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Data Mining For Dummies
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 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.
416 pages, Paperback
First published September 4, 2014
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Displaying 1 - 9 of 9 reviews
December 28, 2014
I'm going to start by saying I really enjoyed this book. I've worked with data for nearly 20 years but there's always something new, interesting and energising to learn.
However, my bugbear is creating a bubble for data geeks, data nerds and big data consultants. Insights are for everyone and data is one resource we can tap into for that. I am always on the lookout for books that help non-techies understand techie concepts and most importantly, what's in it for them. This book falls squarely into that plain English, no bull approach.
Meta S. Brown aims this book squarely at domain experts - people who already have the know how that comes from working daily in their chosen fields. She then goes on to demonstrate how they can benefit from one method of manipulating data for insight: data mining.
I won't pretend this book is perfect - it's not. I understand some of the decisions, for example to focus on visual tools over text based ones. This is a minor niggle. The rest of the books covers in a good balance between succinctness and detail the *methodology* and *approach* to data mining.
This is key. With this grounding, the learning curve to pick up using a specific tool is reduced. And you'll need to invest in that because that's where this book fails to deliver. However, I'm pretty sure it would have been twice the size had Meta attempted to correct this, so as I mentioned, a minor niggle.
Bottom line: should you read this and what's in it for you?
TLDR; Yes you should. Especially if you already do data jiggery pokery using spreadsheets, have that essential domain knowledge and want to up your game.
Even if you're an expert data zen master, you'll benefit from comparing Meta's experiences, methodology and approach based on CRISP-DM to your own. A little practice-based analysis is a good thing.
Originally published on ekoner.com
However, my bugbear is creating a bubble for data geeks, data nerds and big data consultants. Insights are for everyone and data is one resource we can tap into for that. I am always on the lookout for books that help non-techies understand techie concepts and most importantly, what's in it for them. This book falls squarely into that plain English, no bull approach.
Meta S. Brown aims this book squarely at domain experts - people who already have the know how that comes from working daily in their chosen fields. She then goes on to demonstrate how they can benefit from one method of manipulating data for insight: data mining.
I won't pretend this book is perfect - it's not. I understand some of the decisions, for example to focus on visual tools over text based ones. This is a minor niggle. The rest of the books covers in a good balance between succinctness and detail the *methodology* and *approach* to data mining.
This is key. With this grounding, the learning curve to pick up using a specific tool is reduced. And you'll need to invest in that because that's where this book fails to deliver. However, I'm pretty sure it would have been twice the size had Meta attempted to correct this, so as I mentioned, a minor niggle.
Bottom line: should you read this and what's in it for you?
TLDR; Yes you should. Especially if you already do data jiggery pokery using spreadsheets, have that essential domain knowledge and want to up your game.
Even if you're an expert data zen master, you'll benefit from comparing Meta's experiences, methodology and approach based on CRISP-DM to your own. A little practice-based analysis is a good thing.
Originally published on ekoner.com
June 16, 2019
I wanted to start teaching myself about data-mining because it can e very useful tool for programming. The content of this book is so general than I expected. It does not cover any technical skills that you can get out of reading from it. It talks about moral responsibilities and moral obligations as a data miner which is actually a good input for beginners but not what I am looking for. Its a good read though.
October 1, 2017
Written in a very non-bullshitting tone. Cudos!
September 16, 2022
Mine All Day
A great book on mining data.
As someone who has gotten a lot out of this book and actioned on it, I think this was a great book.
Try it out!
3.7/5
A great book on mining data.
As someone who has gotten a lot out of this book and actioned on it, I think this was a great book.
Try it out!
3.7/5
December 15, 2015
Read through the first 100 pages and skimmed through the rest. Author claims data mining is easy, it isn't. It's something that you'll need to commit a lot of your time to acquire a solid skill set at. Book has good content in it and goes through the process of data mining but also is like 3/4ths fluff that shouldn't be in a book like this.
July 23, 2015
I needed this book to help me soft-land into more complicated material. Granted that books from this series are supposed to be light, this one was more theoretical than expected. I would rate subject coverage at about 60%.
December 26, 2014
A good introductory to the vocabulary of datamining. Onward to R and more Google Fusion Tables.
June 13, 2015
Second time through--great introduction to terms and processes.
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December 24, 2017expert in meet industry
This entire review has been hidden because of spoilers.
Displaying 1 - 9 of 9 reviews