counter create hit Data Science for Business: What you need to know about data mining and data-analytic thinking - Download Free eBook
Ads Banner
Hot Best Seller

Data Science for Business: What you need to know about data mining and data-analytic thinking

Availability: Ready to download

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. 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


Compare
Ads Banner

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. 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

30 review for Data Science for Business: What you need to know about data mining and data-analytic thinking

  1. 4 out of 5

    Tom Fawcett

    Since I wrote it I think it's excellent

  2. 5 out of 5

    Todd N

    This is probably the most practical book to read if you are looking for an overview of data science, either so you can be in the know when terms like k-means and ROC curves are being bandied about or so you have some context when you start digging deeper into how some of these algorithms are implemented (esp when plowing through a book like The Elements of Statistical Learning: Data Mining, Inference, and Prediction). I found it to be at just the right level because there is just enough math to e This is probably the most practical book to read if you are looking for an overview of data science, either so you can be in the know when terms like k-means and ROC curves are being bandied about or so you have some context when you start digging deeper into how some of these algorithms are implemented (esp when plowing through a book like The Elements of Statistical Learning: Data Mining, Inference, and Prediction). I found it to be at just the right level because there is just enough math to explain the fundamental concepts and make them stick in my head. This isn't a book on implementing these concepts or a bunch of algorithms. (Check out Elements of Statistical Leaning above or Data Mining: Practical Machine Learning Tools and Techniques, Second Edition for that.) This gives the book the advantage of being something you throw at an intelligent manager or interested developer, and they can both get a lot out of it. And if they are interested in the next level of learning there are plenty of pointers. Other chapters cover the business-related aspects, which frankly I'm less interested in. Though I did find the chapter on presenting results through ROC curves, lift curves, etc. pretty interesting. It would be cool if this book had some more hands on, so maybe after reading this one should download Weka and jump to Part 3 of Data Mining: Practical Machine Learning Tools or maybe go to Kaggle and browse around the current and past competitions. A few minor nits -- I felt that Baysean methods were covered too quickly, even though the book is clear that it's a pretty large topic in itself. The equation that is finally shown has a big hole in it in that is can quickly go to zero, so it would be nice to mention that sometimes terms are left out or to mention the LaPlace estimator, at least in a side bar. Also, random forests get a only passing mention. But I'm partly complaining because I think I'd benefit from their explanations of these things too. Oh, and it was cool to see Topic Models mentioned in the chapter on text because way back in the '90s I worked for a company that used a very primitive, manual version of this technique for classifying documents.

  3. 5 out of 5

    Victoriano

    When people say that data science is the way of the future, I break into a bit of a cold sweat because there’s an implication that I’m going to have to read another book filled solely with equations and proofs. It’s rare to find a book where you can get into the grit of a scientific framework without getting too bogged down by endless abstraction. However, Provost and Fawcett manage to soften the blow of overtly academic writing, while simultaneously fostering an intricate understanding and appr When people say that data science is the way of the future, I break into a bit of a cold sweat because there’s an implication that I’m going to have to read another book filled solely with equations and proofs. It’s rare to find a book where you can get into the grit of a scientific framework without getting too bogged down by endless abstraction. However, Provost and Fawcett manage to soften the blow of overtly academic writing, while simultaneously fostering an intricate understanding and appreciation of the field. Data Science for Business is all about the conceptual framework of the field as it pertains to the different aspects of entrepreneurship. The authors place a lot of emphasis on the structure of the book: there’s a very clear progression across statistical theory and its application across very detailed examples. Every technical term is immediately connected to real world applications, making this an invaluable resource for someone finding a pragmatic use for what would otherwise be esoteric concepts. However, beginners and veterans alike are advised that the content of this book is a little dense. If you’re a complete beginner, it will take a while to fully digest the content. However, for the initiated, this will make for excellent reference material. This book explores the idea that data science is more than a perfect machine: it is analytical engineering paired with innovative exploration, but it takes a lot of tweaking to get the results that you need. If you find the wave of innovation sweeping you towards the field of data science, Fawcett and Provost’s book will serve as a gentle but thorough introduction to the basics of data science. You can then use their very detailed reference list to dive further into the most relevant topics. Or at the very least you could name drop a few of the references during your next meeting to score some points with the data scientists at work.

  4. 4 out of 5

    Alan

    An an engineer I didn't like this book, it is too shallow. As a founder of a company doing data science I found some good business and management insights. I wish the authors had focused more on the business stuff. For business people I recommend the initial and final chapters.

  5. 4 out of 5

    Mbogo J

    Data Science is quite a buzz word these days, though what it really entails is unclear. It's a porous field and you need to know a thing or two about it before you get sold hot air couched as substance. When I saw a friend mark this book as to read I decided to check it out and at least have a working definition of the current state of data science. This is a good introductory book though to fully get it you need a working knowledge of basic statistics or be tenacious enough to be always consulti Data Science is quite a buzz word these days, though what it really entails is unclear. It's a porous field and you need to know a thing or two about it before you get sold hot air couched as substance. When I saw a friend mark this book as to read I decided to check it out and at least have a working definition of the current state of data science. This is a good introductory book though to fully get it you need a working knowledge of basic statistics or be tenacious enough to be always consulting Google whenever you come across a foreign concept. Readers with no statistical background, or may be bereft of time may be advised to first read Chapters 1 and the last 2 which give a good overview of data science without the jargon. Chapters 2-10 cover the core areas of data science and should be read at a pace the reader is comfortable with. This is a recommended read to anyone interested in having a working knowledge of data science.

  6. 5 out of 5

    Barth Siemens

    I set this book aside a little over a year ago, and this morning I've decided that I probably won't finish it. With my background in databases and business intelligence reporting, I thought I would really enjoy this book. I appreciated about the first half of the book. As I recall, the latter half of the book mined deeper into the slight variations of the same theories. This would undoubtedly be fascinating for the reader who wants to begin studying data science. I somehow doubt that many manage I set this book aside a little over a year ago, and this morning I've decided that I probably won't finish it. With my background in databases and business intelligence reporting, I thought I would really enjoy this book. I appreciated about the first half of the book. As I recall, the latter half of the book mined deeper into the slight variations of the same theories. This would undoubtedly be fascinating for the reader who wants to begin studying data science. I somehow doubt that many managers would get farther than half way.

  7. 5 out of 5

    Robert

    Provost and Fawcett do a fantastic job of describing the main techniques used in data mining - classification, clustering and regression - along with high level explanations of the algorithms most commonly used for each. In addition, they present an expected value framework that is very useful for choosing the right balance between true positives, false positives, etc. in the predictions of a model. Data Science for Business is by no means an easy read for even technical readers, unless you have Provost and Fawcett do a fantastic job of describing the main techniques used in data mining - classification, clustering and regression - along with high level explanations of the algorithms most commonly used for each. In addition, they present an expected value framework that is very useful for choosing the right balance between true positives, false positives, etc. in the predictions of a model. Data Science for Business is by no means an easy read for even technical readers, unless you have significant prior experience in machine learning and the relevant statistical techniques and algorithms. The book calls out the deeper technical sections that it says you can safely skip, but I feel there was a lot of critical detail in them. Nonetheless, it's still relatively light on the math, in keeping with the target audience. The first few chapters could have been much shorter and clearer if the authors had replaced a lot of words with a much smaller number of equations, but then they may have needed to retitle the book. One area the book doesn't cover in extensive detail is the visualization of model performance, though there is an adequate description of ROC graphs, fitting graphs, lift curves, with an emphasis on learning enough about them to understand them at a high level. The Coursera ML class is a better way to learn how machine learning algorithms really work, but you have to have decent programming and math skills and be willing to spend about 10-15 hours a week on it for up to 10 weeks. This book is an excellent compromise for the much shorter investment in time to read it. Data Science for Business also turned out to be a great complement to another book I've been reading, How Not to Be Wrong, by Jordan Ellenberg, which I will also will recommend once I finish it. Or maybe I'll just recommend it now.

  8. 4 out of 5

    Diana Nassar

    A thorough introduction to Data Science concepts and techniques. While this book, as the name implies, targets business professionals mainly, I personally think it is best to read it after [or while] getting your hands dirty with data science problems or taking an online course to establish a technical foundation first -- it depends on your background. I did this and I found it more relevant this way. Good choice of real-world everyday business cases to keep things in context and illustrate the p A thorough introduction to Data Science concepts and techniques. While this book, as the name implies, targets business professionals mainly, I personally think it is best to read it after [or while] getting your hands dirty with data science problems or taking an online course to establish a technical foundation first -- it depends on your background. I did this and I found it more relevant this way. Good choice of real-world everyday business cases to keep things in context and illustrate the practical value of understanding Data Science. It is also useful to understand important terms in the field of Data Science, and this book does a great job bridging the "language barrier" between tech and business; which is very crucial for people who work at that intersection. I found the sections about making data-driven decisions and using data science to drive strategy especially interesting. I like how practical, not-so-deep not-so-shallow this book is, and I recommend it!

  9. 4 out of 5

    Felipe Saldarriaga Bejarano

    A great book for whom that ask about to implement data sciences on its work or projects, the book present from the simpliers ways to the most advanceds uses of matematical aplications; all of it linked with Gerential Skills and Decission making for business

  10. 4 out of 5

    Tarek Amr

    The book is very well written, it explains the data mining tasks in good details, it also shows you how to approach your data mining problem from both its business and technical sides.

  11. 5 out of 5

    Iannes Patrus

    Great introductory book to analytics in management without being too technical but exploring the possibilities, applications and how to see when something is off in a project.

  12. 4 out of 5

    Ala

    Excellent read for managers and newbie. Focuses more on the concept than on the technical framework. Should read by any manager or team leader planning to start a data science project.

  13. 5 out of 5

    Daniel Aguilar

    Really good introduction to Data Science. Covers important principles that can be applied to many different applications in a way that gets into the technical details, but not too much. Many different profiles can take advantage of the lessons: engineers, managers, sales reps... and of course anyone interested on making data talk, both for specific needs (business-related or not) or just for the sake of discovering value in data. From project planning, data acquisition, exploration, evaluation t Really good introduction to Data Science. Covers important principles that can be applied to many different applications in a way that gets into the technical details, but not too much. Many different profiles can take advantage of the lessons: engineers, managers, sales reps... and of course anyone interested on making data talk, both for specific needs (business-related or not) or just for the sake of discovering value in data. From project planning, data acquisition, exploration, evaluation to optimisation and wrapping it up as a professional service or product. I read most of it lightly, stopping here and there where the contents resonated more with my current projects. Planning to get back to it soon for specific questions.

  14. 4 out of 5

    Jesus

    Starts really well. It is not another author trying to get a best seller under the "Big Data" hype. The book is NOT about algorithms (the information technology part they call it) but about the data science, the stages before and after "data preparation" and "modelling" in CRISP-DM.

  15. 4 out of 5

    Piefi

    Great introduction and overview to the topic.

  16. 5 out of 5

    Priyank

    In a short summary, I am so glad I came across this book! It’s not only a great introduction to the complex world of data science, it has sparked a real interest in me to dig deeper into this subject. The book will prove valuable both to readers with business or technical backgrounds, esp. if you want to understand how to harness data to add value to business problems, or how to engage with colleagues working on data mining, analytical models etc. There are a few mathematical bits to the book – h In a short summary, I am so glad I came across this book! It’s not only a great introduction to the complex world of data science, it has sparked a real interest in me to dig deeper into this subject. The book will prove valuable both to readers with business or technical backgrounds, esp. if you want to understand how to harness data to add value to business problems, or how to engage with colleagues working on data mining, analytical models etc. There are a few mathematical bits to the book – however I would recommend not to be deterred, and take time to wrap your head around those concepts as they would go a long way to add value to your understanding. Extended review: I am a business analyst by profession, so I deal with business processes, identifying business needs, and engaging with colleagues from various technical and non-technical backgrounds. Data Science underlies much of the emerging business practices for data driven decision making. When I picked up this book I had no prior knowledge of this field but I was looking to develop conceptual understanding and a real world perspective to the field of data science. This book delivers on both those aspects. I had actually started off with some online courses, but the interest quickly faded off as the content discussed were either too detailed or were too generic in introduction. However the simple narration style of the book, and good business application based case development examples made sure that I do not put down this book till I reach the last page. Now to add a pinch of salt : Even though content is outstanding, my only critique would be that the book references links and papers which are of much higher difficulty than concepts presented in the book. In case you want to read more into certain topics you might find that there is a longer bridge that still needs crossing. Overall: It’s a journey of a book, and I am glad that I took it!

  17. 5 out of 5

    Joel

    Scanned through it really quickly. The book does a good job explaining the most commonly used concepts in data science from a "business" perspective, as the title suggests. It delves sufficiently deep into the mathematics and goes through some good examples too. The only serious limit this book has is that it doesn't give you actual code examples or pseudo-code of the algorithms. So if your goal is to learn how to implement these algorithms, you'll need to go further than this book, but if your go Scanned through it really quickly. The book does a good job explaining the most commonly used concepts in data science from a "business" perspective, as the title suggests. It delves sufficiently deep into the mathematics and goes through some good examples too. The only serious limit this book has is that it doesn't give you actual code examples or pseudo-code of the algorithms. So if your goal is to learn how to implement these algorithms, you'll need to go further than this book, but if your goal is to be able to communicate with data scientists or manage a data science project as a business manager, this book will get you up to speed. Bottom line: If your goal is to be a data scientist, then I'd recommend starting with something like Data Science from Scratch: First Principles with Python and then moving to something like An Introduction to Statistical Learning: With Applications in R. If your goal is to be a business manager of a data science project, then this book is for you.

  18. 4 out of 5

    Kimberly Cheng

    Data Science for Business is a great book to give an overall view of how data analysis can be used in day-to-day business problems. The authors do a really good job of describing a construct or process, and then using examples to really flesh those out into real-life situations. It is overall a good overview of data-analytic thinking and great for those who aren't too familiar with the subject but looking to dip their toes into the field. At parts, it did feel like there was a lot of "here's a co Data Science for Business is a great book to give an overall view of how data analysis can be used in day-to-day business problems. The authors do a really good job of describing a construct or process, and then using examples to really flesh those out into real-life situations. It is overall a good overview of data-analytic thinking and great for those who aren't too familiar with the subject but looking to dip their toes into the field. At parts, it did feel like there was a lot of "here's a concept, we will talk about it later, and it relates to that which we will also talk about in another chapter" so it is not always the smoothest read, and I would recommend going back and forth between different parts of the book - re-reading certain chapters, etc. Finally, I can't give it 5 stars because I actually have a major gripe with the book. The whisky example in Chapter 6 (pg 178). In what world does a Laphroaig fall into the same group as an Aberfeldy in a cluster based on scotch characteristics?

  19. 4 out of 5

    Justin

    A thorough discussion of data science as a discipline. Books on data science can be loosely organized into two categories; technical manuals that explain data science techniques, and extended articles discussing how data science can impact the world. The two categories naturally cater to two different audiences (the "techies" and the "suits"). Dr. Provost's book is a rare exception that manages to walk in both worlds. Having a diverse audience proves to be both an advantage and a disadvantage thou A thorough discussion of data science as a discipline. Books on data science can be loosely organized into two categories; technical manuals that explain data science techniques, and extended articles discussing how data science can impact the world. The two categories naturally cater to two different audiences (the "techies" and the "suits"). Dr. Provost's book is a rare exception that manages to walk in both worlds. Having a diverse audience proves to be both an advantage and a disadvantage though. Sections that emphasize technical knowledge would be difficult to parse without some form of STEM background, while the business case studies aren't likely to make much of an impression on programmers. The result is that both parties are likely to feel isolated at various points. On balance, I would say that Data Science for Business favors technical resources by volume of content, though a dedicated manager could certainly work through the material. Recommended primarily for aspiring data scientists.

  20. 4 out of 5

    Andrii Tymchuk

    The first two chapters had given me a bad expectation from the rest of the book with all those exclamations like "we'll discuss this, and this, but very-very shortly, and without math". But it was an enjoyable journey throughout the basic Data Science milestones, or, as it's called in the book, fundamental concepts. I've had some experience with statistics only during my university degree, so I can't call myself advanced expert in the field of this book. That's the reason the level if math in the The first two chapters had given me a bad expectation from the rest of the book with all those exclamations like "we'll discuss this, and this, but very-very shortly, and without math". But it was an enjoyable journey throughout the basic Data Science milestones, or, as it's called in the book, fundamental concepts. I've had some experience with statistics only during my university degree, so I can't call myself advanced expert in the field of this book. That's the reason the level if math in the book was totally suitable for me. But taking into account the fact that the main audience of the book is managers who already face with professional challenges, the math simplification principle, which was applied to the content of this book, was a bit confusing to me. I was completely satisfied with the knowledge I've got from this book, but I've been left with only one question: do American business practitioners scare of math so much?🤔

  21. 4 out of 5

    Matt Heavner

    This is a surprisingly good high level overview of data science. It showed up on the new books shelf at our library - I'm surprised it is from 2013 ("ancient" in computer book terms) but this has held its age very well. Definitely written as a communication tool between the "suits" and the "hackers" - perhaps just a little too much math for the suits and definitely not enough depth for the hackers, so a good balance. The books avoids "Sigma" and "Pi" notation (not quite sure, maybe the analytics This is a surprisingly good high level overview of data science. It showed up on the new books shelf at our library - I'm surprised it is from 2013 ("ancient" in computer book terms) but this has held its age very well. Definitely written as a communication tool between the "suits" and the "hackers" - perhaps just a little too much math for the suits and definitely not enough depth for the hackers, so a good balance. The books avoids "Sigma" and "Pi" notation (not quite sure, maybe the analytics said that scared off suits?), but doesn't shy away from supportive math. It would be fun to teach a class using this - it was developed through business schools and MBA curriculum, but could have other interesting teaching uses. Good sense of humor and good overall data science framework. Again, I'm really surprised how well it aged for such an "ancient" computer book (and pleasantly surprised I stumbled upon it in the new acquisitions shelf at the library!).

  22. 4 out of 5

    Roy Wang

    The is a well-written and highly accessible guide for non-tech people with a marketing or business executive background, focusing on the underlying principles and data science techniques to solve real-world problems when using data mining and data analytics for improved business outcomes. The authors present just enough math and technical details on how to use those techniques as well as several business scenarios and examples demonstrating how the tech stuffs fall into place so that readers are The is a well-written and highly accessible guide for non-tech people with a marketing or business executive background, focusing on the underlying principles and data science techniques to solve real-world problems when using data mining and data analytics for improved business outcomes. The authors present just enough math and technical details on how to use those techniques as well as several business scenarios and examples demonstrating how the tech stuffs fall into place so that readers are equipped with a solid foundation to dive deeper into the nuts and bolts of the particular fields they're interested in. I would recommend the book to anyone who wants to get their feet wet and learn about the basic principles of doing data science and machine learning.

  23. 5 out of 5

    A Mig

    Indispensable complement to more technical data science books and, overall, a very easy and enjoyable read. A rich selection of business cases (from Whisky list diversification at a liquor store, to stock price movement prediction based on news stories mining, via customer churn management). Thanks to very nice examples and illustrations, I, for example, now better understand (i) purity measure via entropy, and information gain for segmentation, (ii) the importance of expected value for classifi Indispensable complement to more technical data science books and, overall, a very easy and enjoyable read. A rich selection of business cases (from Whisky list diversification at a liquor store, to stock price movement prediction based on news stories mining, via customer churn management). Thanks to very nice examples and illustrations, I, for example, now better understand (i) purity measure via entropy, and information gain for segmentation, (ii) the importance of expected value for classifier use framing, (iii) the relationship between IDF (inverse document frequency) and entropy, etc, etc.

  24. 4 out of 5

    Shayne

    I had only a basic understanding of statistics before tackling this book (least square regression mostly). However, I waa able to draw much from this book as it does a great job of showcasing many different techniques while also providing exemples of which type of situations they can be applied. The book really helps expand perspective and guides the reader if he/she wants to delve deaper into certain subjects. Highly recommended for anyone studying business or who is interested in data science I had only a basic understanding of statistics before tackling this book (least square regression mostly). However, I waa able to draw much from this book as it does a great job of showcasing many different techniques while also providing exemples of which type of situations they can be applied. The book really helps expand perspective and guides the reader if he/she wants to delve deaper into certain subjects. Highly recommended for anyone studying business or who is interested in data science in general.

  25. 4 out of 5

    Quinten Van

    As a graduated Data Science student, I can say that this book and my Master touched the same topics. I would give it 4,5 stars as it is a great book to understand Data Science. For people who are using this book as a starter into Data Science, I would recommend to also have a look at R or Python and look at a dataset (There are build-in Data sets). This way you can have a clearer image of what Data Science is like. A remark for this book is that you need some understanding of data sets in order As a graduated Data Science student, I can say that this book and my Master touched the same topics. I would give it 4,5 stars as it is a great book to understand Data Science. For people who are using this book as a starter into Data Science, I would recommend to also have a look at R or Python and look at a dataset (There are build-in Data sets). This way you can have a clearer image of what Data Science is like. A remark for this book is that you need some understanding of data sets in order to understand it.

  26. 4 out of 5

    Enrique Martinez

    Not what I was expecting, is not an introductory book at all, maybe the last chapters of you want to know some nitty gritty of hiring a data scientist. Analytics is not something that you could learn reading, you need to do it and to do it well, and you can do it using excel not only R or python, so if you really want to learn these are the books that you need: 1. Marketing analytics by Wayne Winston 2. Data Smart by John Foreman Dry reading at several chapters.

  27. 4 out of 5

    David Nishimoto

    I read the book and gained an understanding of the data science concepts and terminology then I search for code samples in python to try see how the classifers worked. It was helped to understand what the classifier did and why it could be applied to a business case before programming classifier to run in python. I thought the book was comprehensive and the author had experience solving business problems

  28. 4 out of 5

    Jeff Hascall

    A solid read that covers the broad spectrum of applicable data science in a business context. It goes into the various techniques with a fair amount of depth. Not recommended for those looking for a purely business focused read, as it's more for those seriously interested in how data science works, including some fairly complex math. If you are looking at data science and want an introduction that's not afraid to give you some meat in the process, check this out.

  29. 4 out of 5

    Jiwon Kim

    I HIGHLY RECOMMEND this book to those who are trying to understand the concepts of data science. He kept the difficult math formulas to the minimum and focused on the core ideas, which made it really easy to read and comprehend. Since I'm watching Andrew Ng's courses online, this book was the perfect complimentary resource. There are difficult chapters, but still overall he has a wonderful way of explaining.

  30. 5 out of 5

    Anton Holmström

    Superb introduction to data mining and it's usage in business. This book does not focus on the technical side, but rather on high level description and explanation of data mining methods and it's area of use. Would recommend to people whom are interested in data mining and it's business usage.

Add a review

Your email address will not be published. Required fields are marked *

Loading...
We use cookies to give you the best online experience. By using our website you agree to our use of cookies in accordance with our cookie policy.