. The scope of this book is purely to learn the fundamentals - if you want to learn more advanced topics like intractable problems, you will have too look elsewhere.
This book absolutely does not worth its price. Very little useful information regarding Data Science.  Very superficial. It is basically a collection of many many screenshot  pictures of someone working on various data science jumping from one subject to another. I learned very little from this book. Does not worth its cost.... Waste of money....
This book is about the tools that are available through Anaconda that are useful for performing data science - it is not about how to do data science.
This is exactly the book a practitioner who is picking up python is looking for. A cookbook that touches on every step of the model development cycle, with simple to follow examples. Only if it also shows me tips of doing general plots, and drills a bit deeper into pandas.
Book is quite useful, however contains alot of coding errors. I wouldn't recommend if completely new to coding in general, however if you are confident debugging (as the errors are generally small), it is useful. Not mentioned are some of the data files which are located on the authors github account (the url links in the book don't work also)
The book starts by explaining each function with an example that makes sense. But it builds on these pieces as the book continues.The approach is well disciplined and pretty quickly you are building solutions. A mix of theory and code with emphasis on the latter. You wonâ€™t learn calculus but you will learn how to do it from the code.Good for those who dropped out of Andrew Ngâ€™s courses and mortals wanting results.Me: 40 years development, MBA, and 5 years data science.  Imagining and prediction mostly.
Chris Albon has a broad and deep knowledge of data science.  This book is a treasure of hints, explanations, and examples for the use of Python and associated libraries, including Pandas, in machine learning.  It is not a text, but it much more than cheat sheets.Chris begins with common operations with arrays, vectors, and matrices -- loading, sorting, examining, analyzing, combining, modifying, transforming.  Then continues with procedures used to prepare data -- data handling, normalization, standardization, encoding categories, selecting date ranges, dimensionality reduction, feature selection, and cross-validation.  Several chapters review models, including techniques for model selection, hyper-parameter tuning, performance metrics, and discussions of fitting and validation.Each topic is clearly explained and accompanied by a short, self-contained, listing of a Python (Version 3) program as illustration.I highly recommend this book to all of us who need a quick refresher related to a command or procedure.
This is a great resource for quick and insightful tips for your machine learning challenges. As the author states, you do need to have python and machine learning experience to get maximum benefit from this book. I would argue that even those with less Machine Learning experience can learn a lot from the first 8 chapters. Chapters 9-21 are definitely best suited for someone that does have some experience using scikit-learn.Bottom line, this is a useful resource to have next to your computer when those machine learning problems pop up in your day to day work.
The layout on the kindle reader is an individual character rendering it impossible to read.
Tbh itâ€™s very broad, but shallow.
I loved this book for helpful code on python. It isn't too detailed because it covers a wide range of topics, however it really sets up a base to help google further. Highly recommend
Great book. Initial chapters on Data Mugging/Cleaning are very helpful. Def as advertised.
This book is perfect to have on your desk to look up a quick how-to on just about any data science task. It has everything from data wrangling to advanced machine learning techniques.
The purpose of this book is not to teach you ML. As a reference however, I've found it indispensable.  I currently have the Kindle edition which is great because I can just search on "random forest" or whatever. But I intend to also purchase the paperback to keep on my desk.  I also like the template the authors use: Problem, Solution, Discussion. Find the problem you want to solve, use the solution code as your template, but be sure to read and comprehend the discussion. I'm employed as a Data Scientist. I have over a dozen books on Data Science-type subjects, and the only two I reference almost daily are this one, and "Pandas for Everyone" by Daniel Chen.
I've worked with data and programming for over 3 decades and have never seen a worse structured technical book.This thing is atrociuos. Topics are haphazardly pulled together. Content jumps from tangent to tangent and skims the most important fundamentals.I fear reading this would hurt more than help.
This is a great book for someone wanting to get into data analytics and programming targeted at marketing.  Highly recommended.  It isn't perfect, but it is probably one of the best out there.
A fantastic "cookbook" written best as a textbook for marketing science. Although the book advertises itself as a complete book that covers concepts and theory and tools, it is not really best suited for someone that is just starting out. This is best viewed as a textbook or for someone that has advanced statistics knowledge. It will provide many useful examples and a step by step methodology for those looking to enhance or strengthen their data analysis toolkit. Of course, this is a large data set book ...Lots of code and examples are found on the companion webpage.All in all a solid book that dives deep into specific problems. It serves as a textbook and a great book to strengthen others skill set in specific areas.
This is an amazing book filled with excellent code, and it comes with all the data in GitHub at https://github.com/mtpa/
The organization of the book is confusing. You can't use the table of contents to decide what to read. After reading the first 4 chapters, I'm extremely disappointed.
Very good book, well written, and the best pas, as with all of Miller's books that I have purchased, is that it comes with real code examples in both Python and R. Great way to get up and running.
good book, but....no data sets to work with. Seems critical for a source code heavy book (ie almost every chapter has pages of code). We would prefer not to scan, then try to run the code ourselves. Read the appendix first at that seems to be where the theory is then go back to the chapters for practical work. Borrowed this book from the library....its really expensive otherwiseupdate: OK --kept reading, and paying overdue fines at the library, so I bought the book. Really worth the read if you're serious about focusing on marketing data science. Great starting read for technical marketers who want to do something in this field.Glad the code samples are now available. Will have to test and see. One thing I noticed about the content. Every time something got interesting Prof. Miller would quote a reference for further reading (ie. details). That's sort of OK, but leaves me wanting and having to go dig elsewhere. Suggestion: one more paragraph for such situations would put my curiosity at rest. A lot of content around product development, positioning, recommending, but a little light on broader examples - It might be helpful to describe a broader range of techniques (ie. list them), then drill down on one or two. It just seems too narrow, like drinking from a straw when really a funnel is needed with the huge alternatives. Enjoyed the book (looks like a text book but reads like a novel - that's a good thing)
If you want to have just one book on Marketing Data Science, this is the one.
Updated my rating from 2 to 5 stars as the code has become available on FTPress. I received an email from the publisher last week. Not sure why it took over 6 months for them to post this.
Good book, very well explained examples (the R and the Python codes are very well written) but if you have read other books from Prof. Miller, you would be able to remember some exacts paragraphs across some books.
Now that the data is available I will go through this book and do a proper review. But in general I do not like this book as much as this oneÂ R for Marketing Research and Analytics (Use R!)
This is a difficult book to review, and I struggled with it a bit. On one hand, it is well written with good use of hypothetical and relevant examples (.e.g Amazon, AT&T). On the other hand, it reads like a programming class - lectures and all, which can be dense and difficult to glean information from - not exactly the rapid fire approach many data scientists I work with/am use (caveat: I'm in life sciences).Pros:-Wealth of information - book is dense-Covers topics based on marketing not programming approaches (e.g. Recommending Products with approaches rather than Building Network Diagrams with marketing examples of how to use this technique)-Uses my two favorite languages - R & Python - very common and can be applied to modeling, charting and analytics more readily than other languages - they work well together - Python for building interfaces and specific R packages for doing the deep statistical/data crunching & visualization/presentation (at least that is how I use them)-Plenty of example code that can be readily used - sample data described in text available for download-I like that there is a list of Tables in the front of the book - makes it easy to rapidly find the right examplesCons:-This book is difficult to go through - you need to be comfortable with both R and Python-Book also assumes familiarity with common statistical/analytical approachesBottom line: as this book does not cover fundamentals of any of the core subjects (marketing, Python, R, Predictive Analytics) my gut is that to approach this topic you would be better served learning first predictive analytics and marketing concepts prior to this book being of full utility. That said it is incredibly informative and I found it a fascinating.
There is a lot of good information in this book, but I find it poorly organized and presented.It's hard to figure out the best audience for this book, and I think when you imagine it as a textbook in a specific degree program, where the instructor would know exactly the prerequisite classes taken by the students coming into the class, then it starts to make sense. Ideas toward the front of the book are not properly introduced and code is thrown at the reader without much in the way of explanation. In the context of a class, the instructor could smooth out these rough edges and likely deliver a coherent educational experience. But as a standalone learning experience, this is rough going.(I'm sure it's relevant what my own background is, which is completely comfortable with predictive analytics and programming, but ignorant of the specific way that "marketing" folks look at the world.)I find the book very strangely organized in that Appendix A, "Data Science Methods", is, well, relegated to an appendix, rather than being a central part of the flow of the book. It's a really good chapter!There is some tremendously useful code presented in this book, but again, the way that it's presented will make it of limited utility to those who do not have a guide in the form of a professor teaching a class or who do not have sufficient background to ramp up to the ideas presented.I am also not fond of the production qualities of the book, e.g:1. The paper is overly thin, so you can see the text on the other side of the page, which just makes reading harder to my mind.2. The code is presented with this horrible gray background. Black text on a  gray background (I dunno, maybe 30% gray) is not good contrast. So the code ends up being unnecessarily difficult to read.3. That this was created using LaTeX is obvious to me. Not because the typesetting is problematic, but because of the front matter, with the separate listings of Figures, Tables, and Exhibits. LaTeX makes producing these listings easy, but just because you can produce them doesn't mean you should. They feel anachronistic and strange.The fact that all the code and underlying data is available on the web is amazing, so for some folks, the difficult presentation will be worth slogging through to see the examples. But I fear that many would-be readers will just give up because the path is so rough.
The 14 chapters in this book cover a good variety of topics, ranging from how to conduct marketing research for the purpose of customer acquisition, segmentation, and retention; product design, positioning and promotion; and how to use and analyze social networks to better understand the determinants of product promotion success, etc.  The business, data collection, and model validation issues are discussed reasonably well, but the discussions tend to be a little bit jargon heavy, and some jargons tend to be explained only tersely, and sometimes not at all.The 14 chapters comprise only about 60 percent of the book, with the remainder being appendices and an extensive bibliography for further reading. Appendix A (Data Science Methods) is a very high level overview of the most commonly used analytic techniques such as regression and classification, machine learning, data visualization, text and sentiment analysis, and time series and market response models. Appendix B (Marketing Data Sources) is mostly about measurement theory, sampling techniques, and how to conduct surveys, interviews, and field research, etc.  Appendix C contains information about the case studies discussed in this book, and Appendix D contains some programming utility code.Readers will get more out of this book if they already have working knowledge of at least the R or the Python programming language, and some or all of the analytic techniques overviewed in Appendix A because this book does not provide any introductory tutorials on any of these topics. Solutions to the coding part are usually in R but many of the R code have corresponding Python versions.  All of the code and sample data are available for download from the website mentioned in the book, and many of the code are reproduced in full in the book as well.
An excellent overview and software implementation of marketing analysis techniques by a team of individuals with wide experience in both the academic and consulting/corporate environments. Also the first Data Science book of several in my possession to reflect the main tools used inreal data science applications and contrasting them in many cases side by side..  The code is excellent and well-commented although I wouldhave appreciated a greater discussion of the tools and methods in Python and R as they are used in the examples and in conditioning as well asanalyzing data for commercial and academic applications.  I also would have appreciated more of a discussion of how practitioners should choose between R and Python for various categories of practical applications.  The bibliography is extensive and up-to-date.I highly recommend this book for the business or data science student, marketing practioner or consumer of data science particularly in the marketing and business arena/
Valuable for both marketing professionals and programmers.  The goal of informed business decisions is a priceless target in my life and work, and data science is here to help.  My favorites, and the most impressive chapters for me, are Predicting Consumer Choice, Finding New Customers, and Retaining Customers, because these add value that is hard to come by.  For those with a talent or interest in math and programming as well, this should give you loads of inspiration and I could foresee some really great ventures, ideas, and entrepreneurship coming from the stimulus of this book.  The real-world examples, citations, charts, and graphs are excellent.  This is obviously not an "average person" or popular consumer-level "pop salesman" book, but much deeper than that.  The detailed programming examples are superb and thorough, such as "Analysis for a Field Test of Laundry Soaps" -- to give you an idea of this level of expertise.  The Appendices are fantastic, with lots of case studies, code, and data sources.  Highly recommended.
I admit to struggling with programming languages. Over the years, Iâ€™ve learned a few, but still consider myself to be a brute-force coder. Fortunately, with the growth of free-lance brokering services, over the past few years, Iâ€™ve been able to procure third-parties to write the code I need at reasonable rates. Python, however, is a different story. I want to be personally proficient in Python for a number of reasons. I started hearing about R a year or so ago and have read one very entertaining and informative book on using it to analyze baseball statistics. So I came to Professor Millerâ€™s book with high hopes and I am pleased to say they have been fulfilled. The second page of his Preface, Miller provides the clearest explanation of the relationship between Python and R that I have seen. It cleared up several points of confusion and questioning for me. Millerâ€™s writing style is delightfully light and familiar, eschewing the dead hand style of most academic work. He leaps right in explaining in very easy to understand language the nature of models and modeling. Yes, things do grow more complex as Miller explores his subject over the next 300 pages. Data examples are downloadable and the book itself is filled with clear illustrations of Millerâ€™s points. I still need to work more on pattern matching and Regular Expressions in Python before I can move into serious study of this book, but from my initial examination, it is certainly one I want to spend considerable time with. Just page 2 of the Preface makes this worthwhile for me.Jerry
This book is designed like a traditional programming textbook. You get some exposition about conceptual things and then you are given code. You are then left to determine how things work or should not work. In my opinion, there are possibly two types of users of books like these: (a) you are one of those people that can figure things out or (b) you need your hand to be held. If you are in the former camp, then this book is good for you. You get a good amount of conceptual discussion and then you are then able to implement the code without any problem. However, if you already know the concepts and want to really learn programming, my opinion is that the concept-then-code approach is improper. The case studies at the end are interesting, but no code and output are really presented. I don't know about Python, but for R you can show intermediate output that helps the student along the way to be able to determine where in the 150 steps they went wrong. It's something that requires a lot of work to do for an author, but in my opinion that is the most helpful thing for students wanting to learn how to program.
One day, a few months back, I was on a domestic flight and noticed that an older man across the aisle was flipping through a PowerPoint presentation on genetics.  What struck me in particular were some of the graphics.  They were 2-D full-color data visualizations.  As an engineer, Iâ€™m familiar with quite a few plotting packages such as Excel, MATLAB, MathCAD, IDL and PV-WAVE, but the graphics in his presentation didnâ€™t look like they originated from any of those.  After a few minutes, I gently touched his arm and said, â€œExcuse me.  Sorry to bother you, but what software was used to generate those graphics?â€  He then informed me that it was a program called â€œR.â€  I thanked him and went on my merry way, making a note to check out this mysterious-sounding software. Turns out this â€œRâ€ software wasâ€”and still isâ€”freewareâ€”like Pythonâ€”and quite powerful.  Itâ€™s primarily used for data analysis and visualization and operates like a command-line program that interprets programming-style code.  In other words, itâ€™s essentially a programming language with its own syntax and which lends itself toward analyzing gobs of data.  If youâ€™ve ever seen crazy visualizations that you canâ€™t make in Excelâ€”think of something you might see in the newspapers or various financial magazines. So, the author of â€œMarketing Data Scienceâ€ illustrates how to use R and Python to â€œlearn about marketing data scienceâ€ and points out that the best way to do that is to â€œwork through examples.â€  There are plenty of those provided for various subjects and I wonâ€™t even discuss what they are, but only state that in order to have a grasp of what this book communicates, you would likely benefit from an understanding of statistics, regression analysis, Bayesian methods and things of that nature.  Now, Iâ€™m not THAT guy, just an engineer, but I did find the sample programs in each chapter easy to follow and straightforward enough to understand how data was being compared, arranged and visualized.  I will likely, however, pick up a book geared more toward engineering graphics rather than predictive data analytics. One really great thing is that the author provides full R and Python codes to solving various problems.  As a Python programmer, I found that it was a sort of Rosetta stone to determining how to take a Python program and make it into an R program, sort of like some translated poetry books where the left-hand page is in the original authorâ€™s language while the right-hand page is in the translated language.
This is a well written book that covers real life market analysis scenarios and provides in depth usage of R and Python language. It is not a tutorial for any of them, however if you're already versed in data science, R and/or Python it would help improve your skills.The chapters are organized like "cookbook" styles. Which means there is a problem definition, and then samples, and the relevant code examples to actually solve the problem. After the main chapters there are extensive appendix and resources section to help learn further information on this trade.Overall the book is really useful, if you have some experience in these topics, and would like to improve upon them. I liked the language and the style as well, and found the book easy to follow.
Straight to the point and filled with case studies and code examples. Written to be easily read by the practicing data scientist.
By far, the most extensive review of marketing science techniques that I've seen. Miller's background in this field is incredibly apparent in this book. This piece is packed with information, use cases, and code examples for many key marketing science topics.A strong grasp of R and Python are absolutely essential to making it through this book; it is not a light read. I'd encourage any marketer interested in improving their marketing efforts to skim these pages, though most will need a data scientist or analyst to read and make use of this information. It can be a great conversation starter and will help you think about the first steps you can make to improve your marketing efforts.
This book is a handy reference and tutorial on learning how to approach marketing based data science type problems. The handy part is the abundance of code samples that show you how to approach the problem. Although, I am already familiar with Python and R, specific Marketing related topics (such as targeting customers or recommendations) are well introduced here. Once you have understood the concepts and the samples, that's where the data science begins in understanding the specific problem you are trying to solve, however this particular book helps set the stage quite well.
I had never heard of Python Programming.  I haven't done much programming since college.  I liked how they compared it to C++ so that you can see where to start from if that's what you're used to.  There is information on installations and data, including numbers and strings.There is a LOT of information on variables.  You'll find examples of coding and data structures as well.  In the 2nd part of the book, it goes into data analysis and how that works into what was already covered.2 books in one, so it's a great deal!
I brought it from Amazon. My friend suggested me to buy this. I'm very appreciation to have this book. A great deal of significant guidance of Python for Beginners. It is an extraordinary book for learning Ultimate Beginners Guide to Python Programming. I am using this book as a recommended text for my first year physics students who have never programmed before.
Iâ€™ve seen some books similar to this topic and in comparison, i prefer this one. It has been more extensive in terms of explanation and itâ€™s more detailed compared to the previous ones iâ€™ve read.
This is great for beginner's wanting to learn about python Programming even if they ha e never done it before. It teaches how to install the different  programs, data analysis and how it all goes together in a set of 2 books. Python Programming is very confusing but this book helps straighten things out.
This is one of several data programming and data analysis books I have browsed through lately. I think it is straight forward and informative. I am looking to take some online classes and just trying to get a feel for some differnt things I may want to explore.
This hardback are actually fantastically amazing to read, which is same informative about Python Programming. I am identical impress with this book, for the reason that i recognize therefore loads of new equipment from this book. The Ultimate Beginnerâ€™s conductor to Master Python brainwashing move by rung with realistic Exercises, you will realize the basics of Python and foster a platform upon which you will succeed, unvarying if you produce no preceding know-how at all, by means of the effortless advice that are limited in this book. If you choose to recognize about this clothes properly, at that time I ponder this put your name down for will be superlative for you. Iâ€™m awfully delighted to asset and examine this book. I exceedingly recommend this tome for individuals people who are truly incisive to live through about this things.......!!!
It's one of the best intro books I've come across, and is an excellent introductory resource for programmers who want to learn python quickly. Advanced programmers may opt for a more code-intensive read, but if you are looking for a straightforward, interesting, and fun read on your way to learning python, this is a great book for it.
I was recommended this book by my computer science professor for a C++ class. I wanted to learn python on my own and I can say, it's a very easy book to follow, especially compared to C++. I highly recommend it to anyone wanting to learn python l.
It's one of the best intro books I've arrive across and is an superior introductive resource for programmers who dearth to study python quickly. Advanced programmers may opt for a more digest-intent learned, but if you are countenance for a straightforward, interesting, and fun tell on your distance to erudition python, this is a powerful book of account for it.
This guide provides information on different aspects of the language and helped me learn more about different structures and functions in Python. I learned different ways to adapt my programming style to overcome some problems.
Have had this for one day and the cover is already coming off. Nothing a little glue wonâ€™t fix, and thatâ€™s the downside of paperbacks that you have to put weight on to keep closed, in my opinion.
This book was perfect for me and you might want to look at one also. you must find this book useful Python Programming & Crash Course. Especially the author explained this book very well.
Great inside!!!  Very good ideas and share lots of thing about programming in this book. Must follow the guidelines and highly recommend