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Useful reference for updating my thoughts on Data Analysis and analytics. I personally liked the examples through the book, but would recommend it converted into an audio book as the detail in the book was compiled in an almost conversational sense, or as a lecture series (in my mind). I can see this aimed at an organisation that is little and growing, or old school and could see the benefits of tapping into readily available externally available information.
Understand data and interpreting it is necessary to your business and this book explain that in an simple to understand manner, but I would like to see more detailed examples on how to collect the info and what tools to use if you wish to obtain the most of them. Anyway is a goodintroduction to the subject.
A short book but could summarize what we need to know about Data Science, Huge Data and especially Data is book does'nt teach us the detail things but give us a bird view of these topics. To a further need, the readers have to read other books but, at the beginning all people only need to read this ebook to grasp the main concepts at first.A useful book for me on the method of starting learning Data Analytics.
This short introduction is well written and provides the uninitiated an beginning foundation to build oupon. It provides definitions for the common terms and provides the reader with understandable examples of a relatively easy nature. while easy they are informative and present the possibilities of Huge Data and Data analytics.
Amazing book with a very clever our Hi-tech developing world, you need every time to analyse the data around you. If not, you will loose your money, your time. As a general rule, the most successful main in life is the man who has the best information - and this book can support you to be this man.
Not sure the title is accurate, this is a very introductory level view of the subject. It seems to be written in the style of someone speaking casually. I think a amazing proof reader could really benefit this book. The author seems very well intentioned however the content just falls a bit short. A harsh edit and some concrete info instead of contrived examples would go a long method in making this a more satisfying read.
This is a very comprehensive book which reveals the importance of Data Analytics in business: large volumes of info which is processed and analyzed with the goal of predicting patterns and improving the managerial decision-making process. After reading this book, you will definitely have a complete understanding of the concept.
This book has introduced a wide range of ideas and concepts used for deriving useful info from a set of data. And also it contains data analytics techniques and what can be achieved by using them. It includes huge data analysis, advantage, considerations of pros and cons, methods, and more. The importance of huge data is also showed in this book as well as the software and everything required to improve business data.
It's a broad high level overview of how data analytics can support businesses increase their productivity and gives guidance on the correct policies and tactics to adopt towards that end.
Data analysis is at least as much art as it is science. This book is focused on the info of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks. I concise introduction and instructions about all stages of data analysis. Each subject can be expanded into a much more deep communication but the suggestions mentioned are very practical. I think it's a amazing starting point if you're a new-comer to data analysis. And it would be helpful to frequently look it up when you're doing the process to create sure you're on the right track.
I purchased this book because I wanted to use the data to better shop my business. I never realized that there was so a lot of tools available . I defiantly got the info I was searching for and a whole lot more.
Decent introduction to huge data, however very disappointed that this book does not work on my kindle! The words are see through because the file is automatically formatted to brown which can’t be changed on the kindle itself! Very upset, a waste of $15...
A amazing overview and fast read for those short on timeProvides definition for key technologies to support you participate in huge data conversations.
Learn to understand data scienceThis amazing tutorial will equip the reader with a sound understanding of what huge data is, analytics, technologies and tools used in huge data, as well as the practical applications of huge data.
Data analytics is something that all businesses need but not most businesses have. My parents taught me this lingo as they own a successful coffee shop. They also said to educate yourself to create intelligent decisions and to avoid ignorance. With that being said I bought this book to solely educate myself in data analytics as I endeavor to continue my parents business. I found this book to be enlightening and jam-packed with information. My favorite subject covered in this book is data management. Amazing read so far and I will tell my parents about it
This is really an awesome resource that defines data analysis and tools and methods that create it successful. I really need to develop my data analysis so that I will not be left behind in the ground. This book is very informative and a very useful tutorial for beginners to easily understand data analytics. A very spoon fed knowledge to the readers wanting to understand and learn data analytics. Time is spent wisely with this book. My deepest gratitude to this book for sharing these ideas.
Amazing light brush type to what data analytics is and how it can support a business. l was really looking for something a small deeper with more detail. I like the method the author covered the terminology and the processes. This would be a amazing book for say an executive level person who has to interact with the data analytical function but is not involved in the day to day operations.
One of the few books on data analytics that I've read cover to cover. In looking over my kindle reader highlights...counted over 50 highlights over the entire book. Have referred back to those highlights when reading from other resources on data analytics.
Absolutely worthless. Not a book but a triple-spaced size 18 font glossary. You could read this entire book in less than 15 minutes, some kind of scam.
Very primary level information, an introduction to the topics. I bought the paperback ver and the formatting is poorly done. Seems slapped together for a paper release.
For me very useful in book chapter where author explain : social media tactics for the business owner. If you have own business please read this book you can search a lot of tips and tactics for increase your business. Its really special book with special info if you begin own business and YOU test develop YOUR business.
First time Amazon teaches me not to believe the reviews. This book has less content than even a web post. Just a few bullets with few comments. You're reading this book like reading someone's tweets. Returning it back, now...
This a amazing book if your just looking for primary and formative knowledge about Data Analytics. This book is simple to read and process filled with a lot of true life examples about how each subject is applied in business.
It is just an introduction to data analytics. If you already focus on the news about data analytics, you found nothing more than you already knew.
Author is right in that he doesn't tell you that you're going to be a data scientist over night - as data mining is a long and challenging discipline to master – but at the same time I think this book is definitely a amazing lead-in to the topic for guys and girls just starting
Data Science is bloody complicated. I've spent 3 weeks of my holiday break on Quora and reading my textbook for next term trying to piece it all together. This book won't victory awards for advanced theories but is a amazing easy-to-follow intro to the topic for beginners and people like me.
I bought this book cos it was cheap. After reading it I'd say its amazing value for money. Really simple to follow and well written.
This book does not teach you how to use huge data analytics. It attempts to teach you ABOUT huge data analytics and does a very not good job at even reads like the author read some Wikipedia pages about huge data and place them into his own words (without any true knowledge on the subject). He then realized his word count was half of what his editor wanted so he added pointless sentences (eg: "Depending on your business, its size and the product and services provided, primary statistical info will have more or less significance." My apologies for wasting your time having to read that) and countless reiterations ("In chapter X I told you about[...]" over and over... yea, I read that chapter 20 mins earlier and you've repeated that same info 8 times since then already). If edited properly this already short book would have been a quarter the length.
It is a decent book. Amazing clarified models that you can use with other programming dialects. Valuable to anybody. Needed only a small persistence on the off possibility that you are not used to utilizing exceed expectations. I am utilizing it to present info examination to my significant other. Yet, I am found out a amazing deal from it.
An abuse of cash and time. Incapably made. Three clear syntactic mistakes on the plain first page of the Introduction.. Nothing valuable picked up from perusing this short book. It has all the earmarks of being an independently published blog passage.
There are so a lot of sophisticated tools and techniques to handle growing volume of data that generates from social e book inspired me more to discover the knowledge of Machine Learning and Data Mining and I'm eager to obtain a hands on experience as a data engineer.
This is an excellent, very precise and detailed book. The book is well-structured, gives me the full knowledge of huge data and the technique on how to analyze the data which is very useful for my job.
This book content is very simple to understand and very informative. A lot of info in this book. After reading this book and learn a lot of things. I really enjoyed read this book. Thanks author!
This book will enable you to learn and see more about Data Analytics. I have a decent perception of the subject in the wake of perusing this book and would prescribe it to everybody who is amazing to go.
A waste of cash and time. Poorly written. Three obvious typos on the very first page of the Introduction. Nothing useful gained from reading this short book. It appears to be a self-published blog entry.
Useful for people with primary engineering or technical skills as an introduction to various techniques and use cases for data science
I didn't understand how I could use this book.I didn't search here any suitable examples.I didn't search here even one formula or something like that.Just a speech on data analytics is cool. Ok, I understood. But what can I do with it?Probably, if you are a novice, you'll search something. I couldn't.
When trying to learn about a fresh field, one of the most common difficulties is to search books (and other materials) that have the right "depth". All too often one ends up with either a friendly but largely useless book that oversimplifies or a massive academic tome that, though authoritative and comprehensive, is condemned to sit gathering dust in one's shelves. "Data Science for Business" gets it just right.What I mean might become clearer if I point out what this book is *not*:- It is *not* a computer science textbook with a focus on theoretical derivations and algorithms.- It is *not* a "cookbook" that provides "step-by-step" guidance with small to no explanation of what one is doing.- It is *not* your standard "management" title on the cool tech du jour available at airport stands and meant to be read in one sitting (buzzwords, hype and overly enthusiastic statements making up for the dearth of actual content).Instead, it is close to being the excellent tutorial for the smart reader who -- regardless of whether s/he has a tech background -- has a sincere desire to learn how the tools and principles of data science can be used to extract meaningful info from large datasets. Highly recommended.
At it's core, Data science is the elimination of guess, intuition,hunch and decisions backed by Data .Data Science is ranked the Sexiest Job Of 21st Century by Harvard Business Review. Today there is a tremendous demand for everything "Data Science", Companies need "Data scientists", IT resources are refocusing themselves to be the "Data scientists". Contrary to famous beliefs that Marketing benefits a lot from data science, companies are finding benefits across the spectrum of their operations . Example : A leading Trucking company used Data mining skill to predict which part of the truck is going to break next instead of replacing it at specific intervals, a Leading insurer predicted those who will complete their antibiotic course based on their home ownership history. If this type of stories and scope interests you, read the book "Big Data: A Revolution That Will Transform How We Live, Work, and Think".I am an aspiring "Data Scientist" and so this review will have a slight tilt from a "Data Scientist" perspective over the business user.WHAT THIS BOOK IS ?This book is very well written ,but not for the faint heart. It is a text book and authors have taken lot of care so general audience can also benefit from it, and also not to dilute it's textbook value. To obtain the full benefit of the book, read about 50 pages ( Do not flip pages), never more than 10 -15 pages per session. The book is intense so you will need to take a break in between or will lose the thread. Once you are finished with fifteen pages, go to the first page and read , highlight the necessary locations and then go to the next page. So plan to read this book in a span of 2 -3 months. I know it is slow but if you wish to understand the inner workings of "Data science", there is not much other option. Alternative is to flip across several superficial articles that is a staple diet of every blog and magazines.WHAT THIS BOOK WILL DELIVER ?When you are finished with the book, you should have a fairly amazing understanding of data science, For example, what type of analysis that needs to be done to identify A. Will the Customer switch loyalty ? ( Yes / No ) B. What type of customers will cancel my subscription ? ( Ex : Middle Aged male from Manhattan will be 5% more likely to switch) C. What are the methodologies to identify If I can up-sell a customer ( Ex : Someone who bought this book also bought ) D. What is a supervised Segmentation and When will you use it ? ( When the target is clear, if the person will default on his loan) E. What is the significance of entropy in Data Science ? F. Exposure to several formula's ( sleep triggers as I call it). A lot of of the tools have in-built formula's but you still need some idea what these formula's are. G. Don't obtain defensive, be comfortable when your colleague sprinkles words like like Classification ,regression, Similarity Matching, Clustering, Modelling, Entropy etc.WHAT ELSE YOU WILL NEED ?Data Science does not exist in silo. It helps in decision making . So should be your learning, Here are my suggestions:1. First and foremost, you need to spend consistent time. If you are running short of time, don't even bother to start2. For those who are interested in understanding Data science, courseera dot org conducts a free 8 weeks course on "Introduction To Data Science" by an eminent Stanford Professor. It needs time and Commitment3. You can obtain true life examples to work on in coursesolve dot org ( ex: Analyze the sleep cycle)4. As a Data Scientist, you will need to understand "Big Data" . Browse an article and even experts use Data Science and Huge Data interchangeably. Hadoop is the core of Huge Data,but it is a globe of it's own.5. Read and begin experimenting with Hadoop , PIG , HIVE, HBASE and the variations it offers. I did a basics training at edureka dot in , an Indian firm, not a amazing training but enough for you to understand and then go on your own. But if time and cash permits, go to cloudera www service and sign up for training. you will not go wrong6. I signed up for Amazon elastic map reduce which has a higher level abstraction (for developers it is the difference between using sqlplus versus TOAD). It is not free but very cheap.7. Test to be the "umbilical cord that looks for a stomach to plug ", look for a mentor, look for opportunity in your firm or elsewhere to grow your Data scientist r those looking for inspiration , google for Rayid Ghani, Chief Data Scientist at Obama 2012 Campaign.
The institution tactic and goals need to be reflected in the procedures used to analyse the data base of the institution and the determination as to what data is relevant. The book discusses ways to get the data required and the short term volatility in return to the company that can result. But the authors present that this can eventually lead to improved efficiency focus and profitability for the company. The book requires a background in a number of supportive academics for full understanding . The discipline has defined its own language much like most of the technolgical disciplines and is best appreciated by those familiar with the vocabulary. It is a book that warrants study not just as a fast read for introduction. For a person studying or practicing in this zone I highly recommend this book for both its interest and as a reference book. Foster Provost and Tom Fawcett have created a valuable contribution to the understanding of Data Science.
Perfect discussion of data science methods without excessive focus on mathematical elements. These are included at a level that can be understood for the skilled marketer who has background but does not want to go deep into the math. The coverage is broad with both supervised and unsupervised methods in data mining. Subjects cover tree models to logistic regression, to scoring. A discussion of holdout model tests, prediction & validation. Particular emphasis is placed on how to frame questions to apply to the business case so suitable conclusions can tutorial business decisions and strategy. You will obtain the sense that the authors are war tested veterans of the data mining business and have applied their creativity to a broad range of business, data and technical y two caveats to this book. First, as purchaser of the kindle edition, I found the equations included in the text were sometimes very readable and sometimes the type was so little as not to be legible at all. Be warned. If you intend to follow the math that is included, perhaps the paper edition would be best. Second, this book does not dwell on the statistical packages that can be used to help data mining efforts. If you are interested in exploring these methods in practice, you will need to look further.
Foster Provost and Tom Fawcett are known for their work on fraud detection, among others. I have recently read their latest book, Data Science for Business – What you need to know about data mining and data-analytic thinking. No suspense: it’s one of the best data mining book I have ever read. Its style allows the book to be read by beginners, but its wide coverage and detailed case studies makes it a reference for experts as the title suggest, the book has a true focus on business with plenty of industry examples and challenges. The style is very pleasant since authors have created efforts to place the reader in specific situations to better understand a problem. To be noted the very interesting discussion of data mining leaks as well as data mining automation. The book is divided by concepts and provides a focus on them (instead of techniques). Although no exercice is present, the book could easily be used as a resource for a course.Each chapter is clearly divided into primary and advanced topics. The evaluation phase of the data mining standard process is deeply discussed. The section about Bayes rule is very well written. Data Science for Business is also an perfect resource to avoid data mining pitfalls. Chapter 13 is a must-read in order to understand success factor for implementing data mining in a company. To conclude, targeted at both beginners and experts, Data Science for Business is the fresh reference for data mining specialists working in industry.
This book is fantastic. it's a excellent mix of high-level explanation and technical details. There doesn't seem to be much to support one actually execute the methods described, but that does not appear to be the author's intent (which is why there is no negative impact on my rating).I appreciated the accessibility and plain English - albeit thorough - writing (from the perspective of a person who is self-taught in data science and sometimes less acquainted with the terminology).
An perfect resource for the Business Analyst (or the curious executive) looking for a comprehensive understanding of data analytics for business, especially the newer zone of heavy data/big data for business. This book gives a really solid grounding in both the business (strategic) and data (analytic, technical) aspects of modern data analytics. The authors clearly present that data is the next wave of change and that it will require a mindset change across all business functions--a mindset they call data-analytic thinking. If you need to master/improve this thinking skill set--here is a amazing put to begin no matter what your job vost and Fawcett have place a lot of work into the instructional design of this book--you can follow it down to the technical/mathematical level of algorithm design or just read the content concerning business tactic and general data design and use. Either way, you will achieve a satisfactory understanding that serves your purpose--the authors maintain a conceptual continuity at two or three levels of discourse. Very nicely done and very engaging. Five stars.
Note - I was provided an ebook ver in exchange for my review as part of the Library Thing Early Reviewers brief –This is a amazing book for any in the data science field or wanting to just understand “Big Data” or a manager/professional just trying to “get current. “ I have a masters degree in software engineering with a data science background and three years experience in a prior job in Data warehousing. It was a long read, especially with the holidays, but well worth it, and more enjoyable than almost every technical book I have every rengths – Organization, having technical info in a side by side section for those who wish it, covering info from definition, through use and application, as well as doing a amazing job explaining similarities and differences on key topics.Weaknesses – there are a few little locations I wanted more. Meaning if they could have somehow had more examples for the various models, situations, etc., especially as I got into more of the predictive models.
Provost and Fawcett's book is one of the very few in the field that neither condescends nor patronizes the reader as it explores the motivations and machinery behind the most commonly used data analysis techniques in the analytics professional's toolbox. While it stops short of providing detailed instruction on how to use these techniques, it provides the reader a solid foundation for taking this next hands-on step. And for those who are not working directly with data, but are otherwise stakeholders in the use of analytics to drive better organizational outcomes, this book will greatly enable you to understand and add value to the analytical process.---Zain KhandwalaExecutive Director,Institute for Advanced AnalyticsBellarmine UniversityLouisville, KY
Both authors practicing data science professionals. Their book outlines practical considerations, explains available tools and techniques, and shows results of a lot of well-chosen e book is appropriate for all data scientists, regardless of background or education. The math is minimal. There are no computer programs or algorithms.
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This is by far the best book out in shop to obtain you started with using python for data science. You will need some primary understanding of python and machine learning to understand concepts here, but this book will definitely take you skill to next is is no-nonsense book and goes deep into items which are relevant and necessary to do data science in python, every page is rich in info and provides practical use case, optimization tricks and adds fresh dimensions to your understanding of topic.
I have used R for a few years and this was my first book that covered Python for data science. Even though it does not go into super amazing depth in any area, it is definitely a super book. It covers everything from Pandas, Matplotlib, and scikit-learn. I would highly recommend it for anyone that is fresh to Python and/or data science. The book is written with Jupyter Notebooks so it is simple to follow along and test code from the book in your own notebook.
I truly delighted in this book. I had very small involvement with python preceding perusing the book anyway I had the option to lift it up rapidly. After a short time I was plotting appropriations of continuous insights and prototypes a prescient displaying smaller scale administration. I think about this as an absolute necessity have book for any hopeful info researcher.