• Title: Data Mining for the Masses
• Author(s) Matthew North
• Publisher: The Global Text Project (GTP); eBook (Creative Commons Licensed)
• License(s): CC BY 3.0
• Paperback: 256 pages
• eBook: PDF (264 pages, 16.7 MB)
• Language: English
• ISBN-10: 0615684378
• ISBN-13: 978-0615684376
• Share This:  

Book Description

Have you ever found yourself working with a spreadsheet full of data and wishing you could make more sense of the numbers. Have you reviewed sales or operations reports, wondering if there's a better way to anticipate your customers' needs. Perhaps you've even thought to yourself: There's got to be more to these figures than what I'm seeing!

Data Mining can help, and you don't need a Ph.D. in Computer Science to do it. You can forecast staffing levels, predict demand for inventory, even sift through millions of lines of customer emails looking for common themes - all using data mining. It's easier than you might think.

In Data Mining for the Masses, professor Matt North - a former risk analyst and database developer for eBay.com - uses simple examples, clear explanations and free, powerful, easy-to-use software to teach you the basics of data mining; techniques that can help you answer some of your toughest business questions.

You've got data and you know it's got value, if only you can figure out how to unlock it. This book can show you how. Let's start digging!

Through an agreement with the Global Text Project, an electronic version of this text is available online at (http://globaltext.terry.uga.edu/books). Proceeds from the sales of printed copies through Amazon enable the author to support the Global Text Project's goal of making electronic texts available to students in developing economies.

• N/A

• Amazon

• Data Analysis and Data Mining
• Algorithms and Data Structures
• Statistics, R Language and SAS Programming
• Probability, Stochastic Process, Queueing Theory, etc.

• Data Mining for the Masses (Matthew North)
• The Mirror Site (1) - PDF

• Mining of Massive Datasets (Jure Leskovec, et al)

  It focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically.

• Data Mining and Analysis: Fundamental Concepts and Algorithms

  This textbook provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification.

• Spectral Feature Selection for Data Mining (Zheng A. Zhao, et al.)

  This book introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications.

• A Programmer's Guide to Data Mining (Ron Zacharski)

  This book is a tool for learning basic data mining techniques. If you are a programmer interested in learning a bit about data mining you might be interested in a beginner's hands-on guide as a first step. That's what this book provides.

• An Introduction to Data Mining (Dr. Saed Sayad)

  This book presents fundamental concepts and algorithms for those learning data mining for the first time, provides both theoretical and practical coverage of all data mining topics. Includes extensive number of integrated examples and figures.

• Data Mining Desktop Survival Guide (Graham William)

  This book thoroughly acquaints you with the new generation of data mining tools and techniques and shows you how to use them to make better business decisions. Assuming no prior knowledge of R or data mining/statistical techniques.

• Mining Social Media: Finding Stories in Internet Data

  This book shows you how to use Python and key data analysis tools to find the stories buried in social media. Perform advanced data analysis using Python, Jupyter Notebooks, and the Pandas library.

• Social Media Mining: An Introduction (Reza Zafarani, et al)

  This textbook introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining.

• Mining the Web: Discovering Knowledge from Hypertext Data

  This is is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issues-including Web crawling and indexing.

• Twitter Data Analytics (Shamanth Kumar, et al)

  This book provides methods for harnessing Twitter data to discover solutions to complex inquiries. The brief introduces the process of collecting data through Twitter's APIs and offers strategies for curating large datasets.

• O'Reilly® Mining the Social Web, 2nd Edition (Matthew A. Russell)

  This book shows you how to answer these questions like how can you tap into social data and discover who's connecting with whom, which insights are lurking just beneath the surface, and what people are talking about?