Read Online Data Science for Business What You Need to Know about Data Mining and DataAnalytic Thinking Foster Provost Tom Fawcett 8601400897911 Books
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Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces 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. This guide also helps you understand the many data-mining techniques in use today.
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.
- Understand how data science fits in your organization—and how you can use it for competitive advantage
- Treat data as a business asset that requires careful investment if you’re to gain real value
- Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
- Learn general concepts for actually extracting knowledge from data
- Apply data science principles when interviewing data science job candidates
Read Online Data Science for Business What You Need to Know about Data Mining and DataAnalytic Thinking Foster Provost Tom Fawcett 8601400897911 Books
"I recently made a career transition from academia as a Professor of IT Management to industry as Senior Data Scientist at one of the Big Four consulting firms. The research skills that I use for academic research are the same ones that I use to help my firm discover data-driven insights that are actionable and transformative for restructuring strategies and operations to drive business value. I thought this would be an easy argument to make during the interview process, but in the end I had to develop a less academic-sounding argument for the interviews. Provost and Fawcetts’ Data Science for Business was ideally suited for this purpose.
Provost and Fawcett is THE text if you want to learn advanced statistical methods in business-related problem-solving contexts separate from any specific programming language like R. It’s also the right choice if you want to understand data science from a strategic perspective and its process characteristics. Provost and Fawcett is extremely useful for anyone who is trying to get up to speed and demonstrate knowledge in business analytics or data science in relatively short manner. This text is extremely well written—the authors use non-technical language for the most part—and it’s interesting!"
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Data Science for Business What You Need to Know about Data Mining and DataAnalytic Thinking Foster Provost Tom Fawcett 8601400897911 Books Reviews :
Data Science for Business What You Need to Know about Data Mining and DataAnalytic Thinking Foster Provost Tom Fawcett 8601400897911 Books Reviews
- Data Science for Business is an ideal book for introducing someone to Data Science. The authors have tried to break down their knowledge into simple explanations. I am skeptical of non-technical Data Science books, but this one works well.
In the beginning we are shown the motivations for Data Science and what fields they apply to. Some examples include movie recommendations, credit card charges, telecom churn rate, and automated analysis of stock market news. The book avoids going into the highly technical parts of creating the system but gives you links for where to go.
They do not really reveal the whole Data Science stack. For example Hadoop was mentioned as an implementation of MapReduce but they said going into Hadoop configuration would be too detailed for this type of book. I tended to agree, and even being a progammer myself, I thought they made the right choice to leave that out.
Where the book shines is in the explanations. I am very familiar with expected value calculations and there was a chapter on this. It was a much better high level discussion than I have seen elsewhere, and they mentioned possible pitfalls of the expected value framework.
I liked that the emphasis was on deciding what problem to solve in Data Science. The title of the book is appropriate as it is not just about analyzing data, but figuring out the business case. If you are new to Data Science or looking to get a high level overview this book is an great place to start. - I recently made a career transition from academia as a Professor of IT Management to industry as Senior Data Scientist at one of the Big Four consulting firms. The research skills that I use for academic research are the same ones that I use to help my firm discover data-driven insights that are actionable and transformative for restructuring strategies and operations to drive business value. I thought this would be an easy argument to make during the interview process, but in the end I had to develop a less academic-sounding argument for the interviews. Provost and Fawcetts’ Data Science for Business was ideally suited for this purpose.
Provost and Fawcett is THE text if you want to learn advanced statistical methods in business-related problem-solving contexts separate from any specific programming language like R. It’s also the right choice if you want to understand data science from a strategic perspective and its process characteristics. Provost and Fawcett is extremely useful for anyone who is trying to get up to speed and demonstrate knowledge in business analytics or data science in relatively short manner. This text is extremely well written—the authors use non-technical language for the most part—and it’s interesting! - This isn't really a book about the business applications of data science. Instead it has some businessy sounding bits and the start and end which feel like an afterthought. The middle seems like it was taken from a data mining textbook (or perhaps previously was one). Particularly strangely, it presents some math for machine learning but in a dumbed down way using notation the author invented (the strangest was a replacement for sigma as sum notation).
Rather than reading this you're probably better off reading a book about how business might be impacted by machine learning and related things (The Second Machine Age or Average is Over). Alternatively, if you want to know more about data science / data mining (now fairly deprecated term this book uses) or machine learning you'd be better off picking up Hastie's or Mitchell's book or taking Andrew Ng's course on Coursera. - 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. - Provost and Fawcett do a good job of explaining how a particular technique can be used, but the book has some serious flaws. The book's main defect is that it's disorganized. While explaining a concept, the authors will add additional information with the qualifier- 'we'll discuss this more in Chapter XX,.' making it difficult to grasp the nuance. I also didn't feel that there were enough examples and what examples they did have was explained quickly and poorly. Some basic statistical concepts are explained in excruciating detail, which more complex concepts and mathematical formulas specific to data science are glossed over. Data Science for Business cannot stand alone, but could be useful to a reader studying another text who would like supplementary content.
