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Data Mining For Dummies, by Meta S. Brown
Free PDF Data Mining For Dummies, by Meta S. Brown
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Delve into your data for the key to success
Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business's entire paradigm for a more successful outcome.
Data Mining for Dummies shows you why it doesn't take a data scientist to gain this advantage, and empowers average business people to start shaping a process relevant to their business's needs. In this book, you'll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. The book explains the details of the knowledge discovery process including:
- Model creation, validity testing, and interpretation
- Effective communication of findings
- Available tools, both paid and open-source
- Data selection, transformation, and evaluation
Data Mining for Dummies takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. You'll gain the confidence you need to start making data mining practices a routine part of your successful business. If you're serious about doing everything you can to push your company to the top, Data Mining for Dummies is your ticket to effective data mining.
- Sales Rank: #282954 in Books
- Published on: 2014-09-29
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .75" w x 7.35" l, .0 pounds
- Binding: Paperback
- 408 pages
From the Back Cover
Learn to:
- Understand key data mining concepts and best practices
- Create a data model and test its validity
- Interpret results and communicate your findings
- Make a business case for investing in data mining
Delve into your data for the key to success
This friendly guide shows you why it doesn’t take a data scientist to reap the benefits of exploring data independently, making informative discoveries, and putting the information to work. Written for those who know more about their business than math, this resource quickly gets you up-to-speed on how data mining can push your company to the top.
- Learn the laws of data mining — get a grip on the principles of data mining, find out how to make data your informant, and set your data mining goals
- A day in the life of a data miner — get to know the members of a typical data mining team, leverage your colleagues’ complementary skills and knowledge, and frame your work with a standard industry practice
- Define and refine — establish a framework for data mining, drill into the CRISP-DM process, and establish good habits
- Make a career in modeling — discover why modeling is the data miner’s path to knowing the unknown
- Dig into your data — draw on the knowledge that you and other members of your team have accumulated from experience and training to extract useful information from data
Open the book and find:
- Common-sense steps for data mining success
- Guidance on letting your business needs direct your software selection process
- Ways to unearth data you already have
- How to conduct experiments—no lab coat required
- Public sources for data
- Guidance on buying data and searching for suppliers
- Plain-English explanations of correlation
- The most common nonlinear models
About the Author
Meta S. Brown helps organizations use practical data analysis to solve everyday business problems. A hands-on data miner who has tackled projects with up to $900 million at stake, she is a recognized expert in cutting-edge business analytics.
Most helpful customer reviews
16 of 17 people found the following review helpful.
The Profession of Data Mining: Make this your First Read
By Keith McCormick
Meta Brown has done an amazing job with this book. It takes the reader in a totally different direction than other books that have attempted to introduce Data Mining to a wider audience. Forgive the detail that I provide. I consider this a watershed book, and will need some room to make my case.
Tom Davenport's very successful books Competing on Analytics: The New Science of Winning have tried to make the case for Data Mining and Predictive Analytics. He has succeeded to a considerable degree, and I recommend his books. Berry and Linoff Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management and Dean Abbott Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst have tried to explain some of the nuts and bolts, adopting different styles as they go, but both styles work, and both books are very good. Eric Seigel Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die has tried to be the consummate popularizer for Predictive Analytics. He has found a wide audience, and deservedly so, but he doesn't capture the Day in the Life. Rather, he tries to give a general audience a sense of why Data Mining matters.
Meta's book is the book that I wish I could have written. I don't think anyone else could have with quite the same style and flair. I know dozens of data miners, but Meta has honed the craft of explaining predictive analytics to a wide audience through years (many years) of diligent, non stop writing and public appearances - and the quality is very high. In another era, or perhaps in another industry, she would be a newspaper columnist, and a damn good one. She is a thought leader in a era where more claim it than earn it.
No other book - truly no other book - captures what it feels like to do what we when we are doing data mining like this book does. The highly successful For Dummies series works because everyone knows that they can read a book in this series first. You won't get half way through, and find yourself lost. Meta has somehow honored that implicit agreement, and yet transcended the genre. This is a truly important book. I bet there are plenty of data mining "certificate" students that have mastered their software tool of choice, but have failed to internalize half of the lessons that Meta has provided here. Yet, the lessons are consumed painlessly, and reading this book is actually quite frequently fun.
One of the things that Meta has done masterfully is to allow the software operations, although present, to fade into the background. They aren't left out completely as that actually could be distracting, but they never take center stage. She isn't shy about details, but she is so wonderfully and wisely judicious about which details she indulges in that you barely notice how much you are learning. Finally, one of the chapters, literally called A Day in your Life as a Data Miner, is just brilliant. It succeeds in its goal in a way that no other books on the subject, and I've read dozens, manages to do. The fact that she boldly places a chapter like that as the second chapter underscore just how well she can explain the thought process without getting bogged down in the technical details, technical details that could easily fill a 1000 page tome.
What do the book cover? There are good chapters on Tom Khabaza's Nine Laws of Data Mining and CRISP-DM. These chapters represent critical background for Data Mining using any combination of software tools that you would encounter. These are combined with an overall of available tools. You could read dozens of books on the subject and would be lucky to find a handful that are truly tool neutral. At best, software neutrality is represented by having two or three software options represented. So how are you going to decide which software option makes sense? Read this book first.
Much of what follows is pretty much required content, and it is well presented. There is an overview of techniques and algorithms. It manages to be a brisk easy read while also being thorough enough to introduce some important concepts. Clearly written for beginners, it is not watered down. If you have encountered a book on the subject where you felt that the explanations were just out of your reach, then read these sections first, and you will be ready for the numerous books that discuss the algorithms in technical detail.
Importantly data preparation is not ignored. You truly get a feel for what preparing data for data mining is like. Despite the fact that almost all authors claim that it is the most important step, they then proceed to invest no pages in it. You will have to find a book dedicated to your tool of choice to read next, but this is a great place to start. Also, if you have ambitions to be a data miner, but are not yet in the role, the section on Public Data Sources will be especially useful. While useful to any data miner, these resources are just waiting for your discovery and analysis. Finally, a classic For Dummies addition is Ten Great Resources for Data Miners. The author probably could have listed ten times that many so you know that these "top ten" are going to be a great place to start.
I can not recommend this book more highly as first book in your journey to become a data miner.
10 of 11 people found the following review helpful.
Data Mining For Dummies is for anyone interested in learning about data mining and BigData from a world-class expert!
By William Dean A. Garner
Full Disclosure: I connected with the author of Data Mining For Dummies, Ms. Meta Brown, on LinkedIn about a month ago, in September 2014. Before then, I had never heard of "data mining" or "BigData." After several chats with Ms. Brown, I dived into the once-esotheric deep of data mining and BigData . . . and will be forever grateful to her for having introduced me! I purchased the ebook on my own, on 04 October 2014, and blazed through it, cover to cover, in one sitting. Yes, it was *that* compelling, fascinating, well written, and presented!
As an aside, I am a former scientist, trained in physics, engineering and biology. So I LOVE mathematics and statistics and experimental design. Meta Brown's Data Mining For Dummies is now my fave book on statistical analysis, in general, even though Data Mining For Dummies covers sooo much more. I wish she would rewrite all the statistics and analyses books I studied in university and grad school.
As much as I want to tell you that Data Mining For Dummies is for everyone out there . . . sadly, it is not.
This gem is written for those who at least appreciate general-level mathematics, statistics and experimental design, and how to apply these general yet powerful tools to study various problems and issues in any field.
However, those of you who do not fall into this narrow category above can rest assured that you can and will glean much from Data Mining For Dummies simply by studying the chapter titles, major and minor headings, helpful tips and lists, and using the wonderful resources Ms. Brown has included. Data Mining For Dummies can be understood on an elementary level by employing this method, which then will encourage you to seek assistance from an expert in the field. That way, she will fill you in on the details you did not read about or understand when you glanced through the book on your own.
In this regard, Data Mining For Dummies is for anyone interested in learning about data mining and BigData, and how to gain even better knowledge about these new and exciting subjects and fields.
So, whom is this book truly for?
Data Mining For Dummies is an excellent resource for IT, SAM and ITAM managers and quality-control personnel, all of whom have ready access to BigData from their own respective company. While Ms. Brown has gone out of her way to make the book easily accessible and readable, even conversational (yay!), it is still somewhat advanced for the average professional. Therefore, it is up to the expert to take this book and help the uninitiated (e.g. The Boss) understand and appreciate what data mining and BigData are, how they can assist you in revealing new and helpful patterns about your own BigData, and how to implement what you've studied, discovered and learned to the bottom line of your business.
What I really love about Data Mining For Dummies are Ms. Brown's "9 Laws of Data Mining," which can be applied to other fields, as well:
1. Business Goals: Business objectives are the origin of every data-mining solution
2. Business Knowledge: Business knowledge to every step of the data-mining process
3. Data Preparation: Data preparation is more than half of every data-mining process
4. Right Model: The right model for a given application can only be discovered by experiment
5. Pattern: There are always patterns
6. Amplification: data mining amplifies perception in the business domain
7. Prediction: Prediction increases information locally by generalization
8. Value: The value of data-mining results is not determined by the accuracy or stability of predictive models
9. Change: All patterns are subject to change
Another very helpful bit of intel is Table 6-1 The Elements of a Business Case:
Situations, Options, Benefits, and Costs. This intel also can be used in almost any business proposal, because it addresses some of the most important elements of a business, in general. I found myself making copious notes on my online Kindle reader, then transcribing them to pieces of paper to hang on my wall in my office!
As stated previously, Data Mining For Dummies also has very helpful resources, including many online sites that have helpful statistics about . . . anything! If you're a researcher on labor or business or nonprofits, then you'll find some useful sites to visit. Ms. Brown also lists two dozen portals for federal and local government data and statistics that have FREE data. You also can buy data from sources she lists, as well.
I highly recommend Data Mining For Dummies to anyone who:
1. Has a business or venture or project, and would like to improve its bottom line
2. Would like to get up to speed on the latest in data mining and BigData
3. Would like to become a data miner
4. Supports The Boss as an IT, ITAM, SAM person
Bravo to Ms. Meta Brown for producing what is most certain to become a BIG part of the foundation of knowledge of these hot topics, data mining and BigData!
1 of 1 people found the following review helpful.
Almost everything you need ...
By Gabriele F.
... to get started, and more.
Very smart also for the advices on how to improve your analyst's mindset.
See all 11 customer reviews...
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