Search results for: fundamentals-of-data-visualization

Fundamentals of Data Visualization

Author : Claus O. Wilke
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Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color as a tool to highlight, distinguish, or represent a value Understand the importance of redundant coding to ensure you provide key information in multiple ways Use the book’s visualizations directory, a graphical guide to commonly used types of data visualizations Get extensive examples of good and bad figures Learn how to use figures in a document or report and how employ them effectively to tell a compelling story

Exam Prep for Fundamentals of Data Visualization

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Foundations of Data Visualization

Author : Min Chen
File Size : 53.36 MB
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Hands on Fundamentals of Data Science with Go

Author : Sanket Gupta
File Size : 46.10 MB
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"Go (also known as Golang), created at Google, is increasingly proving to be faster, easy to code in, highly efficient and concurrent programming languages. It is the next-gen language of data science, machine learning and AI in general - as it strikes a great balance between productivity and maintainability of code. Many data scientists prototype models, which are then deployed to production by someone else, Go will allow you to do both! In these videos, you will get complete hands-on guidance on how to perform data mining, natural language processing, machine learning, linear algebra and understand in detail how you can use these to boost data science projects in your teams using Go. You will gain practical coverage on how to do data collection, data cleaning and mining, use of statistical models for analysis and data visualization. You will also get to use cutting-edge libraries in Go, and use them with popular datasets used by the machine learning community. The course would also guides you to build real-life hands on projects like twitter bot which tweets on your behalf, sentiment analysis on movie reviews using Naive Bayes and decision trees, two different kinds of recommendation systems to recommend movies and a regression model to perform stock prices forecasting, along with performing your own data visualizations in Go. You will get a complete hold on the use of statistics, linear algebra and understand in detail how you can boost your data science using Go. You will gain practical coverage on how to do data collection, data sanitation and munging, the use of statistical models for analysis and data visualization. The video would also get you couple with the fundamentals of machine learning along with a quick run through in implementing models such as Decision Trees, Naive Bayes, SVM and so on. You will also get to know how you can work with big data processing tools like Apache Spark and Kafka in your data science projects. The course would also get you through a couple of examples like recommendation system, sentimental analysis and stock prices forecasting."--Resource description page.

Fundamentals of Data Science with MATLAB

Author : Arash Karimpour
File Size : 26.64 MB
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Fundamentals of Data Mining in Genomics and Proteomics

Author : Werner Dubitzky
File Size : 70.96 MB
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This book presents state-of-the-art analytical methods from statistics and data mining for the analysis of high-throughput data from genomics and proteomics. It adopts an approach focusing on concepts and applications and presents key analytical techniques for the analysis of genomics and proteomics data by detailing their underlying principles, merits and limitations.

Data Literacy

Author : David Herzog
File Size : 26.9 MB
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A practical, skill-based introduction to data analysis and literacy We are swimming in a world of data, and this handy guide will keep you afloat while you learn to make sense of it all. In Data Literacy: A User's Guide, David Herzog, a journalist with a decade of experience using data analysis to transform information into captivating storytelling, introduces students and professionals to the fundamentals of data literacy, a key skill in today’s world. Assuming the reader has no advanced knowledge of data analysis or statistics, this book shows how to create insight from publicly-available data through exercises using simple Excel functions. Extensively illustrated, step-by-step instructions within a concise, yet comprehensive, reference will help readers identify, obtain, evaluate, clean, analyze and visualize data. A concluding chapter introduces more sophisticated data analysis methods and tools including database managers such as Microsoft Access and MySQL and standalone statistical programs such as SPSS, SAS and R.

Data Warehousing Fundamentals for IT Professionals

Author : Paulraj Ponniah
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CUTTING-EDGE CONTENT AND GUIDANCE FROM A DATA WAREHOUSING EXPERT—NOW EXPANDED TO REFLECT FIELD TRENDS Data warehousing has revolutionized the way businesses in a wide variety of industries perform analysis and make strategic decisions. Since the first edition of Data Warehousing Fundamentals, numerous enterprises have implemented data warehouse systems and reaped enormous benefits. Many more are in the process of doing so. Now, this new, revised edition covers the essential fundamentals of data warehousing and business intelligence as well as significant recent trends in the field. The author provides an enhanced, comprehensive overview of data warehousing together with in-depth explanations of critical issues in planning, design, deployment, and ongoing maintenance. IT professionals eager to get into the field will gain a clear understanding of techniques for data extraction from source systems, data cleansing, data transformations, data warehouse architecture and infrastructure, and the various methods for information delivery. This practical Second Edition highlights the areas of data warehousing and business intelligence where high-impact technological progress has been made. Discussions on developments include data marts, real-time information delivery, data visualization, requirements gathering methods, multi-tier architecture, OLAP applications, Web clickstream analysis, data warehouse appliances, and data mining techniques. The book also contains review questions and exercises for each chapter, appropriate for self-study or classroom work, industry examples of real-world situations, and several appendices with valuable information. Specifically written for professionals responsible for designing, implementing, or maintaining data warehousing systems, Data Warehousing Fundamentals presents agile, thorough, and systematic development principles for the IT professional and anyone working or researching in information management.

Data Visualization Fundamentals

Author : Eleanor Johnson
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"Data visualization is the process of taking some of the data, the numbers produced by our ever-computing technical world, and creating pictures from them to make sense of them. The pictures show the patterns, the trends, the relationships as visual elements so we can see them. The video describes the basic methods to create simple charts, some of the rules and caveats, and how these can also be made even more friendly and accessible to a non-technical audience. Making sure the data is useful and truthful, ensures the visualizations are useful and truthful. The talk covers the most basic charts and how they are created, and their uses. It covers some of the mistakes which are easily made, and the most useful elements to be included -- such as color and icons. It describes the process to follow and models which describe the most useful aspects of a successful graphic."--Resource description page.

Storytelling with Data

Author : Cole Nussbaumer Knaflic
File Size : 47.10 MB
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Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it!

Mathematical Foundations of Scientific Visualization Computer Graphics and Massive Data Exploration

Author : Torsten Möller
File Size : 70.56 MB
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The goal of visualization is the accurate, interactive, and intuitive presentation of data. Complex numerical simulations, high-resolution imaging devices and incre- ingly common environment-embedded sensors are the primary generators of m- sive data sets. Being able to derive scienti?c insight from data increasingly depends on having mathematical and perceptual models to provide the necessary foundation for effective data analysis and comprehension. The peer-reviewed state-of-the-art research papers included in this book focus on continuous data models, such as is common in medical imaging or computational modeling. From the viewpoint of a visualization scientist, we typically collaborate with an application scientist or engineer who needs to visually explore or study an object which is given by a set of sample points, which originally may or may not have been connected by a mesh. At some point, one generally employs low-order piecewise polynomial approximationsof an object, using one or several dependent functions. In order to have an understanding of a higher-dimensional geometrical “object” or function, ef?cient algorithms supporting real-time analysis and manipulation (- tation, zooming) are needed. Often, the data represents 3D or even time-varying 3D phenomena (such as medical data), and the access to different layers (slices) and structures (the underlying topology) comprising such data is needed.

Effective Data Visualization

Author : Jeffrey Michael Heer
File Size : 27.83 MB
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"Learn the methods you need to bring data to life through effective visualizations. In this video course, host Jeffrey Heer--co-founder of Trifacta--takes you through best practices for designing interactive visualizations, performing exploratory data analysis, and examining multidimensional data. You'll begin by learning the value of visualization, through design principles drawn from graphic design, visual art, perceptual psychology, and cognitive science. Using Trifacta's data transformation tools to illustrate some of the concepts, you'll also learn techniques for scaling visualizations to extremely large data sets."--Resource description page.

Data Science Fundamentals and Practical Approaches

Author : Dr. Gypsy Nandi
File Size : 42.22 MB
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Learn how to process and analysis data using Python KEY FEATURES - The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. - The book is not just dealing with the background mathematics alone or only the programs but beautifully correlates the background mathematics to the theory and then finally translating it into the programs. - A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. DESCRIPTION This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems. Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic. WHAT WILL YOU LEARN Perform processing on data for making it ready for visual plot and understand the pattern in data over time. Understand what machine learning is and how learning can be incorporated into a program. Know how tools can be used to perform analysis on big data using python and other standard tools. Perform social media analytics, business analytics, and data analytics on any data of a company or organization. WHO THIS BOOK IS FOR The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. TABLE OF CONTENTS 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics

Mastering Python for Data Science

Author : Samir Madhavan
File Size : 36.52 MB
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Explore the world of data science through Python and learn how to make sense of data About This Book Master data science methods using Python and its libraries Create data visualizations and mine for patterns Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning Who This Book Is For If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed. What You Will Learn Manage data and perform linear algebra in Python Derive inferences from the analysis by performing inferential statistics Solve data science problems in Python Create high-end visualizations using Python Evaluate and apply the linear regression technique to estimate the relationships among variables. Build recommendation engines with the various collaborative filtering algorithms Apply the ensemble methods to improve your predictions Work with big data technologies to handle data at scale In Detail Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science. Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods. Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics. Style and approach This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.

Fundamentals of Three dimensional Digital Image Processing

Author : Junichiro Toriwaki
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This book is a detailed description of the basics of three-dimensional digital image processing. A 3D digital image (abbreviated as “3D image” below) is a digitalized representation of a 3D object or an entire 3D space, stored in a computer as a 3D array. Whereas normal digital image processing is concerned with screens that are a collection of square shapes called “pixels” and their corresponding density levels, the “image plane” in three dimensions is represented by a division into cubical graphical elements (called “voxels”) that represent corresponding density levels. Inthecontextofimageprocessing,in manycases3Dimageprocessingwill refer to the input of multiple 2D images and performing processing in order to understand the 3D space (or “scene”) that they depict. This is a result of research into how to use input from image sensors such as television cameras as a basis for learning about a 3D scene, thereby replicating the sense of vision for humans or intelligent robots, and this has been the central problem in image processing research since the 1970s. However, a completely di?erent type of image with its own new problems, the 3D digital image discussed in this book, rapidly took prominence in the 1980s, particularly in the ?eld of medical imaging. These were recordings of human bodies obtained through computed (or “computerized”) tomography (CT),imagesthatrecordednotonlytheexternal,visiblesurfaceofthesubject but also, to some degree of resolution, its internal structure. This was a type of image that no one had experienced before.

Fraud Analytics

Author : Delena D. Spann
File Size : 86.1 MB
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Proven guidance for expertly using analytics in fraud examinations, financial analysis, auditing and fraud prevention Fraud Analytics thoroughly reveals the elements of analysis that are used in today's fraud examinations, fraud investigations, and financial crime investigations. This valuable resource reviews the types of analysis that should be considered prior to beginning an investigation and explains how to optimally use data mining techniques to detect fraud. Packed with examples and sample cases illustrating pertinent concepts in practice, this book also explores the two major data analytics providers: ACL and IDEA. Looks at elements of analysis used in today's fraud examinations Reveals how to use data mining (fraud analytic) techniques to detect fraud Examines ACL and IDEA as indispensable tools for fraud detection Includes an abundance of sample cases and examples Written by Delena D Spann, Board of Regent (Emeritus) for the Association of Certified Fraud Examiners (ACFE), who currently serves as Advisory Board Member of the Association of Certified Fraud Examiners, Board Member of the Education Task Force of the Association of Certified Anti-Money Laundering Specialists ASIS International (Economic Crime Council) and Advisory Board Member of the Robert Morris University (School of Business), Fraud Analytics equips you with authoritative fraud analysis techniques you can put to use right away.

e Business Fundamentals

Author : Peter Eckersley
File Size : 63.89 MB
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This comprehensive textbook considers all of the key business, management and technical issues of e-Business, examining and explaining how technologies can help organizations in both the public and private sectors conduct business in new ways.After addressing the changing nature of the e-Economy and the impact of the dot.com 'bubble' of the late 19

Interactive Data Visualization for the Web

Author : Scott Murray
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Create and publish your own interactive data visualization projects on the Web—even if you have little or no experience with data visualization or web development. It’s easy and fun with this practical, hands-on introduction. Author Scott Murray teaches you the fundamental concepts and methods of D3, a JavaScript library that lets you express data visually in a web browser. Along the way, you’ll expand your web programming skills, using tools such as HTML and JavaScript. This step-by-step guide is ideal whether you’re a designer or visual artist with no programming experience, a reporter exploring the new frontier of data journalism, or anyone who wants to visualize and share data. Learn HTML, CSS, JavaScript, and SVG basics Dynamically generate web page elements from your data—and choose visual encoding rules to style them Create bar charts, scatter plots, pie charts, stacked bar charts, and force-directed layouts Use smooth, animated transitions to show changes in your data Introduce interactivity to help users explore data through different views Create customized geographic maps with data Explore hands-on with downloadable code and over 100 examples

Data Visualization Made Simple

Author : Kristen Sosulski
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Data Visualization Made Simple is a practical guide to the fundamentals, strategies, and real-world cases for data visualization, an essential skill required in today’s information-rich world. With foundations rooted in statistics, psychology, and computer science, data visualization offers practitioners in almost every field a coherent way to share findings from original research, big data, learning analytics, and more. In nine appealing chapters, the book: examines the role of data graphics in decision-making, sharing information, sparking discussions, and inspiring future research; scrutinizes data graphics, deliberates on the messages they convey, and looks at options for design visualization; and includes cases and interviews to provide a contemporary view of how data graphics are used by professionals across industries Both novices and seasoned designers in education, business, and other areas can use this book’s effective, linear process to develop data visualization literacy and promote exploratory, inquiry-based approaches to visualization problems.

Big Data Fundamentals

Author : Thomas Erl
File Size : 58.77 MB
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“This text should be required reading for everyone in contemporary business.” --Peter Woodhull, CEO, Modus21 “The one book that clearly describes and links Big Data concepts to business utility.” --Dr. Christopher Starr, PhD “Simply, this is the best Big Data book on the market!” --Sam Rostam, Cascadian IT Group “...one of the most contemporary approaches I’ve seen to Big Data fundamentals...” --Joshua M. Davis, PhD The Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages. Discovering Big Data’s fundamental concepts and what makes it different from previous forms of data analysis and data science Understanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovation Planning strategic, business-driven Big Data initiatives Addressing considerations such as data management, governance, and security Recognizing the 5 “V” characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and value Clarifying Big Data’s relationships with OLTP, OLAP, ETL, data warehouses, and data marts Working with Big Data in structured, unstructured, semi-structured, and metadata formats Increasing value by integrating Big Data resources with corporate performance monitoring Understanding how Big Data leverages distributed and parallel processing Using NoSQL and other technologies to meet Big Data’s distinct data processing requirements Leveraging statistical approaches of quantitative and qualitative analysis Applying computational analysis methods, including machine learning