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Data Science for Business This broad, deep, but not-too-technical guide introduces you to the fundamental principles of data science and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. Full description
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Ayant un background d'ingénieur en informatique, la data science n'est pas un sujet évident à aborder pour moi. Foster Provost illustre avec efficacité tous les concepts utilisés en data science. Ce livre n'est pas prêt d'être dépassé n'était pas basé sur des technologies particulières (le mot "hadoop" est mentionné une seule fois).
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131 internautes sur 135 ont trouvé ce commentaire utile
The perfect balance10 août 2013
- Publié sur Amazon.com
Format: Format Kindle
When trying to learn about a new field, one of the most common difficulties is to find 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 heavy 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 little 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 perfect guide for the intelligent 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 information from huge datasets. Highly recommended.
62 internautes sur 65 ont trouvé ce commentaire utile
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 popular 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 get 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 important areas 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 want 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 good 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). Many of the tools have in-built formula's but you still need some idea what these formula's are. G. Don't get 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 start 2. 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 Commitment 3. You can get real 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 Big Data interchangeably. Hadoop is the core of Big Data,but it is a world of it's own. 5. Read and start 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 great training but enough for you to understand and then go on your own. But if time and money permits, go to cloudera website and sign up for training. you will not go wrong 6. I signed up for Amazon elastic map reduce which has a higher level abstraction (for developers it is the difference between using sqlplus vs TOAD). It is not free but very cheap. 7. Try 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 skills.
For those looking for inspiration , google for Rayid Ghani, Chief Data Scientist at Obama 2012 Campaign.
32 internautes sur 32 ont trouvé ce commentaire utile
Most practical data mining book!12 août 2013
- Publié sur Amazon.com
Format: Format Kindle
This book tells you how to **think** from the angle of data when you make decisions. I have read so many data mining books. They often claim themselves practical simply because they provide examples in addition to the technical details. Well, this statement can be seriously misleading since no one is going to solve the same problem as the one in the book. Without a good explanation on the intuition underlying the technique, it is hard to make true links with examples and eventually even harder, if not possible, to extend what you read to what you need to solve in real applications.
This book does an excellent job in this perspective. All the fundamental DM ideas (although there are so many different DM algorithms, they are all variations of only a few fundamental ideas) are explained by almost plain words illustrating human's thinking process. You will feel all the DM methods are familiar even though you have never learned them because they are presented just as a codification of rational thinking in everyday life. Once the intuition is uncovered this well, the examples in the book look so natural and you get a way to start doing your own DM tasks.
It can be your first DM book or an insightful book worth revisiting from time to time. I, as a DM educator, enjoy reading the book and learn a lot not only the insights but also how to transmit DM knowledge effectively.
I love this book!
34 internautes sur 36 ont trouvé ce commentaire utile
An Outstanding, Readable and Efficient Data Analytics Practitioner's Guide9 août 2013
- Publié sur Amazon.com
Format: Format Kindle
Excellent! Data Science for Business is an extremely well written practitioner's guide. The concepts and methodologies found inside ARE NOT EASY TO FIND in one comprehensive, comprehendible, and no-nonsense book. I received the book initially as part of my Master of Science in Business Analytics curriculum, and still, I purchased the eBook to have with me and use for my work. In fact, the principles and techniques covered in the book reshaped my understanding of big data business analytics and exactly how to do it and do it right.
41 internautes sur 45 ont trouvé ce commentaire utile
Data Mining? I do that too!25 août 2013
- Publié sur Amazon.com
Foster Provost and Tom Fawcett have set out to write the go-to reference on Big Data. 'Data Science for Business', what you need to know about Data Mining and Data-Analytic Thinking, published by O'Reilly Media.
They have produced an authoritative book that is both a pathfinder and a lighthouse. It is a long, clearly-written book that shows what can be done using Big Data, where to go and what techniques to use to get it done, and what to watch out for. Thank you for writing this book. The authors and their many references are already established and respected. The book brings the issues and their business applications together in one essential place. Already in just 1 month since release (25th July 2013) the eBook has gathered praise quotes from a dozen industry names. I am honoured to receive a complimentary review copy. So to add to the recommendations, I pitch my review slightly differently: Who in business should buy this book? What does this book add to what we are already doing in business with Data and Data Mining?
On first reading, if you work in analysis, IT, Business Intelligence, Management Reporting, Marketing or SEO, I guarantee your reaction at some point will be 'I do that too'.
For me the 'Aha!' realisation came a few pages into chapter 2. The authors discuss database searches for the most profitable items in a business. All businesses do that every day! But not always in the way the academics think. The book surprised me in covering a broader range of topics than I previously considered were Data Science. Here are some great success stories to illustrate what data science is. Buy the book to see how these things really work and how the leading companies are applying themselves to these challenges. These studies border on the commercially sensitive.
- How a supermarket can use their sales analysis to predict when people are expecting a baby, and so gain an advantage by making offers before their competitors. - How advertisers use Facebook Likes to profile and segment their audience - How Netflix make their movie recommendations - How to compare web pages for plagiarism - How to tell how far away a customer is from their mobile app Chapter 10 talks about text analysis. In contrast to most of the book, I would say here that small and medium sized businesses are ahead of Google and the academics. While the search engines refine their algorithms to extract news and meaning from bare text, there is whole industry sector manipulating the source data to fool the algorithms and keep one step ahead: it is called Search Engine Optimisation.
If you are just starting out in using Big Data for your business decisions, you need to know the importance of Maths. In particular there are 2 challenges in the mathematics that underpin Data Science that I should warn you about even if you do not read the book:
* One is causation and correlation. When you find the beer-buying customers are also the nappy-buying customers, that is just the first step towards some very careful thinking before you draw any conclusions about which is cause and which is effect and how you might adjust your marketing or product mix to assist your customers accordingly * The other is what is now called 'Overfitting'. Gaze hard enough and you will find trends in data just like you can find shapes in clouds or patterns on the back of your eyelids. If you search too hard through too much data, you invalidate correlation co-efficients and confidence calculations. Or to put it another way, every cloud looks like something.
A great book. For everyone who can still manage their high-school level maths, I recommend you buy this book. For everyone else, I recommend you be aware of the book and the issues within it and get it on the corporate bookshelf. For myself I look forward to checking back regularly for future editions as the science develops. Five stars.