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31858

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marke...ting. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics.Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit. FROM THE REVIEWS: TECHNOMETRICS "[This] is a vast and complex book. Generally, it concentrates on explaining why and how the methods work, rather than how to use them. Examples and especially the visualizations are principle features...As a source for the methods of statistical learning...it will probably be a long time before there is a competitor to this book."

Author:

Trevor Hastie

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169050

Data Smart: Using Data Science to Transform Information into Insight

Data Science Gets Thrown Around In The Press Like It's Magic. Major Retailers Are Predicting Everything From When Their Customers Are Pregnant To When They Want A New Pair Of Chuck Taylors. It's A Bra...ve New World Where Seemingly Meaningless Data Can Be Transformed Into Valuable Insight To Drive Smart Business Decisions. But How Does One Exactly Do Data Science? Do You Have To Hire One Of These Priests Of The Dark Arts, The Data Scientist, To Extract This Gold From Your Data? Nope. Data Science Is Little More Than Using Straight-forward Steps To Process Raw Data Into Actionable Insight. And In Data Smart, Author And Data Scientist John Foreman Will Show You How That's Done Within The Familiar Environment Of A Spreadsheet.-- Everything You Ever Needed To Know About Spreadsheets But Were Too Afraid To Ask -- Cluster Analysis Part I : Using K-means To Segment Your Customer Base -- Naïve Bayes And The Incredible Lightness Of Being An Idiot -- Optimization Modeling : Because That Fresh Squeezed Orange Juice Ain't Gonna Blend Itself -- Cluster Analysis Part Ii : Network Graphs And Community Detection -- The Granddaddy Of Supervised Artificial Intelligence : Regression -- Ensemble Models : A Whole Lot Of Bad Pizza -- Forecasting : Breathe Easy; You Can't Win -- Outlier Detection : Just Because They're Odd Doesn't Mean They're Unimportant -- Moving From Spreadsheets Into R -- Conclusion. John W. Foreman. Companion Website. Includes Index.

Author:

John W. Foreman

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210569

Data Science from Scratch: First Principles with Python

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data scienc...e. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.Get a crash course in PythonLearn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data scienceCollect, explore, clean, munge, and manipulate dataDive into the fundamentals of machine learningImplement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clusteringExplore recommender systems, natural language processing, network analysis, MapReduce, and databases

Author:

Joel Grus

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39684

Data Mining: Practical Machine Learning Tools and Techniques

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting a...lgorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.* Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods* Performance improvement techniques that work by transforming the input or output* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization—in a new, interactive interface

Author:

Ian H. Witten

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166634

Doing Data Science

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, ...interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

Author:

Rachel Schutt

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248738

Numsense! Data Science for the Layman: No Math Added

No description available

Author:

Annalyn Ng

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258763

Data Science

The Goal Of Data Science Is To Improve Decision Making Through The Analysis Of Data. Today Data Science Determines The Ads We See Online, The Books And Movies That Are Recommended To Us Online, Which ...Emails Are Filtered Into Our Spam Folders, And Even How Much We Pay For Health Insurance. This Volume In The Mit Press Essential Knowledge Series Offers A Concise Introduction To The Emerging Field Of Data Science, Explaining Its Evolution, Current Uses, Data Infrastructure Issues, And Ethical Challenges.--provided By Publisher. What Is Data Science? -- What Is Data And What Is A Dataset? -- The Data Science Ecosystem -- Machine Learning 101 -- Standard Data Science Tasks -- Privacy And Ethics -- Future Trends And Principles Of Success. John D. Kelleher And Brendan Tierney. Includes Bibliographical References And Index.

Author:

John D. Kelleher

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217335

Python Data Science Handbook: Tools and Techniques for Developers

For Many Researchers, Python Is A First-class Tool Mainly Because Of Its Libraries For Storing, Manipulating, And Gaining Insight From Data. Several Resources Exist For Individual Pieces Of This Data ...Science Stack, But Only With The Python Data Science Handbook Do You Get Them All—ipython, Numpy, Pandas, Matplotlib, Scikit-learn, And Other Related Tools. Working Scientists And Data Crunchers Familiar With Reading And Writing Python Code Will Find This Comprehensive Desk Reference Ideal For Tackling Day-to-day Issues: Manipulating, Transforming, And Cleaning Data; Visualizing Different Types Of Data; And Using Data To Build Statistical Or Machine Learning Models. Quite Simply, This Is The Must-have Reference For Scientific Computing In Python.-- Ipython: Beyond Normal Python -- Introduction To Numpy -- Data Manipulation With Pandas -- Visualization With Matplatlib -- Machine Learning. Jake Vanderplas. Includes Index.

Author:

Jake Vanderplas

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125844

Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists

Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experien...ced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysis'Finally, a concise reference for understanding how to conquer piles of data.'--Austin King, Senior Web Developer, Mozilla'An indispensable text for aspiring data scientists.'--Michael E. Driscoll, CEO/Founder, Dataspora

Author:

Philipp K. Janert

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225750

Practical Statistics for Data Scientists: 50 Essential Concepts

Peter Bruce And Andrew Bruce. Includes Bibliographical References And Index.

Author:

Peter Bruce

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