laptak2003/AmazonDataScience huggingface dataset:
This book both serves as a very general to what  data is and how it may be used. along with a pretty technical description of how to "do" data science (e.g., use Python programs).  In this way, the book serves two audiences.  A more general reader might skip the technical/programming sections, while a technical person would likely skim background before getting into nuts and bolts,  It really serves (at least) two audiences, so there is likely a "buyer beware" for a less-technical reader who may feel they are getting only about half of a full "Dummies" book.  A more technical reader will likely see this a good introduction, but will use it as a springboard to more advanced material, including readily available computer routines in R an other packages.There is a lot of good material here- the book is both an introduction and a fairly advanced reference -- Recommended with caution.
â€˜Data science for Dummiesâ€™ is surprisingly comprehensive â€“ and not an easy read.  But the first three chapters give an effective overview of the subject requiring little or no background in statistics or engineering.The author is an entrepreneurial data scientist whoâ€™s worked with organizations including IBM and brings a realistic sense of the insights Data Science can bring to the table of a wide-range of business and industry clients.As a close-to-home example: Much of the targeted marketing that Amazon directs to customers in the form of browsing recommendations, emails, roaming ads, and feedback mechanisms are the direct result of data science efforts behind the scenes.The author does an effective job upfront distinguishing the often interchangeably-used Data Science and Data Engineering. The former uses math and statistics to draw analytical value from captured data while the latter focuses on the technical means of acquiring data (e.g. mining) using tools such as Hadoop, Python and R. The raw data itself is typically acquired from a variety of storage contexts and locations including traditional SQL databases as well as non-relational, unstructured data sources.While I agree with the authorâ€™s premise that itâ€™s not necessary to possess a PhD in Math or Statistics to function in a Data Science role, to get the most value from the book readers should have some background in â€“ or at least an inclination towards - math, especially probability and statistics (what they called â€˜Quantitative Methodsâ€™ when I was in school) along with some programming background â€“ at least using a high-level, object-oriented scripting language.The book wonâ€™t teach you enough about Python, R or Hadoop to get through an engineering interview, but itâ€™s enough to point you in the direction of more detail. By the same token, while it discusses various distributions, regressions and clustering techniques, I would not rely on it for â€˜from scratchâ€™ learning if youâ€™re completely new to the field.The author repeatedly makes the point that both Data Scientist and Data Engineering roles include a fair amount of skills overlap. I would suggest the book is more appropriate for readers with Software Engineering backgrounds looking to work with â€“ or in a some adjunct role as - a Data Scientist rather than the opposite â€“ a scientist looking for an in-depth technical background.The writing is excellent, serious without being stuffy, but free of the sometimes excessive cornball humor other Dummies books have. The part of tens â€“ often throwaway â€“ is full of genuinely useful links to a variety of data visualization tools and resources.I learned a tremendous amount from this book and plan to keep it close at hand as my own job increasingly takes on data engineering aspects and projects.
Data Science is the new buzzword du jour - how to extract information from data, but more than that: make it useful. Pierson assembled a book with some very cogent and relevant examples, but it's hampered by some lack of explanation, editing, and writing. In some cases, Pierson uses technical jargon that she doesn't explain. In other "Dummies" books, that's usually accompanied by a little call-out that defines it. Unfortunately, there is a lot of jargon without the call-outs, which might make it difficult for someone new to the field to understand.As an example, in looking at "extracting meaning from data" - "If there are extreme outliers in the z-values of your dataset, make sure to either a) transform  your data first or b) exclude that spatial area from your interpolative kriging analysis. Never remove outliers not caused by measurement errors because these are typically the most interesting observations; for example, they might indicate a hotspot of contamination."Ok, great. How do you define "measurement errors"? Could it be due to sample size (not discussed), random variation, poor data sampling? There are times it is completely appropriate to remove outliers from a statistical, methodological, and causal perspective. Also, the "transforming" of data isn't discussed. Will people know what to do with that? There are times this reader wonders if any non-technical audience edited this book.It takes a big leap. "Intro to probability and statistics" to "clustering and nearest neighbor algorithms." WHOA. That's a fast progression. Nearest neighbor didn't get discussed in my coursework for 3 stats courses. Jump in and hold on to your bootstraps! (statistics humor)For the data visualization pieces, Pierson focuses exclusively on d3, which is a JavaScript based language. For people who are programmers or who want to develop that skillset, that's a great route to take. However, there are a TON of free or low-cost options for doing viz that don't require programming. They got short shrift in the book. Also of interest is that R, which is mentioned as a computing tool, has  a number of packages that do data viz (and R is free), but they also aren't really covered.Pierson would've provided a better service if the target audience of the book had been better defined (managers considering learning about data science; database managers thinking about moving towards more data science techniques; statisticians or other methods based analysis professionals looking to enhance their careers, etc) because it reads as though it's trying to be too many things to too many readers. There are some gems in the book, so it's not wholly without merit. But if you are an experienced data analyst, only a few points will be relevant. If you're a neophyte, some of it will likely be over your head. Keep that in mind when reading Data Science for Dummies.
Do you pull data from databases to create reports? Do you produce pretty pie charts and graphs?  Do you have the foundational skills but want to learn a bit more? Do you need just a bit more to put it all together?Then, this book might be for you. It does help to open up the world on the world of Data Science which is formalizing around us as we speak. I found it less of an intro and more of a conceptual how to manual for putting some concepts together to help you delve into the world of data science.  Topics like regression, clustering, SQL, HTML, algorithms etc are tossed around like you know them already, because you do right?  So, in that sense, it's not an introduction, but to the person who has a solid foundation in these concepts this book can be a godsend and expose you to some bigger concepts that you'll quickly understand but that you hadn't thought of before.  I would not label it for dummies, I'd label it for college grads with a solid foundation in computer software like databases, Excel, visualization tools, etc.  If that's you, you'll likely love the book.  If you're entirely nontechnical, you're probably going to be frustrated with all the jargon.  At least to me, it wasn't an introduction.The book is fairly true in its marketing but this is not a detailed how-to, it's more of a conceptual how-to book.  The last bullet in the marketing might be a bridge  to far.  On the front cover it reads:LEARN TO:- Deduce, discover, and communicate valuable insights from structured, unstructured, semi-structured, and unstructured data sources- Use meaningful visualizations to display and interpret data- Take advantage of data processing tools like Hadoop and MapReduce- Turn your organization's data into a competitive advantage.In the end, I surmised that:Data + Statistics + Visualization + Creativity = Data ScienceI hope this helps you decide if this book is for you.
I work in healthcare, an industry that is rich in data. Unfortunately, much of that data is disconnected, poorly structured, and often not very reliable or information-rich. Turning problem-ridden data into useful information is a central part of my employer's business. As someone with a background in statistical data analysis but little experience with the analytic techniques now being developed to extract actionable "intelligence" from "big data," I had hoped that this book would give me a solid introduction to the field."Data Science for Dummies" is not the book I was hoping for. If data science has a theoretical foundation to guide scientists in their exploration of data, that foundation was barely evident in this book. I found the book to be much too focused on specific software applications such as the big-data-wrangling packages mapreduce and hadoop, with too little attention given to general principles. By focusing so much on technical issues and software tools, author Lillian Pierson set the wrong sort of tone for me. She further soured me on the book by making a number of errors in her chapter on statistics and probability. (E.g., Pierson says, "A weighted average is an average value of a measure over a very large number of data points."  Whether an average is simple or weighted has nothing to do with how many data points are involved.) Another turnoff is Pierson's strongly business-centered approach.Despite these problems, the book has definite value. Chapters on clustering and classification algorithms, spatial statistics, visualization techniques, and programming tools may not be elaborated enough to allow a novice to usefully tackle a problem of even the smallest degree, but they do point the way towards self-learning opportunities. They also offer links to significant Internet resources. These are both core features of the "Dummies" books.Bottom line: This book is not going to tell you how to turn a petabyte of garbage data into a brilliant dissertation. It may, however, satisfy some of your curiosity about the analytic techniques now emerging to help make sense of the ever-larger agglomerations of data that we are gathering. I'm sure there are worse places to begin.
As a fairly new digital nomad, I am exposed daily to new streams of seemingly-boundless raw data that I can barely grasp, but that makes me think â€œdang, if I just knew how to manipulate this and make meaning out of it, I could create so many neat things and make the world a better place!â€ At times I feel overwhelmed and yet keenly aware that I desperately need to know more just to stay afloat in the digital ocean I am swimming through.I have studied college-level statistics and programming, but I am neither a statistician nor a programmer. I was looking for a book that could introduce me to the field, get me used to the language of big data and dip my toes in the water without drinking a whole pitcher-full of the electric kool-aid.Enter Lillian Pierson, and Data Science for dummies. To me, the â€œfor dummiesâ€ series always means informative, understandable, amusing excellence, and Lillian Piersonâ€™s addition to their line met and exceeded those expectations. As soon as I started hearing Lillianâ€™s voice, and understood how accessible the information was, I really enjoyed reading it, and now feel much more comfortable around the vocabulary and ideas than before I encountered the book.I canâ€™t speak to the practical applications of the book, since that isnâ€™t my thing, nor was it what I was looking to take from it. However, as for a basic primer to get you started in understanding the world of data science and the theories behind it, Data Science for Dummies earns a solid five stars from me.
Data Science For Dummies is a perfect foundation to begin with for the journey into understanding and applying, visualizing and extracting meaning from the increasing sources and volumes of data. The need for this expertise is growing exponentially and Lilian's book is the perfect gateway for comprehension and exploration.
Nice introduction to data science.  Well organized, easy to understand, covers the basics.  Not the definitive source for this topic, but a fine introduction for the novice.
Great book! Congratulations Lillian on the book and the family! Much love!  Jack
The book meets expectations and is easy to understand for anyone without having knowledge of mathematics, statistics, etc. I really enjoyed the book. Thanks Lilian!
A simple first step into some powerful ideas.
Very good start for a shiny Data Scientist career!
Very good for people who want to know what? Where? And how?
This book adds little in the way of know how, and merely presents the data science landscape. If you want to read 350+ pages just to know data science instead of how to do data science, then be my guest.The rest of us will rely on google.
Great introduction into the vast field of data science, lots of information (from relevant maths and statistics to infographics and presentation tips)  in there but still easy to read. Though I wish I bought a hard copy since the electronic edition is not so nicely formatted
Good book to help get a basic understanding of data science.
Got it as promised and on time. No complaints hera.
Excellent overview and how-to guide to data science (and some data engineering topics).  A number of analytical techniques are discussed ranging from basic statistics and techniques of information visualization, to more specific and advanced methodologies for analyzing structured, semi-structured and unstructured data sets of various sizes and data velocities.  Specific analytical techniques such as clustering, classification and K nearest neighbors are discussed.  Programming languages and techniques are documented sufficiently to begin coding and select techniques which require further documentation.  Data manipulation with Python and R, as well as Excel are described and some detailed examples are given.  Sql data manipulation is also documented as are tools for Information Visualization.  Finally applications ranging from environmental modeling to criminal activity prediction.In all cases as little jargon as possible is used and these complex topics are described in a way that an intelligent layman can at least understand there application, and those with some background in college statistics or information technology could actually implement solutions using these tools (if supplemented by their help files and documentation).  A clear and respectable introduction to data science and some aspects of data engineering.--Ira Laefsky, MS Engineering/MBA formerly on the Senior Consulting Staff of Arthur D. Little, Inc. and Digital Equipment Corporation
Data Science for Dummies by Lillian Pierson is a 364-page educational book that introduces the reader to data science basics while delving into topics such as big data and its infrastructure, data visualization, and real-world applications of data science. It is a well-formatted book, and Piersonâ€™s use of charts, graphs, and pictures helps the reader further understand the material.One of my favorite sections of the book was Chapter 9, â€œFollowing the Principles of Data Visualization and Design.â€ In this chapter, Pierson talks about creating basic types of data visualizations, tailoring them to your audience, and crafting powerful visual messages using the right data graphics. I especially liked the part when she talked about incorporating design artistry into your data visualization that invokes an emotional response in your target audience. Throughout the book, readers can see how helpful and practical data science can be when applied to real-life situations.The book certainly covers a wide variety of topics; Chapter 4, for example, talks about machine learning, while Chapter 5 discusses math, probability, and statistical modeling. And you can go from learning about making maps from spatial data in Chapter 13, to learning about using Python for data science in Chapter 14. The variety of subject matters covered in the book makes the text fun and interesting to read.I believe that an introductory guide to a subject should follow two almost paradoxical rules: it should be easy enough for a beginner to understand, but also contain enough rich and engaging information for the beginner to truly learn something about the subject and set a good foundation for additonal studies on that subject. Fortunately, Data Science for Dummies follows both of those rules; the content is easy for a beginner to jump into, but the book is also thorough enough to really educate the reader about data science.*I received this book for review*
This is not a coding reference, a statistics textbook, or a machine learning tome.It's an introduction that in very relatable terms explains the breadth and business case of data science.There are great ideas in this book for what to study, how to study, and what options are available for specialization.To me, the most valuable part of what Pierson brings to the table here is how to "pitch" data science (and the products it encompasses) to management/clients and how to assess opportunities for the implementation of it. She communicates benefits and concepts in a way that is conducive to the conference room conversation.Taken for what it is, I believe this book is an excellent resource.
Data Science is hot topic today. There are companies now with whole departments called Data Science, awakening CS and Stats academics, get business folks interested in how much money is hidden in bytes upon bytes of backed up "garbage" data. So it's only fitting it's a great candidate "for dummies".This book focuses very much on the "for dummies". It surprised me how quickly it introduced Hadoop, and how quickly it finished introducing it. I feel that this is great, because a lot of the target audience probably heard of Hadoop first, Data Science second.The chapters on algorithms are very too the point. All the popular algorithms are here, condensing a typical CS AI text book from hundreds to dozens of pages (with generous spacing). The point isn't to learn how to implement them here, but more to meaningfully participate in meetings when R&D starts mentioning these names.Much of the what we consider the purpose of data science is for making business money, so I think it's great that the author dedicated large portion to visualization, including D3js. The book wraps up with examples in Python and R, and then some ideas for real world application.The organization of this book is very logical and serves very well as an introductory work. I sometimes find the format difficult to follow because there are just too many short sections, which is jarring when trying to read it through like a book instead of like a reference. But I can see how if each section expands, the book would be just too long.
I think the author and her editor(s) missed the mark here. Donâ€™t get me wrong; this book is an okay introduction to the concept of â€œdata scienceâ€. But it attempts to reach too many audiences.Big Data, as it has come to be known, is nothing new. Big Data has abounded in our nation since its beginning. Just think of the Decennial Census and the myriad ways in which all its compilations are put ot use in planning everything from voting districts to predicting the need for sewer systems.What is of relatively recent origin is the ability to compile and, more importantly, mine data in any organization.Knowing what such data compilations are, what they might be used for and judging whether or not the expense of compiling and processing them is part of what this book covers in an elementary way. And it does it fairly well with copious illustrations and moderately good writing.Where it goes off the rails, I think, is the inclusion of so much technical material for essentially a non-technical audience. The users of Big Data donâ€™t care whether something was accomplished with Python, R or a loaf of rye bread. Chapters like â€œUsing Python fro Data Scienceâ€ and â€œUsing R for Data Scienceâ€ seem misplaced here. An illustration of â€œDefining a Class in Pythonâ€ isnâ€™t wasted on me, because I know Python. But a manager who doesnâ€™t and is merely interested in learning about the benefits of Big Data probably wonâ€™t understand or care.So is this worth reading? Yes. But non-technical and technical types will probably find themselves skipping large portions of the book that arenâ€™t really of use to them.Jerry
This is an ambitious book that sets out to provide an overview of a complex scientific area in 400 pages. It is doomed to fail on some level -- or, alternatively, your expectations will not be met on some level. But it really does a pretty good job of capturing what's going on when people say "Data Science".... just not in any depth.As an example, the book covers probability and statistics in 24 pages and machine learning in 26... obviously, you can't learn these things so expediently. And certainly, some of the ideas covered are done so briefly that they will make sense only to people who already know them, or maybe those who almost know them.This book touches on the main ideas and technologies that people are buzzing about in Data Science these days. So, pick it up with the expectation of getting a sense of the lay of the land, and getting oriented to where you want to start learning, and you will be well served by this book.
This is a somewhat uneven presentation of Data Science.  Much of the technical material is covered only superficially, while easier concepts get a treatment that is more in-depth than they deserve.  I would say this is an "overview" book:  it introduces the concepts, but it does not provide enough details to DO much.  Many programs are mentioned without much help about how to use them well.  This is a buffet type book, where you can pick & choose, but there is never enough to get a full serving of what you really like.  If you are interested in Data Science and "just looking" at what might be helpful, this could be a good place to start; however, if you actually need to solve any problems, you should pick a book that gives more detail about those topics that are particularly applicable to YOU.
The book is a typical example of assuming that you are a 'dummy' with hardly any knowledge of the topic ( Data Science) and you have heard about it somewhere on the internet and wants to learn more about it. Many of the books in "Dummies" series still fail to reach the audience ( As An Example, a Horrible bookÂ Getting a Big Data Job For Dummies). HoweverÂ Data Science For DummiesÂ hits it out of the park.The book has everything related to Data Science, a brief few Pages on Hadoop, Few Pages on MPP ( Massively Parallel Processing) systems, NOSQL Databases etc. It even covers the hazy relationships between a Business Intelligence Tool vs Data Science. From there it takes to some of the best topics on Probabilities and Statistics. In a single paragraphs, explains in simple layman terms what is a random variable, what is a linear regression , Poisson Distribution and so on. That follows with much deeper topics on Data Science.LEARNING DATA SCIENCE: If you want to learn to be a data scientist ( need programming experience) try  Data Science course provided by coursera ($500) or Harvard ($8000) or many universities are providing in their curriculum. But you do not need a car mechanic to enjoy Driving. Read These Books>Â Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schonberger (Mar 5 2013)>Â Big Data at Work: Dispelling the Myths, Uncovering the Opportunities>Â Data Science for Business: What you need to know about data mining and data-analytic thinkingÂ ( THE BEST START YOU CAN HOPE TO THINK OF )SUMMARY:I am an Aspiring Data Scientist and have read Several books . Believe me, this would be ranked alongwith the other masterpieceÂ Data Science for Business: What you need to know about data mining and data-analytic thinking.
Data Science for Dummies is an excellent primer by Lillian Pearson and is a must have for anyone even remotely interested in Data Science. It is a comprehensive and an easy read. For anyone wanting to make a career in Data Science, they would do well to read this book as this would sufficiently equip them to break into the industry. The author has excellently explained the difference between Data Science and Data Engineering which are often confused to be the same by most people. Though the book won't teach you the programming skills needed for Data Science, but it will definitely point you in the right direction.I learned quite a lot from this book and would always keep it handy.
I wanted a little refresher in Data Sciences as a GIS professional. I found this book very informational, although pretty technical for average person without much computer technical knowledge. There's a little bit of everything from open source to Excel, Geographic Information to best practices in dashboard design, using python to crime analysis. There's a lot of information packed in this little book! It doesn't go into great detail, but gives a good, brief overview with technical jargon that will be sure to impress your co-workers...oh, and help you with understanding data science related to your work!
High level introduction that is quite ambitious as it touches on several different mathematical techniques and software technologies and methods of transforming the collected data into practical insights via visualization tools, etc.  You will certainly not be a data scientist when finished with this book, lol.  However, if you are looking for a way to learn how data models are used and applied, this survey book is for you.  You would then want to take more depth education on statistics, multivariate analysis coupled with SAS, R, SPSS, etc.
True to the spirit of the â€œfor Dummiesâ€ series, this book is written in a friendly style, with a language easy to understand also by the non-experts; personally I found the descriptive parts very interesting (those explaining what Data Science is, how to effectively present Data Visualizations and the real case studies about how Data Science is applied to the real world), but probably because I already knew most of the basics about Statistics, Machine Learning and Programming Languages, I found these parts too long if the pupose was to give only an introduction and too short to cover them in detail. Anyway, all things considered, I think the book provides a useful overview about what Data Science is and, with the huge amount of references about the resources available, it can be used as a starting point for deepening the knowledge about this wide area of expertise.
Great into to big data. Big data is everywhere in corporate america. This book is a great introduction and a precursor to more advanced study. There are nice industry case studies and even a basic primer on stats and probability. All in all, any one looking for a good intro to big data will find this book highly useful and very informative.
Data Science is a broad field that is still so new. The term is starting to be used a lot and this book helped me understand what all is included in Data Science and how complex it is. Pierson did a good job of covering a lot of helpful topics in a short book. I thought this book was helpful and I would certainly recommend it to a friend.
Excellent overview of the many of the different topics that comprise Data Science.  Great introduction for someone who wants to understand more of the details of the skills and tools that a data science needs to master.  Plus it's a very easy and enjoyable read!
First of all, I bought it for clustering purposes. There is nothing related to it. I returned to the book immediately. It seemed like supervised learning only.Secondly, I wouldn't call it a book. It looks and printed like a BORINGLY formatted master thesis. Although it is listed as a book, I would like to call it SURVEY PAPER
The book focuses solely on the machine learning algorithms. The background in math is needed but the best part is that the equations are explained by actual simple examples using numbers and solving the equations using those numbers. Brings the intuition of how simple additions and multiplications to explain complex underlying algorithm.
Short sighted
It has math stuffs for ML beginners with 1 or 2 example, which I liked the most. Price is bit higher for this book though. Overall book is great
Great start for the one who wants to understand maths behind predictive analytics
I am just getting into data science and I find this book to be full of valuable information.
Please don't waste your money. It's not even worth the $10 for the kindle version. Im just so surprised this made it into a book. I feel like I should write a book.
This is the first book to finally succeed  Programming Collective Intelligence.The Python review highlights those features important to data science principles. Later in the book he covers topics important to data science.
Complete manual for learning the information science. All data depicted in detail uncommon for fledglings. In the event that you need to thoroughly understand information science from this book for you.
This book is clear and concise in getting across its theme. I am very happy to have found this book, itâ€™s opened my eyes to a lot. Highly recommended to all for this data science guide.
Excellent book for data science. Tips and tricks for new apps and features are what I really like from this book, also it explains everything very well. Really this book is so helpful.
This is the only book that looks authentic and real. While many others are just a random compilation of information as far as i know. refer to all for this data science guide.
Great read if you're starting out in data science has no typos and is easy to read throughHighly recommended if that's the first book you buy
The Python review highlights those features important to data science principles. Later in the book he covers topics important to data science
So much valuable info that I was taking notes on each chapter. I then purchased the hardback. Then I discovered this data science guide.
This book will give you a great place to start and afterwards, you will have enough knowledge to make an educated choice on what educational resource to use next.
This book step by step guide was very helpful. its a great written book i think.I am glad to purchase this book.its provide a good information. It has a vast information to change anyone.I really liked the book.I dont think we should worry about information. I recommended highly this book for everyone.
This is an excellent book for anyone interested in data science or machine learning.
When I received this book and opened it, I knew there was a problem. There is very little flow between sections and even paragraphs. So, I typed in some phrases from the book and immediately got hits from various blogs, websites, and Microsoft documentation pages.I've taken each sentence from this book, searched for it in Google, and found it taken from a website. Appallingly bad and should be taken off of Amazon.
I am not sure how this book got such good reviews.  The text is full of typos, missing words or spaces between words, and misspellings (such as writing â€œEâ€ instead of â€œRâ€ for the programming language.  All of the commenting in the code was in Spanish.  I am willing to overlook some of these mistakes but the quantity of mistakes made it virtually unreadable and I am giving up after 1 chapter.  Wish I could return it. I am only giving it 2 stars because it seems like the content is well-written other than the complete lack of editing.Also I did not get the free e-book as promised in the description which was disappointing.
I did not get to the main points in this book because the English is fragmentary and badly written, making it excessively hard to follow.
The book starts off with you imagining as another data analyst for a narrative long range relational correspondence arrange for Data Scientists. Which was outstandingly promising start and I was on edge to see how this "character" would deal with the data issues they would stand up to... Spoiler, barely ever discussed it. The book should focus more on the basic idea of data count and use groups. In this book, the basic considerations were not shown so well which makes it hard to seek after the code which write in staggering nuances. The writer talked about here especially organized that remarkably satisfying. This is an incredible book for anyone motivated by data science with Python.
It's incredibly instructive for the novice. I would prescribe this to anybody. Each individual on earth should understand this! In the wake of perusing this Data Science book I am really encountering! This book has helped me incredibly. Was satisfied to find out additional!
This book contains loads of accommodating data. It's elegantly composed and doesn't be excessively expensive. The writer will recall that a delightful book is covered up for us. Much obliged to the writer for giving us such an excellent book.
This book really great, after reading this book i am so impressed. Thanks to author and would must recommended for this book to anyone. So glad I purchased this book.
Such a smooth reading. On es I started it, just couldn't put it down. Loved it. Highly recommended. You won't regret it.
Inspirational, actionable and effective! and very readable. This book has definitely helped me so far, solid principles!
The book it written clearly and would be helpful to any beginner, however, I'd suggest something more challenging.
I liked this book. It is good and informative. Re-read many times over. Wow!!
Nice little starter book! Great price! Highly recommended!!
Decent book and good for starting conversations in class.
The book gives a great overview of python and it's various uses. This gets you ready for the further learning needed to further yourself with your.python learning and skills.
If you are looking for a book that will save your time as well as money, then this is the book for you. It will give you a good overview of the common structures and uses. You will find the quick chapters, followed by the practice examples, to be of great help. The chapters build upon each other, and the examples really reinforce the topic and help you further grasp the concept.
At first, i would like to specially thanks this book author for written about this valuable topic.I am very grateful to have this book. This book some tips and ideas are really very helped me.I learn so many things from this book.I am really happy to receive this book. I suggested to all for read this book.
The book is about Learn Python.This book presents the knowledge and technologies that will allow you to participate in this new era of information.The book contains detailed examples of how to perform the different tasks in Python.I would recommend this book for people who are interested about programming.
Really fantastic book for learning Python. In this book I find many valuable and helpful information. Each content is very well explained by the author. I am really happy to purchase this book. Highly recommended to all!!
I like it. Very good read, this is most of the basics and general knowledge about cyber security with examples of why it is important. Not complicated or methods, etc. If you're looking for techniques, this is good way
I found this to be pedagogically brilliant and walked away with a much stronger understanding of fundamental concepts necessary to understand data science and machine learning.
Great guide for data science. Representations in this book is just awesome. Author did a great job! Look froward to read more book's from this author.
Well written and thoughtful. Exercises are not easy but helpful if you're willing to do them. I would definitely recommend it.
I think everyone needs to read this book.I'm impressed after reading this book. A very good book at low prices. Special thanks to the author.
This is an excellent book for anyone interested in data science or machine learning. It has very clear explanations, and examples.
This book gave an astounding examination to me as I headed into a Data Science business following quite a while of functioning as a Software Engineer. I didn't require an investigation of the genuine math behind each model sort, so it was flawless finding a book that would help me with recalling the techniques and use cases without requiring a couple of pages of evaluating the exact underpinnings. I didn't require an audit of the avowed math behind each model sort, so it was immaculate finding a book that would assist me with reviewing the frameworks and use cases without requiring a few pages of assessing the genuine underpinnings.
When I first read this book I honestly thought it was a crock and a waste of my time and money. I started with an expectation of what I thought the book was about and the style of writing that I should expect. When I initially disagreed with one of the authors viewpoint with in the book and scrutinized another's personal life through research I was almost stop reading, but I decided to continue reading anyway. I made a wise decision to.
Ooooh, what a phenomenal book is it!!! I worship this book to such an extent. I would suggest for this book and exceptionally by ethicalness of writer.
This is an awesome book. Lots of information in this book. This book is really helpful.
This book is a must for every data scientist.
Extraordinary boook information is very nice and neatly written information is presented extraordinarily
The book is well-written and covers a wide range of topics related to data science and Data Science from Scratch with Python. Things I like about it:- The range of topics covered is wide, and includes (i) an intro/refresher for Python, (ii) statistics/probability, (iii) several ML techniques, (iv) data manipulation- For each topic, the author builds up the code in simple steps, so it's easy to follow along- Everything is explained very clearly; there is enough precision, without difficult formal language!Thanks for this job
This book gives an exceptionally essential clarification !I found this book full of tips and advice that really Impressive .After reading this book I am so impressive. I would recommend this book. Specially thanks to the author.
Getting started with data science, I went through page by page taking notes and practicing the techniques. Now I use it as a quick reference guide if I need to brush up on syntax. Highly recommended.
This book presents some useful theory and examples of customer survey data analysis but is entirely too academic - I agree with another review that it looks like a collection of academic papers and its very difficult to distill practical applications
I would be surprised if the book found an audience among practitioners. To me, it looks like a collection of academic papers, which, when they apply the various (mostly quite established) statistical methods to a standard benchmark dataset - typically, referring to R packages and other statistical software products that can be used for the task - do not seem to yield much novel insight about the data, leaving one with the question "So what did we gain by using these techniques?" I would put "Modern analysis of customer surveys" in the category of books whose primary beneficiaries are their authors.
There is hardly any R code in the entire book!
If I could give zero stars I would. This book is written for Python 2, which was released in 2000 and is no longer supported. In 2020, a "complete beginners" book should be based on Python 3 (released in 2008 and currently on v 3.8). I returned this book.
I've done my best to tackle the exercises throughout it and it has definitely given some comfort. This is an incredible prologue to Data Science. Ethan Williams disclosed how to control information, clean information and more with python and various libraries. .The content of this book is very much heart touching and knowledgeable .Author Writes this book very well.
This book was really prepared to help me with learning Python the simple way.I especially got a kick out of the arrangement used to express what is at the bleeding edge of your thoughts.I delighted in this book.this book is informative.I am extremely happy to buy and peruse this book.I would emphatically prescribed to just for this extraordinary book.
This book is well written and easy to follow. It's saved me from spending hours searching the internet to get acquainted with the standard libraries.I have used it extensively for the intro to ML at Berkeley and for now the book belongs to my short list of desk reference books.
Very informative and well written book it is. I had not much experience with python prior to reading the book however I was able to pick it up quickly. The author did a great job in this book. I really like this book.
It was great. Buy this book if you want to learn how to work through Data Science Projects in Python. It was great. Buy this book if you want to learn how to work through Data Science Projects in Python.
I strongly recommend this book if you want to learn data science and machine learning on a practical level applying code and assessing the outcome
Inside this book,you will learn how to use Python to set up your data science toolbox.
Excellent resource for an overview of methods and questions
A very current book that offers excellent research perspectives.
Used for work to expand my SQL knowledge.  The book uses MySQL as its practice, but provides concepts of what can be done in MS or Oracle as well.  It sticks with concepts and includes practice.  Learners like myself need both.
Well done at introducing SQL concepts via MySQL.
After spending a number of years working in Matlab, my new boss asked me to build some code in Python requesting I finish it in two-three weeks. Thankfully, this book was necessary and sufficient to transfer my Matlab knowledge to Python given that time constraint. Other resources included scientifically-irrelevant material or were too focused on specific techniques (whole books just on numerical integration) to hit the ball outa the park. This book, in contrast, delivered forthwith the "neuroscientist's toolkit" in a comprehensive, yet convenient, way. I imagine if I was coming from Python and needed Matlab my results would have been similar. A lot of people are glued to one language (typically the one that they learned first or that is trending), but the reality is, just like human languages, the more you know the more people/networks/institutions you can interface with. Python is a beautiful language with a lot of elegance. Matlab is still the authoritative system for mathematical computing and is designed for math. Get the book.
not super well-written plus it's in python 2 which is outdated if you're reading / considering this in 2020 or later
Excellent book! Highly recommended.I have my background in neuroscience, MatLab and statistics, and still found this book very informative and intriguing. The authors of this book clearly demonstrated how you should think, code, and analyze data. Worthwhile reading.Good start if someone wants to learn neuroscience-oriented data science. Wonderful comparisonâ€‹ between MatLab and Python.
I was home alone one night. Everything was going well. I put together a puzzle, ate some scrumptious pizza and watched movies the entire night long. Then came bed time. So I decided to break open the newly purchased Neural Data Science book that I bought on Amazon (It arrived quickly I may add). I read a few words on the first page and it cured my insomnia. Until about 3am. There was a loud crash and I woke startled with fright. I got up slowly and quietly, grabbing the first thing I could think of to use as a self defense object. It was the book. Perfect! I crept up the stairs and peeked my head up over the bottom of the railing so whoever was there could not see me at first. I had the book out in front of me and ready to swing if need be. I hit the top of the stairs, staying close to the wall, the snake on the front of the book looking ahead for me as well. It was another set of eyes for my safety. I find the source and I release the a bit. I look ahead, dumbfounded, as I see my two cats looking innocent on the countertop while a glass lie broken on the floor. I felt relived and hugged the book. Best purchase I will ever make. It helped me sleep and made it so I felt protected in times of a possible life-threatening situation. 5 star rating.
I am a computational neuroscientist who does signal processing and this book is my go to both as a personal reference and for helping undergraduates, graduate students, and other postdocs understand neural problems. The book also has an outstanding wit and explains code and data analysis steps in depth. The only downside to this book is that there wasn't more of it, I would have loved a computer vision section for analyzing time series data. This book has saved me more time and headaches that almost any other book I own. If you even touch on the problems in this book, it will make your life easier.
I am a retired university professor of computer science, and an early practitioner of artificial intelligence and machine learning.  I am always looking for high quality and practical educational material.  Dr. Mark Fenner's "Machine learning with Python for Everyone" is high quality, practical, and educational.The author's preface describes the intended audience.  "I've written 'Machine Learning with Python for Everyone' for the absolute beginner to machine learning.  Even more so, you may well have little college-level mathematics in your toolbox and I'm not going to try to change that.  While many machine learning books are very heavy on mathematical concepts and equations, I've done my best to minimize the amount of mathematical luggage you'll have to carry.  I do expect, given the book's title, that you'll have some basis proficiency in Python.  If you can read Python, you'll be able to get a lot more out of our discussions.  While many books on machine learning rely on mathematics, I'm relying on stories, pictures, and Python code to communicate with you."Please read the Table of Contents and introductory material, accessible though the 'Look Inside' feature on the book's Amazon page.Throughout the book, the material is clearly presented and requires no previous introduction to machine learning.  That said, while there are no mathematical proofs, and there is some mathematical notation, the reader will need to be comfortable with charts, graphs, and mathematical concepts and terminology.Dr. Fenner covers machine learning from the perspective of developing predictive machine learning models.  He discusses many techniques for developing classification and regression models using well chosen examples and explanations.  His writing style is informal and enjoyable.While this is not a workbook with many listings of ready-to-run machine learning projects, it does include many short examples, written in well-formed Python 3, illustrating concepts and techniques.I highly recommend this book.
The title led me to believe that it would be a complete tutorial including how to set up an environment to use the examples in the book. Unless I'm missing something the book depends on the reader being familiar with juypter and has the time and energy to enter a fairly large python module by hand from the appendix. The text and examples seem interesting but I didn't have the time to enter the required python module. And I'd prefer running the examples instead of taking the author's word that they work as described. I was able to return the book but I'm had high hopes that "Everyone" included me.
I really appreciated Mark's approach to the ever present stack/tool chain issue that stands as a stumbling block to people getting started.  The information is relevant, up to date and approaches many of the same data science/ML hello world type scenarios with a very deep functional perspective.  To go further, I had a question about one of the external libraries... reached out to Mark, and was immediately provided a response.  I highly recommend this text.Sean O'Brien