Bad Data Handbook

Cleaning Up The Data So You Can Get Back To Work
Author: Q. Ethan McCallum
Publisher: "O'Reilly Media, Inc."
ISBN: 1449324975
Category: Computers
Page: 264
View: 4942
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What is bad data? Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they’ve recovered from nasty data problems. From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line? Bad data is data that gets in the way. This book explains effective ways to get around it. Among the many topics covered, you’ll discover how to: Test drive your data to see if it’s ready for analysis Work spreadsheet data into a usable form Handle encoding problems that lurk in text data Develop a successful web-scraping effort Use NLP tools to reveal the real sentiment of online reviews Address cloud computing issues that can impact your analysis effort Avoid policies that create data analysis roadblocks Take a systematic approach to data quality analysis

Applied Mathematics for the Analysis of Biomedical Data

Models, Methods, and MATLAB
Author: Peter J. Costa
Publisher: John Wiley & Sons
ISBN: 1119269490
Category: Mathematics
Page: 448
View: 2040
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Features a practical approach to the analysis of biomedical data via mathematical methods and provides a MATLAB® toolbox for the collection, visualization, and evaluation of experimental and real-life data Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB® presents a practical approach to the task that biological scientists face when analyzing data. The primary focus is on the application of mathematical models and scientific computing methods to provide insight into the behavior of biological systems. The author draws upon his experience in academia, industry, and government–sponsored research as well as his expertise in MATLAB to produce a suite of computer programs with applications in epidemiology, machine learning, and biostatistics. These models are derived from real–world data and concerns. Among the topics included are the spread of infectious disease (HIV/AIDS) through a population, statistical pattern recognition methods to determine the presence of disease in a diagnostic sample, and the fundamentals of hypothesis testing. In addition, the author uses his professional experiences to present unique case studies whose analyses provide detailed insights into biological systems and the problems inherent in their examination. The book contains a well-developed and tested set of MATLAB functions that act as a general toolbox for practitioners of quantitative biology and biostatistics. This combination of MATLAB functions and practical tips amplifies the book’s technical merit and value to industry professionals. Through numerous examples and sample code blocks, the book provides readers with illustrations of MATLAB programming. Moreover, the associated toolbox permits readers to engage in the process of data analysis without needing to delve deeply into the mathematical theory. This gives an accessible view of the material for readers with varied backgrounds. As a result, the book provides a streamlined framework for the development of mathematical models, algorithms, and the corresponding computer code. In addition, the book features: Real–world computational procedures that can be readily applied to similar problems without the need for keen mathematical acumen Clear delineation of topics to accelerate access to data analysis Access to a book companion website containing the MATLAB toolbox created for this book, as well as a Solutions Manual with solutions to selected exercises Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB® is an excellent textbook for students in mathematics, biostatistics, the life and social sciences, and quantitative, computational, and mathematical biology. This book is also an ideal reference for industrial scientists, biostatisticians, product development scientists, and practitioners who use mathematical models of biological systems in biomedical research, medical device development, and pharmaceutical submissions.

Cognitive Computing: Theory and Applications


Author: Vijay V Raghavan,Venkat N. Gudivada,Venu Govindaraju,C.R. Rao
Publisher: Elsevier
ISBN: 0444637516
Category: Mathematics
Page: 404
View: 7228
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Cognitive Computing: Theory and Applications, written by internationally renowned experts, focuses on cognitive computing and its theory and applications, including the use of cognitive computing to manage renewable energy, the environment, and other scarce resources, machine learning models and algorithms, biometrics, Kernel Based Models for transductive learning, neural networks, graph analytics in cyber security, neural networks, data driven speech recognition, and analytical platforms to study the brain-computer interface. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety of diverse applications and recent developments Contributors are internationally renowned experts in their respective areas

Gestión de la información web usando Python


Author: Sarasa Cabezuelo, Antonio
Publisher: Editorial UOC
ISBN: 8491164863
Category: Computers
Page: N.A
View: 8428
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En este manual se realiza una introducción a un conjunto de herramientas y técnicas para el acceso y procesamiento de datos web, que se encuentran en formatos como XML, CSV o JSON, o bien en bases de datos tanto relacionales como NoSQL. El objetivo de esta obra es acercar al lector estos conocimientos a partir de las herramientas y librerías de un lenguaje de programación concreto como Python, el más utilizado hoy en el área del análisis de datos y big data. El primer capítulo constituye una introducción a Python, que sirve como lenguaje vehicular en el resto de los capítulos, los cuales se dedican a estudiar el acceso y procesamiento de datos en los formatos XML, JSON y CSV. Los siguientes capítulos abordan el acceso a bases de datos relacionales, SQLite y MySQL, y a la base de datos NoSQL MongoDB. En los dos últimos capítulos, se tratan técnicas de extracción de información usando web scraping y programación de páginas web con la framework Bottle. Cada capítulo contiene algunos ejercicios propuestos para fijar las ideas expuestas.

Data Quality Assessment


Author: Arkady Maydanchik
Publisher: Technics Publications
ISBN: 163462047X
Category: Computers
Page: 336
View: 2236
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Imagine a group of prehistoric hunters armed with stone-tipped spears. Their primitive weapons made hunting large animals, such as mammoths, dangerous work. Over time, however, a new breed of hunters developed. They would stretch the skin of a previously killed mammoth on the wall and throw their spears, while observing which spear, thrown from which angle and distance, penetrated the skin the best. The data gathered helped them make better spears and develop better hunting strategies. Quality data is the key to any advancement, whether it’s from the Stone Age to the Bronze Age. Or from the Information Age to whatever Age comes next. The success of corporations and government institutions largely depends on the efficiency with which they can collect, organize, and utilize data about products, customers, competitors, and employees. Fortunately, improving your data quality doesn’t have to be such a mammoth task. DATA QUALITY ASSESSMENT is a must read for anyone who needs to understand, correct, or prevent data quality issues in their organization. Skipping theory and focusing purely on what is practical and what works, this text contains a proven approach to identifying, warehousing, and analyzing data errors – the first step in any data quality program. Master techniques in: • Data profiling and gathering metadata • Identifying, designing, and implementing data quality rules • Organizing rule and error catalogues • Ensuring accuracy and completeness of the data quality assessment • Constructing the dimensional data quality scorecard • Executing a recurrent data quality assessment This is one of those books that marks a milestone in the evolution of a discipline. Arkady's insights and techniques fuel the transition of data quality management from art to science -- from crafting to engineering. From deep experience, with thoughtful structure, and with engaging style Arkady brings the discipline of data quality to practitioners. David Wells, Director of Education, Data Warehousing Institute

Python for Data Analysis

Data Wrangling with Pandas, NumPy, and IPython
Author: Wes McKinney
Publisher: "O'Reilly Media, Inc."
ISBN: 1491957611
Category: Computers
Page: 550
View: 5958
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Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Managing RPM-Based Systems with Kickstart and Yum


Author: Q. Ethan McCallum
Publisher: "O'Reilly Media, Inc."
ISBN: 1491905905
Category: Computers
Page: 47
View: 5490
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Managing multiple Red Hat-based systems can be easy--with the right tools. The yum package manager and the Kickstart installation utility are full of power and potential for automatic installation, customization, and updates. Here's what you need to know to take control of your systems.

Statistical Data Cleaning with Applications in R


Author: Mark van der Loo,Edwin de Jonge
Publisher: John Wiley & Sons
ISBN: 1118897153
Category: Computers
Page: 320
View: 7731
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A comprehensive guide to automated statistical data cleaning The production of clean data is a complex and time-consuming process that requires both technical know-how and statistical expertise. Statistical Data Cleaning with Applications in R brings together a wide range of techniques for cleaning textual, numeric or categorical data. This book examines technical data cleaning methods relating to data representation and data structure. A prominent role is given to statistical data validation, data cleaning based on predefined restrictions, and data cleaning strategy. Key features: Focuses on the automation of data cleaning methods, including both theory and applications written in R. Enables the reader to design data cleaning processes for either one-off analytical purposes or for setting up production systems that clean data on a regular basis. Explores statistical techniques for solving issues such as incompleteness, contradictions and outliers, integration of data cleaning components and quality monitoring. Supported by an accompanying website featuring data and R code. Statistical Data Cleaning with Applications in R enables data scientists and statistical analysts working with data to deepen their understanding of data cleaning as well as to upgrade their practical data cleaning skills. This book can also be used as material for courses in both data cleaning and data analysis.

Clean Data


Author: Megan Squire
Publisher: Packt Publishing Ltd
ISBN: 1785289039
Category: Computers
Page: 272
View: 1431
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If you are a data scientist of any level, beginners included, and interested in cleaning up your data, this is the book for you! Experience with Python or PHP is assumed, but no previous knowledge of data cleaning is needed.

Clean Code

A Handbook of Agile Software Craftsmanship
Author: Robert C. Martin
Publisher: Pearson Education
ISBN: 0132350882
Category: Computers
Page: 431
View: 790
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Looks at the principles and clean code, includes case studies showcasing the practices of writing clean code, and contains a list of heuristics and "smells" accumulated from the process of writing clean code.

Visualize This

The FlowingData Guide to Design, Visualization, and Statistics
Author: Nathan Yau
Publisher: John Wiley & Sons
ISBN: 1118140265
Category: Computers
Page: 384
View: 1097
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Practical data design tips from a data visualization expert of the modern age Data doesn?t decrease; it is ever-increasing and can be overwhelming to organize in a way that makes sense to its intended audience. Wouldn?t it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of Nathan Yau, we can. With this full-color book, data visualization guru and author Nathan Yau uses step-by-step tutorials to show you how to visualize and tell stories with data. He explains how to gather, parse, and format data and then design high quality graphics that help you explore and present patterns, outliers, and relationships. Presents a unique approach to visualizing and telling stories with data, from a data visualization expert and the creator of flowingdata.com, Nathan Yau Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbers Details tools that can be used to visualize data-native graphics for the Web, such as ActionScript, Flash libraries, PHP, and JavaScript and tools to design graphics for print, such as R and Illustrator Contains numerous examples and descriptions of patterns and outliers and explains how to show them Visualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing.

Business Models for the Data Economy


Author: Q. Ethan McCallum,Ken Gleason
Publisher: "O'Reilly Media, Inc."
ISBN: 1491947063
Category: Computers
Page: 28
View: 6882
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You're sitting on a pile of interesting data. How do you transform that into money? It's easy to focus on the contents of the data itself, and to succumb to the (rather unimaginative) idea of simply collecting and reselling it in raw form. While that's certainly profitable right now, you'd do well to explore other opportunities if you expect to be in the data business long-term. In this paper, we'll share a framework we developed around monetizing data. We'll show you how to think beyond pure collection and storage, to move up the value chain and consider longer-term opportunities.

Excel Hacks

Tips & Tools for Streamlining Your Spreadsheets
Author: David Hawley,Raina Hawley
Publisher: "O'Reilly Media, Inc."
ISBN: 9780596555283
Category: Computers
Page: 412
View: 6261
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Millions of users create and share Excel spreadsheets every day, but few go deeply enough to learn the techniques that will make their work much easier. There are many ways to take advantage of Excel's advanced capabilities without spending hours on advanced study. Excel Hacks provides more than 130 hacks -- clever tools, tips and techniques -- that will leapfrog your work beyond the ordinary. Now expanded to include Excel 2007, this resourceful, roll-up-your-sleeves guide gives you little known "backdoor" tricks for several Excel versions using different platforms and external applications. Think of this book as a toolbox. When a need arises or a problem occurs, you can simply use the right tool for the job. Hacks are grouped into chapters so you can find what you need quickly, including ways to: Reduce workbook and worksheet frustration -- manage how users interact with worksheets, find and highlight information, and deal with debris and corruption. Analyze and manage data -- extend and automate these features, moving beyond the limited tasks they were designed to perform. Hack names -- learn not only how to name cells and ranges, but also how to create names that adapt to the data in your spreadsheet. Get the most out of PivotTables -- avoid the problems that make them frustrating and learn how to extend them. Create customized charts -- tweak and combine Excel's built-in charting capabilities. Hack formulas and functions -- subjects range from moving formulas around to dealing with datatype issues to improving recalculation time. Make the most of macros -- including ways to manage them and use them to extend other features. Use the enhanced capabilities of Microsoft Office 2007 to combine Excel with Word, Access, and Outlook. You can either browse through the book or read it from cover to cover, studying the procedures and scripts to learn more about Excel. However you use it, Excel Hacks will help you increase productivity and give you hours of "hacking" enjoyment along the way.

Python Data Science Handbook

Essential Tools for Working with Data
Author: Jake VanderPlas
Publisher: "O'Reilly Media, Inc."
ISBN: 1491912138
Category: Computers
Page: 548
View: 5596
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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. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

The Data Journalism Handbook

How Journalists Can Use Data to Improve the News
Author: Jonathan Gray,Lucy Chambers,Liliana Bounegru
Publisher: "O'Reilly Media, Inc."
ISBN: 1449330029
Category: Language Arts & Disciplines
Page: 242
View: 6647
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When you combine the sheer scale and range of digital information now available with a journalist’s "nose for news" and her ability to tell a compelling story, a new world of possibility opens up. With The Data Journalism Handbook, you’ll explore the potential, limits, and applied uses of this new and fascinating field. This valuable handbook has attracted scores of contributors since the European Journalism Centre and the Open Knowledge Foundation launched the project at MozFest 2011. Through a collection of tips and techniques from leading journalists, professors, software developers, and data analysts, you’ll learn how data can be either the source of data journalism or a tool with which the story is told—or both. Examine the use of data journalism at the BBC, the Chicago Tribune, the Guardian, and other news organizations Explore in-depth case studies on elections, riots, school performance, and corruption Learn how to find data from the Web, through freedom of information laws, and by "crowd sourcing" Extract information from raw data with tips for working with numbers and statistics and using data visualization Deliver data through infographics, news apps, open data platforms, and download links

Parallel R

Data Analysis in the Distributed World
Author: Q. Ethan McCallum,Stephen Weston
Publisher: "O'Reilly Media, Inc."
ISBN: 1449320333
Category: Computers
Page: 126
View: 8610
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It’s tough to argue with R as a high-quality, cross-platform, open source statistical software product—unless you’re in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets, including three chapters on using R and Hadoop together. You’ll learn the basics of Snow, Multicore, Parallel, Segue, RHIPE, and Hadoop Streaming, including how to find them, how to use them, when they work well, and when they don’t. With these packages, you can overcome R’s single-threaded nature by spreading work across multiple CPUs, or offloading work to multiple machines to address R’s memory barrier. Snow: works well in a traditional cluster environment Multicore: popular for multiprocessor and multicore computers Parallel: part of the upcoming R 2.14.0 release R+Hadoop: provides low-level access to a popular form of cluster computing RHIPE: uses Hadoop’s power with R’s language and interactive shell Segue: lets you use Elastic MapReduce as a backend for lapply-style operations

Guerrilla Analytics

A Practical Approach to Working with Data
Author: Enda Ridge
Publisher: Morgan Kaufmann
ISBN: 0128005033
Category: Computers
Page: 276
View: 6290
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Doing data science is difficult. Projects are typically very dynamic with requirements that change as data understanding grows. The data itself arrives piecemeal, is added to, replaced, contains undiscovered flaws and comes from a variety of sources. Teams also have mixed skill sets and tooling is often limited. Despite these disruptions, a data science team must get off the ground fast and begin demonstrating value with traceable, tested work products. This is when you need Guerrilla Analytics. In this book, you will learn about: The Guerrilla Analytics Principles: simple rules of thumb for maintaining data provenance across the entire analytics life cycle from data extraction, through analysis to reporting. Reproducible, traceable analytics: how to design and implement work products that are reproducible, testable and stand up to external scrutiny. Practice tips and war stories: 90 practice tips and 16 war stories based on real-world project challenges encountered in consulting, pre-sales and research. Preparing for battle: how to set up your team's analytics environment in terms of tooling, skill sets, workflows and conventions. Data gymnastics: over a dozen analytics patterns that your team will encounter again and again in projects The Guerrilla Analytics Principles: simple rules of thumb for maintaining data provenance across the entire analytics life cycle from data extraction, through analysis to reporting Reproducible, traceable analytics: how to design and implement work products that are reproducible, testable and stand up to external scrutiny Practice tips and war stories: 90 practice tips and 16 war stories based on real-world project challenges encountered in consulting, pre-sales and research Preparing for battle: how to set up your team's analytics environment in terms of tooling, skill sets, workflows and conventions Data gymnastics: over a dozen analytics patterns that your team will encounter again and again in projects

Manufacturing Demand


Author: David Lewis
Publisher: New Year Publishing
ISBN: 1935547372
Category: Business & Economics
Page: 156
View: 3410
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Historically, the discipline of marketing has been heavily skewed toward a subjective art at the expense of a measurable science. But the days of hunches, intuitions, and incomplete or misleading perspectives are rapidly disappearing. Today, savvy marketers and forward-looking organizations are embracing innovative new models driven by cutting-edge technology and analytics to align sales and marketing, pinpoint (and respond to) customer needs, and achieve breakthrough revenue gains. In Manufacturing Demand, marketing guru David Lewis, CEO of DemandGen International, reveals the transformations taking place in marketing today, including the rise of the marketing geek and the emergence of the so-called fifth and sixth P s of marketing: Process and Programming. You ll learn about the key practices and principles of creating your demand-generation factory: buyer personas, the demand funnel, lead scoring, lead nurturing, and analytics. Plus, Manufacturing Demand presents plenty of actionable tips and recommendations as well as real-world case studies that showcase how leading companies are achieving tremendous results applying these principles of successful lead management. If you re ready to move into of the next generation of marketing, get ready to start Manufacturing Demand.

An Executive's Guide to Fundraising Operations

Principles, Tools, and Trends
Author: Christopher M. Cannon
Publisher: John Wiley & Sons
ISBN: 9781118030295
Category: Business & Economics
Page: 256
View: 1705
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A straightforward guide to the principles of effective fundraising operations An Executive Guide to Fundraising Operations provides fundraisers with easy-to-understand approaches to evaluate and address fundraising operations needs and opportunities. This guide simplifies and focuses on the analysis of problems and needs, allowing a quick return to fundraising. Provides the essential framework to improve and innovate development operations Includes dozens of practical tools, including sample policies for data, database, reporting, and business processes Offers sample workflow illustrations for gift processing and acknowledgment, report specification, and other processes Features sample reports for campaign management, performance management, and exception management Delivers effective calculators for operational rules of thumb No matter what the department is called, most fundraisers struggle with evaluating operational issues. This guide leads you through principles of effective fundraising operations, simplifies complicated topics, and offers solutions to some of the most vexing operations dilemmas.

Data Analysis with Open Source Tools

A Hands-On Guide for Programmers and Data Scientists
Author: Philipp K. Janert
Publisher: "O'Reilly Media, Inc."
ISBN: 1449396658
Category: Computers
Page: 540
View: 8970
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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 experienced 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 variables Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments Mine data with computationally intensive methods such as simulation and clustering Make your conclusions understandable through reports, dashboards, and other metrics programs Understand financial calculations, including the time-value of money Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations Become 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