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Data Visualization with JavaScript
by Stephen A. Thomas<P>You’ve got data to communicate. But what kind of visualization do you choose, how do you build your visualizations, and how do you ensure that they're up to the demands of the Web? <P> In Data Visualization with JavaScript, you’ll learn how to use JavaScript, HTML, and CSS to build practical visualizations for your data. Step-by-step examples walk you through creating, integrating, and debugging different types of visualizations and you'll be building basic visualizations (like bar, line, and scatter graphs) in no time. <P>You'll also learn how to: <br>–Create tree maps, heat maps, network graphs, word clouds, and timelines <br>–Map geographic data, and build sparklines and composite charts–Add interactivity and retrieve data with AJAX <br>–Manage data in the browser and build data-driven web applications <br>–Harness the power of the Flotr2, Flot, Chronoline.js, D3.js, Underscore.js, and Backbone.js libraries <P>If you already know your way around building a web page but aren’t quite sure how to build a good visualization, Data Visualization with JavaScript will help you get your feet wet without throwing you into the deep end. You’ll soon be well on your way to creating simple, powerful data visualizations.
Data Visualization with Microsoft Power BI
by Alex Kolokolov Maxim ZelenskyThe sheer volume of business data has reached an all-time high. Using visualizations to transform this data into useful and understandable information can facilitate better decision-making. This practical book shows data analysts as well as professionals in finance, sales, and marketing how to quickly create visualizations and build savvy dashboards.Alex Kolokolov from Data2Speak and Maxim Zelensky from Intelligent Business explain in simple and clear language how to create brilliant charts with Microsoft Power BI and follow best practices for corporate reporting. No technical background is required. Step-by-step guides help you set up any chart in a few clicks and avoid common mistakes. Also, experienced data analysts will find tips and tricks on how to enrich their reports with advanced visuals.This book helps you understand:The basic rules for classic charts that are used in 90% of business reportsExceptions to general rules based on real business casesBest practices for dashboard designHow to properly set up interactionsHow to prepare data for advanced visualsHow to avoid pitfalls with eye-catching charts
Data Visualization with Python: Create an impact with meaningful data insights using interactive and engaging visuals
by Tim GroßmannData Visualization with Python is designed for developers and scientists, who want to get into data science, or want to use data visualizations to enrich their personal and professional projects. You do not need any prior experience in data analytics and visualization, however it’ll help you to have some knowledge of Python and high school level mathematics. Even though this is a beginner level course on data visualization, experienced developers will benefit from improving their Python skills working with real world data.
Data Visualization with Python and JavaScript: Scrape, Clean, Explore & Transform Your Data
by Kyran DaleLearn how to turn raw data into rich, interactive web visualizations with the powerful combination of Python and JavaScript. With this hands-on guide, author Kyran Dale teaches you how build a basic dataviz toolchain with best-of-breed Python and JavaScript libraries--including Scrapy, Matplotlib, Pandas, Flask, and D3--for crafting engaging, browser-based visualizations.As a working example, throughout the book Dale walks you through transforming Wikipedia's table-based list of Nobel Prize winners into an interactive visualization. You'll examine steps along the entire toolchain, from scraping, cleaning, exploring, and delivering data to building the visualization with JavaScript's D3 library. If you're ready to create your own web-based data visualizations--and know either Python or JavaScript-- this is the book for you.Learn how to manipulate data with PythonUnderstand the commonalities between Python and JavaScriptExtract information from websites by using Python's web-scraping tools, BeautifulSoup and ScrapyClean and explore data with Python's Pandas, Matplotlib, and Numpy librariesServe data and create RESTful web APIs with Python's Flask frameworkCreate engaging, interactive web visualizations with JavaScript's D3 library
Data Visualization with Python and JavaScript: Scrape, Clean, Explore And Transform Your Data
by Kyran DaleHow do you turn raw, unprocessed, or malformed data into dynamic, interactive web visualizations? In this practical book, author Kyran Dale shows data scientists and analysts--as well as Python and JavaScript developers--how to create the ideal toolchain for the job. By providing engaging examples and stressing hard-earned best practices, this guide teaches you how to leverage the power of best-of-breed Python and JavaScript libraries.Python provides accessible, powerful, and mature libraries for scraping, cleaning, and processing data. And while JavaScript is the best language when it comes to programming web visualizations, its data processing abilities can't compare with Python's. Together, these two languages are a perfect complement for creating a modern web-visualization toolchain. This book gets you started.You'll learn how to:Obtain data you need programmatically, using scraping tools or web APIs: Requests, Scrapy, Beautiful SoupClean and process data using Python's heavyweight data processing libraries within the NumPy ecosystem: Jupyter notebooks with pandas+Matplotlib+SeabornDeliver the data to a browser with static files or by using Flask, the lightweight Python server, and a RESTful APIPick up enough web development skills (HTML, CSS, JS) to get your visualized data on the webUse the data you've mined and refined to create web charts and visualizations with Plotly, D3, Leaflet, and other libraries
Data Visualization with Python for Beginners: Learn to visualize data from scratch with Python
by AI Sciences OUThis book works as a guide to present fundamental Python libraries and basics related to data visualization using PythonKey FeaturesDetailed introductions to several data visualization libraries such as Matplotlib and SeabornGuided instructions to more advanced data visualization skills such as 3D plotting or interactive visualizationHands-on projects for interactive practice designed to cement your new skills in practical memoryBook DescriptionData science and data visualization are two different but interrelated concepts. Data science refers to the science of extracting and exploring data to find patterns that can be used for decision making at different levels. Data visualization can be considered a subdomain of data science. You visualize data with graphs and tables to find out which data is most significant and help identify meaningful patterns.This book is dedicated to data visualization and explains how to perform data visualization on different datasets using various data visualization libraries written in the Python programming language. It is suggested that you use this book for data visualization purposes only and not for decision making. For decision making and pattern identification, read this book in conjunction with a dedicated book on machine learning and data science.We will start by digging into Python programming as all the projects are developed using it, and it is currently the most used programming language in the world. We will also explore some of the most famous libraries for data visualization, such as Pandas, NumPy, Matplotlib, and Seaborn.You will learn all about Python in three modules—plotting with Matplotlib, plotting with Seaborn, and a final one, Pandas for data visualization. All three modules will contain hands-on projects using real-world datasets and a lot of exercises. By the end of this course, you will have the knowledge and skills required to visualize data with Python all on your own.The code bundle for this course is available at https://www.aispublishing.net/book-data-visualizationWhat you will learnBegin visualizing data with MatplotlibExplore the Python Seaborn library for advanced plottingAnalyze data with the Pandas libraryExpand your visualization skills with PandasPlot in three dimensions with MatplotlibPractice interactive data visualization with Bokeh and PlotlyComplete several hands-on projectsWho this book is forThis book is written with one goal in mind—to help beginners overcome their initial obstacles in learning data visualization using Python. This book aims to isolate the different concepts so that beginners can gradually gain competency in the fundamentals of Python before working on a project. As such, no prior experience is required.
The Data Visualization Workshop: An Interactive Approach to Learning Data Visualization, 2nd Edition
by Tim Großmann Mario DoblerCut through the noise and get real results with a step-by-step approach to learning data visualization with Python Key Features Ideal for Python beginners getting started with data visualization for the first time A step-by-step data visualization tutorial with exercises and activities that help build key skills Structured to let you progress at your own pace, on your own terms Use your physical print copy to redeem free access to the online interactive edition Book Description You already know you want to learn data visualization with Python, and a smarter way to learn is to learn by doing. The Data Visualization Workshop focuses on building up your practical skills so that you can develop clear, expressive real-world charts and diagrams. You'll learn from real examples that lead to real results. Throughout The Data Visualization Workshop, you'll take an engaging step-by-step approach to understand data visualization with Python. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend learning how companies like Uber are using advanced visualization techniques to represent their data visually. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Visualization Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your book. Fast-paced and direct, The Data Visualization Workshop is the ideal companion for Python beginners who want to get up and running with data visualization. You'll visualize your work like a skilled data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead. What you will learn Get to grips with fundamental concepts and conventions of data visualization Learn how to use libraries like NumPy and Pandas to index, slice, and iterate data frames Implement different plotting techniques to produce compelling data visualizations Learn how you can skyrocket your Python data wrangling skills Draw statistical graphics using the Seaborn and Matplotlib libraries Create interactive visualizations and integrate them into any web page Who this book is for Our goal at Packt is to help you be successful, in whatever it is that you choose to do. The Data Visualization Workshop is an ideal tutorial for those who want to perform data visualization with Python and are just getting started. Pick up a Workshop today and let Packt help you develop skills that stick with you for life.
The Data Visualization Workshop: A self-paced, practical approach to transforming your complex data into compelling, captivating graphics
by Tim Großmann Mario DoblerExplore a modern approach to visualizing data with Python and transform large real-world datasets into expressive visual graphics using this beginner-friendly workshop Key Features Discover the essential tools and methods of data visualization Learn to use standard Python plotting libraries such as Matplotlib and Seaborn Gain insights into the visualization techniques of big companies Book Description Do you want to transform data into captivating images? Do you want to make it easy for your audience to process and understand the patterns, trends, and relationships hidden within your data? The Data Visualization Workshop will guide you through the world of data visualization and help you to unlock simple secrets for transforming data into meaningful visuals with the help of exciting exercises and activities. Starting with an introduction to data visualization, this book shows you how to first prepare raw data for visualization using NumPy and pandas operations. As you progress, you'll use plotting techniques, such as comparison and distribution, to identify relationships and similarities between datasets. You'll then work through practical exercises to simplify the process of creating visualizations using Python plotting libraries such as Matplotlib and Seaborn. If you've ever wondered how popular companies like Uber and Airbnb use geoplotlib for geographical visualizations, this book has got you covered, helping you analyze and understand the process effectively. Finally, you'll use the Bokeh library to create dynamic visualizations that can be integrated into any web page. By the end of this workshop, you'll have learned how to present engaging mission-critical insights by creating impactful visualizations with real-world data. What you will learn Understand the importance of data visualization in data science Implement NumPy and pandas operations on real-life datasets Create captivating data visualizations using plotting libraries Use advanced techniques to plot geospatial data on a map Integrate interactive visualizations to a webpage Visualize stock prices with Bokeh and analyze Airbnb data with Matplotlib Who this book is for The Data Visualization Workshop is for beginners who want to learn data visualization, as well as developers and data scientists who are looking to enrich their practical data science skills. Prior knowledge of data analytics, data science, and visualization is not mandatory. Knowledge of Python basics and high-school-level math will help you grasp the concepts covered in this data visualization book more quickly and effectively.
Data Warehouse Designs: Achieving ROI with Market Basket Analysis and Time Variance
by Fon SilversMarket Basket Analysis (MBA) provides the ability to continually monitor the affinities of a business and can help an organization achieve a key competitive advantage. Time Variant data enables data warehouses to directly associate events in the past with the participants in each individual event. In the past however, the use of these powerful tools in tandem led to performance degradation and resulted in unactionable and even damaging information. Data Warehouse Designs: Achieving ROI with Market Basket Analysis and Time Variance presents an innovative, soup-to-nuts approach that successfully combines what was previously incompatible, without degradation, and uses the relational architecture already in place. Built around two main chapters, Market Basket Solution Definition and Time Variant Solution Definition, it provides a tangible how-to design that can be used to facilitate MBA within the context of a data warehouse. Presents a solution for creating home-grown MBA data marts Includes database design solutions in the context of Oracle, DB2, SQL Server, and Teradata relational database management systems (RDBMS) Explains how to extract, transform, and load data used in MBA and Time Variant solutions The book uses standard RDBMS platforms, proven database structures, standard SQL and hardware, and software and practices already accepted and used in the data warehousing community to fill the gaps left by most conceptual discussions of MBA. It employs a form and language intended for a data warehousing audience to explain the practicality of how data is delivered, stored, and viewed. Offering a comprehensive explanation of the applications that provide, store, and use MBA data, Data Warehouse Designs provides you with the language and concepts needed to require and receive information that is relevant and actionable.
The Data Warehouse Lifecycle Toolkit
by Bob Becker Joy Mundy Warren Thornthwaite Ralph Kimball Margy RossA thorough update to the industry standard for designing, developing, and deploying data warehouse and business intelligence systemsThe world of data warehousing has changed remarkably since the first edition of The Data Warehouse Lifecycle Toolkit was published in 1998. In that time, the data warehouse industry has reached full maturity and acceptance, hardware and software have made staggering advances, and the techniques promoted in the premiere edition of this book have been adopted by nearly all data warehouse vendors and practitioners. In addition, the term "business intelligence" emerged to reflect the mission of the data warehouse: wrangling the data out of source systems, cleaning it, and delivering it to add value to the business.Ralph Kimball and his colleagues have refined the original set of Lifecycle methods and techniques based on their consulting and training experience. The authors understand first-hand that a data warehousing/business intelligence (DW/BI) system needs to change as fast as its surrounding organization evolves. To that end, they walk you through the detailed steps of designing, developing, and deploying a DW/BI system. You'll learn to create adaptable systems that deliver data and analyses to business users so they can make better business decisions.
Data Warehouse Requirements Engineering: A Decision Based Approach
by Naveen Prakash Deepika PrakashAs the first to focus on the issue of Data Warehouse Requirements Engineering, this book introduces a model-driven requirements process used to identify requirements granules and incrementally develop data warehouse fragments. In addition, it presents an approach to the pair-wise integration of requirements granules for consolidating multiple data warehouse fragments. The process is systematic and does away with the fuzziness associated with existing techniques. Thus, consolidation is treated as a requirements engineering issue. The notion of a decision occupies a central position in the decision-based approach. On one hand, information relevant to a decision must be elicited from stakeholders; modeled; and transformed into multi-dimensional form. On the other, decisions themselves are to be obtained from decision applications. For the former, the authors introduce a suite of information elicitation techniques specific to data warehousing. This information is subsequently converted into multi-dimensional form. For the latter, not only are decisions obtained from decision applications for managing operational businesses, but also from applications for formulating business policies and for defining rules for enforcing policies, respectively. In this context, the book presents a broad range of models, tools and techniques. For readers from academia, the book identifies the scientific/technological problems it addresses and provides cogent arguments for the proposed solutions; for readers from industry, it presents an approach for ensuring that the product meets its requirements while ensuring low lead times in delivery.
Data-Warehouse-Systeme für Dummies (Für Dummies)
by Wolfgang GerkenJede Business-Intelligence-Anwendung beruht letzten Endes auf einem Data Warehouse. Data Warehousing ist deshalb ein sehr wichtiges Gebiet der Angewandten Informatik, insbesondere im Zeitalter von Big Data. Das vorliegende Buch beleuchtet das Data Warehouse aus zwei Perspektiven: der des Entwicklers und der des Anwenders. Der zukünftige Entwickler lernt, ein Data Warehouse mit geeigneten Methoden selbst zu entwickeln. Für den zukünftigen Anwender geht der Autor auf die Themen Reporting, Online Analytical Processing und Data Mining ein. Das Lehrbuch ist auch zum Selbststudium geeignet. Kenntnisse über Datenbanksysteme sollten allerdings vorhanden sein.
Data-Warehouse-Systeme kompakt: Aufbau, Architektur, Grundfunktionen (Xpert.press)
by Kiumars FarkischIn dem Buch werden Data-Warehouse-Systeme als einheitliche, zentrale, vollständige, historisierte und analytische IT-Plattform untersucht und ihre Rolle für die Datenanalyse und für Entscheidungsfindungsprozesse dargestellt. Dabei behandelt der Autor die einzelnen Komponenten, die für den Aufbau, die Architektur und den Betrieb eines Data-Warehouse-Systems von Bedeutung sind. Die multidimensionale Datenmodellierung, der ETL-Prozess und Analysemethoden werden erörtert und Maßnahmen zur Performancesteigerung von Data-Warehouse-Systemen diskutiert.
The Data Warehouse Toolkit
by Margy Ross Ralph KimballThe latest edition of the single most authoritative guide on dimensional modeling for data warehousing! Dimensional modeling has become the most widely accepted approach for data warehouse design. Here is a complete library of dimensional modeling techniques-- the most comprehensive collection ever written. Greatly expanded to cover both basic and advanced techniques for optimizing data warehouse design, this second edition to Ralph Kimball's classic guide is more than sixty percent updated. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: * Retail sales and e-commerce * Inventory management * Procurement * Order management * Customer relationship management (CRM) * Human resources management * Accounting * Financial services * Telecommunications and utilities * Education * Transportation * Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts. This book is also available as part of the Kimball's Data Warehouse Toolkit Classics Box Set (ISBN: 9780470479575) with the following 3 books: The Data Warehouse Toolkit, 2nd Edition (9780471200246) The Data Warehouse Lifecycle Toolkit, 2nd Edition (9780470149775) The Data Warehouse ETL Toolkit (9780764567575)
The Data WarehouseETL Toolkit
by Joe Caserta Ralph KimballCowritten by Ralph Kimball, the world's leading data warehousing authority, whose previous books have sold more than 150,000 copiesDelivers real-world solutions for the most time- and labor-intensive portion of data warehousing-data staging, or the extract, transform, load (ETL) processDelineates best practices for extracting data from scattered sources, removing redundant and inaccurate data, transforming the remaining data into correctly formatted data structures, and then loading the end product into the data warehouseOffers proven time-saving ETL techniques, comprehensive guidance on building dimensional structures, and crucial advice on ensuring data quality
Data Warehousing and Analytics: Fueling the Data Engine (Data-Centric Systems and Applications)
by David Taniar Wenny RahayuThis textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge.The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics).This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.
Data Warehousing for Biomedical Informatics
by Richard E. BiehlData Warehousing for Biomedical Informatics is a step-by-step how-to guide for designing and building an enterprise-wide data warehouse across a biomedical or healthcare institution, using a four-iteration lifecycle and standardized design pattern. It enables you to quickly implement a fully-scalable generic data architecture that supports your organization's clinical, operational, administrative, financial, and research data. By following the guidelines in this book, you will be able to successfully progress through the Alpha, Beta, and Gamma versions, plus fully implement your first production release in about a year.
Data Warehousing For Dummies
by Thomas C. HammergrenData warehousing is one of the hottest business topics, and there's more to understanding data warehousing technologies than you might think. Find out the basics of data warehousing and how it facilitates data mining and business intelligence with Data Warehousing For Dummies, 2nd Edition.Data is probably your company's most important asset, so your data warehouse should serve your needs. The fully updated Second Edition of Data Warehousing For Dummies helps you understand, develop, implement, and use data warehouses, and offers a sneak peek into their future. You'll learn to:Analyze top-down and bottom-up data warehouse designsUnderstand the structure and technologies of data warehouses, operational data stores, and data martsChoose your project team and apply best development practices to your data warehousing projectsImplement a data warehouse, step by step, and involve end-users in the processReview and upgrade existing data storage to make it serve your needsComprehend OLAP, column-wise databases, hardware assisted databases, and middlewareUse data mining intelligently and find what you needMake informed choices about consultants and data warehousing productsData Warehousing For Dummies, 2nd Edition also shows you how to involve users in the testing process and gain valuable feedback, what it takes to successfully manage a data warehouse project, and how to tell if your project is on track. You'll find it's the most useful source of data on the topic!
Data Warehousing Fundamentals for IT Professionals
by Paulraj PonniahCutting-edge content and guidance from a data warehousing expert--now expanded to reflect field trendsData 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 Warehousing with SAP BW7 BI in SAP Netweaver 2004s
by Christian Mehrwald Sabine MorlockBI in SAP NetWeaver 2004s is the official abbreviation for the successor of the Business Information Warehouse (BW) which has been completely revised by SAP with its latest release. Core elements of this comprehensive suite for decision making applications are functions for extraction, transformation and data management. With this new release, these functions aim more heavily at company-wide data warehousing. The book focuses on these core tasks of SAP BW and gives well-founded insights into the system architecture. As practical handbook and well-structured reference book, the book is for SAP consultants and IT staff that are responsible for or planning a BW-based data warehouse implementation. Apart from system architecture, the book focuses on detailed descriptions of data management (data models and Analytical Engine) as well as the Staging Engine which have been completely revised and deal with new data transfer process technology. The design of the controlled operations has been substantially expanded and besides a comprehensive description of automization techniques by using process chains, regular maintenance and administration tasks are also discussed (model trimming, technical validation). The book emphasizes a comprehensive view on aspects to manageability and system performance which are discussed in individual chapters but also implicitly in all other ranges of topics.
Data Wise, Revised and Expanded Edition: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning
by Kathryn Parker BoudettData Wise, Revised and Expanded Edition presents a continuous, sustainable process that allows school leaders to harness classroom metrics to inform educational practice.At its core, the Data Wise method fosters effective collaboration among educators, enabling teams to study a wide range of evidence and then use what they learn to enrich school culture and climate and ensure that each student thrives.Kathryn Parker Boudett, Elizabeth A. City, and Richard J. Murnane offer clear guidance for enacting all stages of the Data Wise improvement process and for integrating data inquiry into long-term institutional practice. They begin with actions that lay the groundwork for collaboration: advancing assessment literacy among contributors, building productive professional learning communities, and identifying targets for change. They continue with advice on evaluating progress and boosting accountability.Throughout the book, the authors recommend practical tools and proven practices, such as the plus/delta protocol and the ACE Habits of Mind (focusing on action, collaboration, and evidence), that help school leaders optimize the quality of meetings, especially those in which educators analyze data. They also provide tips for how to make best use of developments in education and technology, from Common Core State Standards to online collaboration tools.The field-tested strategies of the Data Wise improvement process have been used to great success in schools around the world, showing that careful examination of test scores, classroom data, and other educational evaluations can become a catalyst for important schoolwide conversations and transformations.
Data Wrangling with JavaScript
by Ashley DavisSummaryData Wrangling with JavaScript is hands-on guide that will teach you how to create a JavaScript-based data processing pipeline, handle common and exotic data, and master practical troubleshooting strategies.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyWhy not handle your data analysis in JavaScript? Modern libraries and data handling techniques mean you can collect, clean, process, store, visualize, and present web application data while enjoying the efficiency of a single-language pipeline and data-centric web applications that stay in JavaScript end to end.About the BookData Wrangling with JavaScript promotes JavaScript to the center of the data analysis stage! With this hands-on guide, you'll create a JavaScript-based data processing pipeline, handle common and exotic data, and master practical troubleshooting strategies. You'll also build interactive visualizations and deploy your apps to production. Each valuable chapter provides a new component for your reusable data wrangling toolkit.What's insideEstablishing a data pipelineAcquisition, storage, and retrievalHandling unusual data setsCleaning and preparing raw dataInteractive visualizations with D3About the ReaderWritten for intermediate JavaScript developers. No data analysis experience required.About the AuthorAshley Davis is a software developer, entrepreneur, author, and the creator of Data-Forge and Data-Forge Notebook, software for data transformation, analysis, and visualization in JavaScript.Table of ContentsGetting started: establishing your data pipelineGetting started with Node.jsAcquisition, storage, and retrievalWorking with unusual dataExploratory codingClean and prepareDealing with huge data filesWorking with a mountain of dataPractical data analysisBrowser-based visualizationServer-side visualizationLive dataAdvanced visualization with D3Getting to production
Data Wrangling with Python
by Katharine Jarmul Jacqueline KazilHow do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that's initially too messy or difficult to access. You don't need to know a thing about the Python programming language to get started.Through various step-by-step exercises, you'll learn how to acquire, clean, analyze, and present data efficiently. You'll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain.Quickly learn basic Python syntax, data types, and language conceptsWork with both machine-readable and human-consumable dataScrape websites and APIs to find a bounty of useful informationClean and format data to eliminate duplicates and errors in your datasetsLearn when to standardize data and when to test and script data cleanupExplore and analyze your datasets with new Python libraries and techniquesUse Python solutions to automate your entire data-wrangling process
Data Wrangling with Python: Creating actionable data from raw sources
by Tirthajyoti Sarkar Shubhadeep RoychowdhurySoftware professionals, web developers, database engineers, and business analysts who want to movetowards a career of full-fledged data scientist/analytics expert or whoever wants to use data analytics/machine learning to enrich their current personal or professional projects.Prior experience with Python is not an absolute requirement, however the knowledge of at least oneobject-oriented programming language (e.g. C/C++/Java/JavaScript), and high school level math is highlypreferred. It is a bonus if you have rudimentary idea about relational database and SQL.Even seasoned Python app/web developers can benefit from this book as it focuses on data engineering aspects
Data Wrangling with R
by Bradley C. BoehmkeThis guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques.This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author's goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned: How to work with different types of data such as numerics, characters, regular expressions, factors, and datesThe difference between different data structures and how to create, add additional components to, and subset each data structureHow to acquire and parse data from locations previously inaccessibleHow to develop functions and use loop control structures to reduce code redundancyHow to use pipe operators to simplify code and make it more readableHow to reshape the layout of data and manipulate, summarize, and join data sets