laptak2003/AmazonDataScience huggingface dataset:
Very interesting and does have the work through guide
Originally a Computer Science person, but the theories it presents actually are useful for any major. In the sciences, we are so closed minded. A recommended read for anyone interested in expanding their conceptual knowledge in the field of social sciences.
It is a very nice book for studying data analysis
Very applicable!!
I use this to teach a senior undergraduate writing course in sociology. Aneshensel presents a comprehensive and clear model for regression (she covers OLS and Logistic, but it could be applied to models for categorical, limited or panel measures) that focuses on aligning statistical tests and interpretation with clear theoretical expectations of the function of variables with reference to a single focal relationship.Put differently, you start with the key independent and dependent variable, then build up a complete model (always based on theory) that may include controls, rival independent variables, mediators, moderators, antecedent and consequent variables. Importantly, the book provides methods and examples of how to include each of these in regression and interpret the results. The second edition is a major step up from the first, with more examples, better diagrams, and extended coverage of logistic regression, including the statistical challenges of interpreting nested models in logistic.The book could be used for an advanced undergraduate or early graduate student. However, be aware that it limits tests for elaborated relationships to standard regression equations, and does not cover such things as new methods for testing hypothesized mediation, methods for non-linear non-binary dependent variables, experimental models, or alternative causal models (e.g. instrumental variables, propensity scores, SEM, etc.).
A priori I thought that Bayes theorem was clear to me. After reading the book and following the examples my a posterior assessment about my understanding temporarily dropped.Not a good sign.The examples are good (except the visual representation that was kind of confusing). But they do not really help to understand the actual Bayes formula. So if you start to think about them you might get lost.But it is a fair attempt to explain the reverse logic of the Bayes inferencing.And since it short and free I can not complain too much about it.The overall conclusion is that it is worth to read.
This is a simple introduction to Bayes Theorm for beginners. The author uses intuitive and interesting examples as well as visual aids to assist with explanation. I believe this introduction could included more vocabulary around frequentist statistics when comparing the two approaches at the beginning and could have included more discussion of further learning in Bayesian statistics at the end (for example, Bayesian networks). Overall a well thought out explanation.
I am a big fan of compact knowledge, so I had high expectations for this book. While I do find the author's approach quite good, I am disppointed by the poor editing. Brevity requires excellence, and the number of typos, grammatical mistakes and awkward phrasings ruined it for me. Please revise!
This is actually one of the first reviews Iâ€™ve done for a book of this type. Scott has done an amazing job explaining in easy to understand language as well as great entertaining examples.He can help you to understand what is undoubtedly a most used procedure/process/method in a lunch hour or two.With todayâ€™s focus on data science and machine learning, let this book be your portal into that world of present and future.
This is a nice  summary Bayes for people who are willing to do the math.  It's not exhaustive but the method presented is useful.
nice short explanation of the concept and uses for Bates Theorem.  not a text book by no means but might peak the interest to someone who may otherwise have avoided a math class
Book makes Bayes simple  to understand with a new way to look at and compute. Wish I had read this first before reading other Bayes books.
It really isn't much. It's barely an introduction, but it was free, and it is short, and it is clear. Not bad.
A decent, if brief, introduction to Bayes Theorem.  I found the examples helpful to gain an intuitive understanding.  Sufficiently aroused my curiosity on the topic to make me want to explore further texts to dive into more detail. Pretty much all you can ask out of a 15 page (free!) introduction to a new topic.  Recommended.
Straight to the point.
Un libro muy util para definir en pocas palabras y con ejemplos puntuales el concepto de estadistica bayesiana.
The author uses a table based method which is clear and intuitive.  Good read!
Well written guide to Bayes Theorem with two great examples, just enough details to stay interested troughout as a novice. Very enjoyable quick read!
A difficult topic made simple. Thank you
pretty basic, but a fun read.
Easy to understand with simple examples.
Scott does a great job of making Bayes Theorem accessible and practical.  I have used his insights and approach (appropriately documented) in my own recent book on using simple mathematics to evaluate many Health Care issues.  Scott deserves accolades for clear, unambiguous thinking, and bringing Bayesian thinking into the realm of â€˜usefulnessâ€™ without the mathematical formalisms that more academic writers use.  Excellent, practical text that I return to again and again.
There are very few comprehensive and current books about the broadrange of Big Data Analytics which are suitable for use as universitytextbooks for the the subject.  Bahga and Madisetti's book is anexception, introducing the characteristics of Big Data projects,surveying modern analytic concepts and methods, and including avariety of illustrative and relevant case studies.  While not goinginto great depth on the use of specific software tools, the bookdoes provide enough introductory guidance for further explorationof such topics as Hadoop, NoSQL, and many of their related toolsfor data management and analysis.  Additional tool-specific referencescan then be used to augment their coverage.The authors' explanations are clear with numerous helpful diagramsand code examples using Python and other programs.  They includebrief instructions for using online Big Data services such as thoseprovided by Hortonworks, Cloudera, Amazon Web Services, and Azure.As a textbook, this reference is quite suitable for introductoryData Analytics courses, as it presents the "big picture" of thisemerging and rapidly evolving area of technology.  The instructors'website for the book does not yet include sample presentations norstudent exercises, but these are easily created from the basematerial.The authors clearly prefer Python and related tools over R foranalysis, and there is little mention of the latter in the book.However, that too is easily augmented by instructors who wish toinclude R in their courses for students with less programmingexperience.
I love this book. It is so much of a straightforward "how-to".  One of the better intro books out there.
Not user friendly.I appreciate the content in the book, but it does not really say how to connect and such basic stuff with which readers will get stuck with for years.
The book was used but it looks like new.A very good coverage of the subject. I would highly recommend this book for anyone interested in knowing more about Big Data.This is a good book in terms of "foundation".
I like the way authors have presented the analytics patterns. There are individual chapters focused on batch, real-time and interactive analytics, that provide a good overview of the big data frameworks. The case studies are very helpful. Overall the book is easy to read.
Comprehensive and organized.  Lots of examples and case studies in this relatively new area of big data analytics.
This is the companion book to the online Executive Data Science course offered by Johns Hopkins University.  It is a nice summary of the topics covered in the course.  It does, however, lack any of the graphics that were used in the discussions.
Covers material in the corresponding Coursera classes, but no incremental value-add.
This book gives a great overview on what data science is, how it works, and how to put together a data science team. The format is in short chapters, and in plain language, as you may hear from the authors' Coursera course. The book will benefit those who are curious about what data science can do for their own companies.
Simply put, not as clear as John Taylor's error analysis book. If you want a good explanation for how and why to approach error analysis, this book does not do the job.
There are lots of misleading in Ch. 3. - Specific error formulas. See the original Bevington's 1st ed., and compare the formulas. I think this misleading should be corrected. Standard deviation is ALWAYS positive!!..
You can tell he isn't an English major.  The material is fine.  A decent reference book.
Great way to get introduced to error analysis methods
Book arrived in a timely manner and the used material was just as the summary described. I found it a good buy and satisfactory to my needs.
I still have the issue I came to use extensively back in the 70's - and I still do.  This new version keeps on being useful for both the undergrad and grad student and for the researcher: it presents everything in a plain but thorough way, and it is invaluable for a quick reference  "mind reminder". I recommend it without any reservations - I bought one for my son!
This book seems to have been completely rewritten by the new author, only keeping the outline of the original, and it's for the better. The writing is as careful as the original, and as economical, so you have to master the early chapters or the rest is hopeless, as things start off slowly but quickly become difficult. It begins by considering the error in a single measurement, and proceeds to estimating errors derived from curve fitting. A few nuclear decay experiments provide examples throughout, and the author insists on calculating many quantities manually, even though in practice it would never be done that way. Some background topics like matrix algebra appear in the appendix too.
First, I would never pay $100 for this book!  I used the 1969 version for a course as an undergraduate physics major back in the 1980's and that version was an excellent intro to computational data analysis.  This book eventually lead me to a PhD in theoretical chemistry.
I first read this book almost forty years ago when I was a physics major taking my first physics lab course.  This book introduced me to such concepts as variance, Chi-squared, and least squares linear regression.  This book was sufficient for my purposes until I was a graduate student and needed a more sophisticated estimate of the standard deviation for my experiments, which led me to Kendall and Stuart:Â Kendall's Advanced Theory of Statistics, Volume 1: Distribution Theory.
As a practicing engineer, this was one of 5 or 6 books that I found most essential throughout my career.  Chapter 4 on Propagation of Errors was referred to very often in my work.
As far as a book that gears you to applying statistics to the lab it does the job adequately.  As far as explaining the theory of probability and statistics in a coherent way it leaves much to be desired.  If I were a lab instructor I would probably use the book "Introduction to Probability and Statistics" by Milton and Arnold and just lecture on propagation of errors and the likewise.
When I joined Ed Stone's space radiation lab at Caltech, in 1978, he handed out copies of this book to every student working for him.  It's still revised and reprinted today.  A very practical book for real world work.
I had the good fortune to be trained by a physicist who, to my horror, left halfway through my bioinformatics PhD.  (Imagine if Luke Skywalker were in the swamp, and Yoda told him he'd taken a job at the Broad.) Before he left, while I nearly collapsed in panic, he told me to get Bevington.I have stacks of statistics books that teach readers to plug things into formulas.  This is not the way to learn or do statistics, and in anything but the most trivial of cases will lead to incorrect results.  Bevington teaches the reader to think about what the data looks like, and specifically the error and uncertainty in the data.  This is far more valuable than knowing the formula for a chi-square test or whatever other rubbish is generally taught in statistics classes.I would recommend this book as the first book that any scientist in training, in any field, read.
This is the standard text for data analysis in physics at an advanced undergraduate or graduate level. Using the chi-square fits and F test sections of this book, I was able to fit transit models to light curves for known transiting exoplanets and reconstruct the radius ratio of the planet and the star, as well as obtain the statistical significance of that detection. This book is a good first place to look for all your physics statistics needs.
This is a book that has been widely used by three generations of students and researchers. I used the 1969 edition as an undergraduate. The 2003 paperback edition would be an equally excellent publication except for the astounding number of editing errors in formulas and a few gross errors that seem to be outright mistakes. One particularly egregious error concerning multiplication of linear matrices appears in appendix B. I bought the text as a refresher and reference. Because I had a working knowledge of the subject matter, most of the errors were immediately evident. However, someone new to the subject would find many of them confusing.In My opinion, McGraw Hill has done a terrible disservice to the authors and should be embarrassed at doing such a poor job of publishing an otherwise excellent book.
My professor loved this book, but it's very difficult and dense, and unfortunately, the examples in the text weren't related to the experiments I was performing.  I found it difficult to apply the readings to the experiments.
Bevington's book "Data Reduction and Error Analysis" is an old war-horse that is now in its third edition, and looks a good intermediate-level, practical reference to have on the shelf. I bought my copy only recently to follow up a reference, so am less familiar with it than I'd like to be before writing a review.Unfortunately, following that reference led me to equation 4.22, which is wrong.  When forming a weighted average of variances, one needs to square the weights in the numerator and square the sum of weights in the denominator. The equation as given has the first power of the weights and of the sum, just as found in the weighted mean. As I sort the matter out myself (with help from a friendly statistician) and look around at other treatments, this appears to be a common mistake, but mistake it is.Otherwise, it seems a good book clearly written.
I'm a grad student and this seems like a very useful book.... I learned about it in a meeting few weeks ago when one of my adviser's friends recommended me to buy it. Wish I had it for a course....
The revised addition is still the gold standard if you want a concise overview of standard data and error analysis techniques. A great reference.
This is a must for experimental science. Also the online avaible codes are great. I wrote a good nonlinear least squares program that can fit almost any function to data. The resuls are ready for plotting in xmgrace.I strongly recommend this old classic.-dgs
Any older but still reliable book. Another perspective on data/uncertainty analysis.
I like both the theory and the code presented in this awesome book.
A table of content in the preview would be helpful for potential purchasers...
With nearly 100 additional pages added since the 1st Edition in 2013, this update to Brett Lantz's excellent text is well worth the purchase, even if you've already got an earlier copy on your shelf. Clear writing, robust explanations, and compelling examples appear throughout, and most chapters explain the math underlying the methods in as simple and easy a manner as possible. I liked the 1st Edition so much, I used it as the primary textbook for my Applied Machine Learning class for undergraduate juniors and seniors in science and engineering.Chapter 1 provides a great overview of the main concepts associated with developing and using ML models for decision making. It includes discussions of traditional topics like overfitting and energing issues like bias and AI ethics. The chapter structure follows the same pattern as previous editions, so knn, Naive Bayes, decision trees, 4 neural networks and SVMs, association rules, k-means, and performance are all covered. Chapter 12, on specialized machine learning topics, is significantly updated from previous editions and now covers tidyverse, domain-specific data, and brief examinations of performance optimization techniques like parallelization, MapReduce,  Hadoop, and Spark.In most chapters, there are fully reproducible examples clearly broken down into steps. Within those steps, subtasks (e.g. transformation, data preparation, model specification) are also clearly specified, making it clear how to structure different types of problems. This book is excellent for beginners, and others who want to use R to learn how to skillfully address ML problems using their own data.
Book provides gentle introduction to the world of the data science with accent on the practice.Using plain English and vivid explanation style the author provide both introduction to machine learning and to R development.Each chapter presents one ML-approach and includes motivation, examples, model quality valuation, and ideas for improving  the results.Many popular and contemporary approaches  are included in the book: boosting, neuronal networks, clustering, NLP etc.In last chapters, the author present more intermediate material:  GPU computing, parallel computing.To conclude: this is a good book for partitions and for beginners . Also the book can be one of the books in theoretical course as complement to more math oriented books.
Excellent book. I have come across one of the best books so far on Machine Learning using R. The author has made sure that the book is readable for both for a new users of R programming as well as who are new to machine learning with multiple case studies. This is also a great book for people who want to refresh their memories in key techniques of machine learning. I am an experience data science professional and I would recommend this book to readers looking to understand how to build machine learning models using R. Thanks to author for writing this book.
This is a good book for those who would like start using R for applied Machine Learning. It is light on theory but full of code snippets demonstrating the power of R. Targeting beginners with little or no previous exposure to either Machine Learning or R, it concentrates on the most frequently used algorithms (k-nearest neighbors, linear regression, support vector machines, naive bayes, neural networks, k-means, decision trees and rules, ensembles) with explanation of basic principles, pros & cons of each algorithm, and of course code examples and rigorous performance evaluation and its optimization. The last chapter briefly touches more advanced topics like reading from databases, scrapping the web pages, analyzing and visualizing network data, parallel code execution with Hadoop and Spark, GPU computing and deep learning, and pre-trained model deployment.
This is a very good starting point for those who want to execute popular machine learning algorithms in the R statistical programming language. Brett Lantz covers supervised and unsupervised machine learning techniques as well as ways of improving model performance via visualization and parameter turning.I think there could be more of an explanation of bootstrap sampling for machine learning. In addition, the concept of linear separability with regard to neural networks could use more exposition as well.In spite of these issues, Brett Lantz provides enough technical details and coding examples in R to give a reader an exhibition of a machine learning model's strengths and shortcomings. Further technical details can be discovered using Lantz's in-text references within each chapter.
I am a new professor of Predictive Analytics at a university here in Michigan and was interested in a book that had a friendly introduction to predictive analytics without undue verbosity. I went through some books and finally looked at this. It checked off the major highlights of what I needed for my business students and I decided to buy and read.After reading through the vivid examples and excellent explanations of the lines of code, there was undeniable evidence of a well-thought-out and well-written book. I have absolutely no reservations in recommending this book for anyone who is interested in a guided understanding of the fundamentals of predictive analytics as a must-have skill in the modern data-driven business world.I have recommended it for my students this fall and I will be using the book as an official class recommended text in a university. I hope that the link to the color images in the book will also be fixed as teachers who are adopting the book will need this in writing slides. Thank you Brett Lantz for a great book!
I think this product is a very direct and concise resource for understanding Machine Learning in R.  The graphics help explain the topics and the code is easy to understand.
Machine Learning with R provides clear guidance on how to use expert techniques in predictive modeling. Great for self-study and to learn advanced techniques using free, open source R software. Brett Lantz starts with clear descriptions of Machine Learning and AI. Brett shows us how to obtain free R Software and tools and downloads to build all the models and case study examples. Machine Learning with R, Third edition, is  clearly written and moves through basic models and then more complex and specialized machine learning topics.
I call myself data native and have been working in Python for over 5 years now, Recently I have been working in R and learning machine learning in R and this book has provided me the most concise and detailed explanation with all the possible syntax and libraries. This course has hands-on exercises which I would recommend to do it. Overall the book is fantastic for R developers who want to develop intuition and gain knowledge in machine learning
I am a complete beginner to R as well as Machine learning, this book has clearly stated every concept and made me understand machine learning even better. I highly recommend this book
I received the book just recently. I will update my review of the contents later. Two things I noticed right away are (a) The 'dataspelunking.com' website mentioned above (in the Amazon description) and in the book, is an empty shell; and (b)  The link in the preface pointing to a page containing a PDF of color-images of the screenshots/diagrams in the book is broken, leading to a 404 message. The kindest thing one can say about this is that the book has been rushed to publication and released before the associated resources are ready. At worst this is a misrepresentation. While those color figures are not a big loss, this carelessness does not bode well for the attention to detail with which the book was produced, especially since it is in its 3rd edition.Edited: Packt repaired the broken link and contacted me here to point that out. Very nice! I am revising my rating. I will revise again for content after I finish the book!
UPDATE: I am increasing the number of stars on this review. I do not take back what I said, this book is quite basic. However, if you are looking for a tutorial with very basic concepts of machine learning, this is a good beginnerâ€™s book. You will definitely need to supplement this with something that has more detail. Itâ€™s deep learning description is not useful, but for basic machine learning this is a good place to start.This book was disappointing. I can understand trying to simplify material so a mass audience can understand it, but the author went too far here. There isnâ€™t nearly enough content to get even a basic grasp of the main machine learning tools. There are also huge holes in the content. The book is based almost solely on classification models and does not delve into numeric prediction models hardly at all, apart from linear regression. Given the price, Iâ€™m really disappointed. Itâ€™s going to stay on my shelf collecting dust.
This is an outstanding book and by far the best machine learning book of the several I have read. I highly recommend.1) The author writes this in a way that you can perform machine learning tasks in R without any prior experience using R or ML. He gets you up to speed quickly on how to download R Studio and explains everything you need to know about R in order to perform the ML tasks that are described later in the book. He doesn't explain all of the things you can do in R as in many other books, because you don't necessarily need to know everything about R to perform ML tasks and this book is about ML. So this is obviously not a book that is exhaustive of everything possible in R.2) The structure of this book is outstanding and very logical. Within each of the chapters on Machine Learning models, he describes the model and how it works, includes a section covering the model's syntax in R, the strengths and weaknesses of each model, compares the model to the prior models discussed, and then uses that foundation to walk you through an example of applying each model to real data. Within the examples, he uses the 5 steps of a modeling process: 1) collecting data, 2) exploring and prepping the data, 3) training the model, 4) evaluating model performance, and 5) Improving model performance. I love this structure!3) His explanations of everything are outstanding. I've read several ML books and he by far is the best at explaining things in a way that people can clearly understand. If the model involves statistical concepts, he explains what you need to know very well.4) The book covers many ML models: K-Nearest Neighbors, Naive Bayes, Decision Trees, Classification Rules, Regression, Regression Trees, Model Trees, Neural Networks, Support Vector Machines, Association Rules, K-Means Clustering, and Random Forests. For an advanced Data Scientist, this may not seem like an exhaustive list. However, I feel that for a beginning data scientist or someone that is just interested in getting a good overview of data modeling, I feel this is a great choice of model coverage.Bottom line- if you're at all interested in machine learning in R, this is the book to get. If you're already an advanced data scientist with years of experience, this may not be the best text for you, though.
I'd have to say that this book is a simple and clear concise introduction to both R and ML. Probably one of the best books on ML with R I've used. It starts of with a robust introduction to R and stats that'll give you the tools to play around with R. Next, the book goes into the ML with good illustrations using physical representations for easier understanding. It goes on to some vector/component concepts in SVM (obviously) but still retains the intuitive properties of the book.  The book chapters, generally, are independent and can be read as stand alone without much loss in understanding.  Overall, a great book to give you the confidence to move forward in ML.
Really like how it goes through all of the major models and is easy to read.  Learning data science and it's SO HARD to find books that are written for people who are new to the field/not well-versed in all the technical.  Loved that this one was so easy to get through!
Easy but i guess this is for programming beginners. I already learned some languages and R programming before so it doesn't help me that much.
The original book is great.  But for a user who is looking for updates 4 years since the previous edition, this book falls flat. It is almost identical to the previous edition which implies newer techniques are not incorporated (which may be ok depending on the landscape of the subject domain, but the book title was misleading)
The bookâ€™s explanations of the concepts/algorithms are very clear and easily understandable. Even if you are not into R, I think it is still a great book for simply learning basics of machine learning.
The second edition of this book was my first real, structured introduction to machine learning. As such, it provided me with a solid foundation to build upon early on as I began my career in Data Science.Now, for the third edition, I see much of what made the old edition great has been updated and improved. It is rare to find a book that is a thorough, accessible and organized as Lantz.You'd be getting essentially two books in one: a good introduction to the R programming language, which although basic is all you really need to get started. And you'd also be getting a high-quality course in Machine Learning.All concepts are motivated and clearly explained, so there are no assumptions made nor anything glossed over for the sake of brevity. Each model under study is broken down in terms of its pros/cons and use cases.Save yourself some time, frustration, and money and give this book a try. I kid you not, I have seen people pay upwards of us$10k in "data science bootcamps" to learn the same things you'd learn here.
This book serves as the less humorous more practical version of Jaynes' probability theory  - an outstanding work that is still relevant today as it was 13 years ago.Five stars without a doubt.
Great book as an introduction into Bayesian statistics. Lots of great examples. Perfect for a physicist or an astronomer...or any physical science for that matter. I have definitely seen the error of my frequentist ways.
Very nice book. Even if you don't use Mathematica (I only use it on occasion), this is a very insightful book on Bayesian inference. The future is Bayesian, this book will help guide you to that future.  Yes, there are a lot of other books on Bayesian inference on the market, but I really think this has something to offer and is complementary to many of the other books.  Being a physicist, Gregory pays attention to Jaynes' brilliant work (especially since he was introduced to it when he found the PhD dissertation by Jaynes' student, Bretthorst, in a restroom one day!), which is light years ahead of its time. If you want to be an ace data analyst/statistician, this book will give you put you in with the front runners.
Nicely written.  Clear and concise.  Chapter 12 contains a great introduction to Monte Carlo methods, the best I ever had read for novice practitioners.  Buy it!
An excellent text-book-length presentation on a fascinating subject.
I'm a physics major and I excel at math. However, I have never used Mathematica (or any math computing software) and this book relies on it heavily. Almost to the point of being unusable to someone not familiar with the software. The concepts in the book are hard enough to learn without having to struggle with computer software.
Phil Gregory has managed to condense the most important aspects of Bayesian probability and data analysis into a book that is actually rather practical. This book will give you a solid Bayesian understanding of probability, starting with first principles (Cox's desiderata), continuing to Bayes theorem, an introduction to common probability distributions, and concluding with rather advanced numerical techniques such as tempered Markov Chain Monte Carlo. The book is geared towards a reader who will use Mathematica to work through examples, but can be successfully used by others who prefer cheaper and more practical computational frameworks.  It's not a flawless book by any means---first of all, although the book purports to cover frequentist alternatives to Bayesian methods, the frequentist coverage is very shallow and inadequate to give the reader enough background to either use or really understand frequentist usage.  The chapter on maximum entropy techniques is woefully incomplete, and doesn't include general Jeffreys priors (derived from the Fisher information) or really explain the various issues associated with defining the entropy for continuous distributions.  The section on deriving priors with uncertain constraints actually doesn't give an answer to how to handle uncertain constraints!  But on the plus side this book answered a number of questions that have long puzzled me, such as why frequentists marginalize over nuisance parameters by minimizing the likelihood function with respect to them instead of integrating over them (it's a dodge that only really works for Gaussian-like distributions), and how to handle the enormous numbers of parameters that Bayesian calculations can generate through the use of Markov Chain Monte Carlos.  I wish the book had been longer or more detailed, but if you want to learn Bayesian analysis and don't care much about understanding frequentist statistics, this is an excellent place to start.
A very poor introduction to Bayesian logic. I am a PhD student in physics & astronomy with a substantial background in mathematics and programming. The book is written in a manner that assumes a rather inordinate prior knowledge of statistics and inference. It also does a poor job of justifying Bayesian as a framework, relying mostly on vague platitudes towards older books that the author has clearly enjoyed. Far better options out there.
Bought this book awhile back and when I finally got to chapter 63, only to find out that the book is missing 5 chapters. Disappointed.
Have not read the book as I received it just few minutes ago. however, it looks like it got damaged from closing the package.. just like some other have reported.
Iâ€™ll call ya
Book was delivered damaged. It didn't even feel like I was getting a new book.
I've been using Excel for years and years, but have never bothered studying it. Now I'm going through this book, and I'm having so much fun. Some of it should be attributed, of course, to Excel. But some credit goes to this book as well. There are some minor problems, mostly concerning the online samples, but seriously - it's a great experience.
Basic must buy
I have used Excel for well over 35 years, just after I got rid of Lotus in the mid 80s. I have written books on writing business plans, I have prepared hundreds if not thousands of financial models, I have done over 35 start ups as investor or CEO. Thus I know Excel from the business perspective. However every once in the while I want to use some function I have not used and need a quick reference. I thought this would be one on Kindle.However this is a highly disorganized random set of simplistic examples and in the Kindle format near impossible to find anything. I guess it is classic Microsoft.The prime example is if you forgot a "what if" model better just go to you tube and fish around. Don't try this. The examples are all too simplistic and the overall approach leaves one wondering how best to do a financial model. It is not with this book.
Was exactly what I was looking for, good explanation of functions, cool exercises and a lot of practice so you can actually learn and teach.
I needed it for 1 class but I decided to buy it because excel is a very useful tool that everyone should learn. Ups your chances of getting a job.  The book explains things very well although there were a few explanations in the downloadable that were wrong or did not use the guidelines specified in the question
Not sure whether the author wanted to write a book about Excel 2019 or about Data Analysis and Business Modeling. Mixing both topics will create discontent on either side. The Excel 2019 features are rather well explained; the other content is disappointing and superficial.
I tracked well with the book until we got to the "Problems" section. First, I had never heard of the Fibonacci Sequence. Shame on me. I felt this was a concept that only a special group of people would understand. Second, the author should have supplied the data for the problems. For instance, in Chapter 1, Problem 2, I had to search for a height table to convert 5'6" to 66 inches regrettably this is not something that the average person would know. I could be wrong..The book does not supply all the data, information, or even spreadsheet examples needed. I think it was worth purchasing, but I must admit being a wee bit disappointed.
This series of books are great on this subject.  Easy to understand useful
Great for all those who want to "learn by doing". Some very basic stuff but as well some complex and advanced problem-solving.
Its hard to navigate through the pages on my mac, otherwise no issues
Very easy to reference.
Great intro to Excel PowerQuery!
Many (or even most) people think about two things when they hear the term 'Data Science': machine learning and predictive analytics. If you're one of those people, I urge you to get your hands on this book and embrace yourself for an exciting journey into the 'real' world of data science. What Gerhard Svolba pulled off in this book is amazing: write a book that is based on a large collection of relevant business cases, provide the technical depth and working examples to show how you could apply this yourself, and be very entertaining at the same time. He also managed to stay away from the standard examples you're likely to find in any introductory data science course or book. So whether you're trying to figure out which employees are likely to quit using survival analysis, what the likelihood is your controller is manipulating the numbers, or want to know what your chances are at winning a game of Monopoly: this book has you covered. Yes, it's a SAS book, and for all the examples and code SAS has been used. The good news: most of the examples will work in SAS's free University Edition, and even if you don't want to use (or learn) SAS there's still a lot of value in this book given the large variety of applied techniques. The code examples are also pretty easy to follow, even for a non SAS programmer. Very highly recommended!
Another great book from Gerhard Svolba that combines analytics, business applications and necessary data. The 8 examples show highly relevant applications of data science that allow businesses to identify use cases to better understand their data and their processes.The book can perfectly be used in academics to train data scientists. Students receive real life case studies with a strong business background and learn how to apply data science methods in a practical environment. The book together with the available data and full SAS programs can be run directly in SAS Studio.
You can't download any of the data files. Incredibly disappointing. In addition, while you can download the code, without the data files it's merely a cheat sheet.
Preparing to enter an advanced degree program. This book is Foundational to beginning that journey!
I was lucky to attend classroom Data Science course with Sinan Ozdemir. Sinan is very good at explain really complex mathematical concepts in a very approachable and everyday terms. This really helps people like myself who aren't in touch with theoretical mathematics. This book is written in the same tone that Sinan delivers lectures and that is very valuable in absorbing the complex concepts delivered in this book. The examples and datasets are carefully chosen to deliver specific concepts provide reference for future use. I would recommend this book to anybody with or without formal training in statistics but would like to pick up on the breadth of Data Science. I would also recommend this book to Data Science instructors as an ideal for organization and flow of training on Data Science. This book is the best Data Science book on the market that can introduce you to the world of Data Science and prepare you for interviews.
Most of the files used by the author do not exist. So, you cannot run the programs in the book.Stay away.No one seems to have proofread this book. The reviewers do not appear to have reviewed this book. If they had, this book would not have been published.
My uni required me to buy this as the Predictive Analytics subject textbook.If you want to use RapidMiner as a tool for predictive analytics then this is the book. It has all the aspects of the application covered. I wouldn't say this is a book from which to learn Data Science directly although it covers it well enough and is a good starting point. There is probably a more in-depth book for that out there that uses R or Python to do the stats etc.
Teaching is an art. This book did such an excellent job in the art of teaching the fundamentals of data science. It was easy to understand and follow throughout the book.
This book is a great read, and not just for data scientists. (I am not one myself, and thoroughly enjoyed it!) It provides insight and perspective on the data & studies that we hear and read about every day, and whether they might provide good information or lead us to bad conclusions.
This should be required reading for all the acolytes of ML who see it as an alternative, rather than supplement, to having to think and learn the fundamentals of the subject matter.
Great insights on data science. Useful guide to understand this important new sector.
The field of data science has exploded in the last 10 years.  Yes, data analysis has existed well before that, but the reality is that being a "data scientist" is pretty great right now.  So many tools, opportunities, and growth potential for businesses around the world.  I absolutely loved this book, and I am a seasoned data analytics professional.  It covers topics generally and much more specifically depending on what your intent is.  I use this book as a reference to paint a data picture of how business intelligence can help individual businesses succeed.  When I am looking for a way to explain a concept to someone who might not live, eat, breath data analysis...this book is great.The author was not out to write the definitive, historical view of data science.  She was trying to paint a broad stroked picture of the landscape right now that is big data, data science, and all things analytics.  It is an enjoyable read and I would recommend this book highly to my friends and colleagues within the field of data analytics.  In addition, I would also highly recommend those that want to start in the field review this book and come to interviews ready to talk about these resources.  It will make you a much better candidate.  Thanks again Lillian Pierson for writing such a great primer on data science for the masses.
This is a fairly high level introduction to data science which should be perfect for someone who is just starting out and wants to know the "what is what" in data science but not the gory details on "how do I accomplish ... whatever".  I do biostatistics and informatics for a living and nearly all my favorite tools are here (sadly SAS is mostly missing)  and the tools are all nicely explained.  Over and over you will see very simple code or screenshots that give a flavor for the tasks that the language/tools are used for.  The only real risk here is the reader may be left thinking that data science is easy, when in fact the devil is in the details.  If you want to get started with data science this is a great resource.  From here head to Coursera and look at the Johns Hopkins Data Science series.  It unpacks the tidy presentation that you will find in this book.
Lillian Pierson is amazing!! I bought this book knowing almost nothing about data science. But her easy to read and straight forward communication style has me coming up to speed in no time! In fact, I had "Data Science For a Dummies" sitting on my kitchen counter and my boyfriend, who is much moreversed than me in all things data related,  was so intrigued he started reading it too! We are going to have to get a second copy, because he keeps swiping mine! Excellent book!
This is an excellent introductory overview to Data Science. I have been a programmer since 1978, and one of the first languages I learned was SAS. I used SAS to analyze a multitude of problems, and also used it to convert data from one format to another (ETL type functions). I kind-of, sort-of realized I morphed into a data scientist through the years, but never had a overall view of the subject. This book supplies the framework I've been missing. It's easy for a programmer to lose the forest for the trees, and count me in, as a member of that group. This book clearly outlines the difference between data engineer and data scientist and the types of tools and activities within each domain. It addresses the why and the what, given various scenarios. It wisely stops short of giving explicit coding examples for most topics, meaning the how, in terms of coding, is frequently not present. However, to do that, in my opinion, would take at least another book. Make no mistake, there are coding examples, links to useful resources and good chapters on Python and R. But what helped me the most, was an outstanding view of the field of data science. Well written and easily accessible. I have the Kindle version, and it looks good on my laptop. Hope to see more from this author.
Great book! I made my staff read it as well and they also commented on the clarity and comprehensiveness of the book. Somehow it feels superior to any other "For Dummies" book I've ever read.PRO TIP: follow the author on Twitter. She's super knowledgeable and tweets great stuff.Overall: I recommend this book to anyone with an interest in Data Science. I also recommend it to those needing a refresher on the new components of the field that have evolved over the years.
This book showed me what I need to know and learn more about to get into the field of data science, but the book never goes into much depth with each topic. If your looking for an overview of data science, its a good start; but if your trying to learn the specific math and programming you need, this is book will only be the beginning of your studies. I will say it did give me some very good leads as to what courses I should take at my university. Then again I could have just googled that information for free.
great condition
Good overview of the field but sometimes gets too much into the weeds of what the author is an expert in (geographic systems). Would have preferred a lot more practical examples and depth and a lot less breadth ...
Great book to begin in data science with reference to what we need to learn later.
Great Book!
Easy read, great resource!
I enjoyed this book.  Anyone who deals with â€˜dataâ€™ or â€˜scienceâ€™ will benefit from reading this.  Itâ€™s a great overview of a topic that covers a broad range of disciplines.  The author puts emphasis on topics and skills that analysts, scientists, engineers, computer scientists, and data visualization experts use daily.  Iâ€™m a geoscientist with a strong GIS background.  So, Iâ€™m familiar with some of the analysis tools and languages discussed such as ArcGIS, Python, and SQL.  However, I learned about other software, languages, and data sources that I was unaware of, but could incorporate into my work.  In fact, an engineer in our company uses R, which is mentioned in the book, but is a language I was unfamiliar with prior to reading.  Bottom line, this book will expose you to whatâ€™s what in the discipline of data science.  If youâ€™re looking to gauge where you are in your field of study, a student looking to get a broad view of the discipline, or simply just wanting to learn more about tools needed for data analytics and visualizationâ€¦ this is a good starting place.  Keep in mind this is a Dummy book, so the topics are merely touched on.  Youâ€™ll have to find other sources for a more in-depth study, but at least youâ€™ll have a better idea of what youâ€™re looking for after reading this.