Author: Philipp K. Janert
Publisher: "O'Reilly Media, Inc."
ISBN: 1449396658
Category : Computers
Languages : en
Pages : 534

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Book Description
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

Author: Segall, Richard S.
Publisher: IGI Global
ISBN: 1799827704
Category : Computers
Languages : en
Pages : 237

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Book Description
With the development of computing technologies in today’s modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines.

Author: Jeffrey Strickland
Publisher: Lulu.com
ISBN: 1365270416
Category : Business & Economics
Languages : en
Pages : 708

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Book Description
This book is about using open-source tools in data analytics. The book covers several subjects, including descriptive and predictive modeling, gradient boosting, cluster modeling, logistic regression, and artificial neural networks, among other topics.

Author: World Intellectual Property Organization
Publisher: WIPO
ISBN: 9280525298
Category : Law
Languages : en
Pages : 131

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Book Description
These Guidelines are designed both for general users of patent information, as well as for those involved in producing Patent Landscape Reports (PLRs). They provide step-by-step instructions on how to prepare a PLR, as well as background information such as objectives, patent analytics, concepts and frameworks.

Author: Dhiraj Bhuyan
Publisher: Dhiraj Bhuyan
ISBN:
Category : Computers
Languages : en
Pages : 331

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Book Description
“Practical Data Analysis – Using Python & Open Source Technology” uses a case-study based approach to explore some of the real-world applications of open source data analysis tools and techniques. Specifically, the following topics are covered in this book: 1. Open Source Data Analysis Tools and Techniques. 2. A Beginner’s Guide to “Python” for Data Analysis. 3. Implementing Custom Search Engines On The Fly. 4. Visualising Missing Data. 5. Sentiment Analysis and Named Entity Recognition. 6. Automatic Document Classification, Clustering and Summarisation. 7. Fraud Detection Using Machine Learning Techniques. 8. Forecasting - Using Data to Map the Future. 9. Continuous Monitoring and Real-Time Analytics. 10. Creating a Robot for Interacting with Web Applications. Free samples of the book is available at - http://timesofdatascience.com

Author: Hadley Wickham
Publisher: "O'Reilly Media, Inc."
ISBN: 1491910364
Category : Computers
Languages : en
Pages : 521

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Book Description
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Author: Hector Cuesta
Publisher: Packt Publishing Ltd
ISBN: 1785286668
Category : Computers
Languages : en
Pages : 330

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Book Description
A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn Acquire, format, and visualize your data Build an image-similarity search engine Generate meaningful visualizations anyone can understand Get started with analyzing social network graphs Find out how to implement sentiment text analysis Install data analysis tools such as Pandas, MongoDB, and Apache Spark Get to grips with Apache Spark Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.

Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1466672315
Category : Computers
Languages : en
Pages : 2050

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Book Description
The pervasiveness of and universal access to modern Information and Communication Technologies has enabled a popular new paradigm in the dissemination of information, art, and ideas. Now, instead of relying on a finite number of content providers to control the flow of information, users can generate and disseminate their own content for a wider audience. Open Source Technology: Concepts, Methodologies, Tools, and Applications investigates examples and methodologies in user-generated and freely-accessible content available through electronic and online media. With applications in education, government, entertainment, and more, the technologies explored in these volumes will provide a comprehensive reference for web designers, software developers, and practitioners in a wide variety of fields and disciplines.

Author: Nilanjan Dey
Publisher: CRC Press
ISBN: 1498734405
Category : Computers
Languages : en
Pages : 198

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Book Description
Embedded Systems and Robotics with Open-Source Tools provides easy-to-understand and easy-to-implement guidance for rapid prototype development. Designed for readers unfamiliar with advanced computing technologies, this highly accessible book: Describes several cutting-edge open-source software and hardware technologies Examines a number of embedded computer systems and their practical applications Includes detailed projects for applying rapid prototype development skills in real time Embedded Systems and Robotics with Open-Source Tools effectively demonstrates that, with the help of high-performance microprocessors, microcontrollers, and highly optimized algorithms, one can develop smarter embedded devices.

Author: Sneha Kumari
Publisher: Emerald Group Publishing
ISBN: 1800438761
Category : Computers
Languages : en
Pages : 288

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Book Description
Data Science & Business Analytics explores the application of big data and business analytics by academics, researchers, industrial experts, policy makers and practitioners, helping the reader to understand how big data can be efficiently utilized in better managerial applications.