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| data.table cheat sheet: R For Dummies Andrie de Vries, Joris Meys, 2012-06-06 Master the programming language of choice among statisticians and data analysts worldwide Coming to grips with R can be tough, even for seasoned statisticians and data analysts. Enter R For Dummies, the quick, easy way to master all the R you'll ever need. Requiring no prior programming experience and packed with practical examples, easy, step-by-step exercises, and sample code, this extremely accessible guide is the ideal introduction to R for complete beginners. It also covers many concepts that intermediate-level programmers will find extremely useful. Master your R ABCs ? get up to speed in no time with the basics, from installing and configuring R to writing simple scripts and performing simultaneous calculations on many variables Put data in its place ? get to know your way around lists, data frames, and other R data structures while learning to interact with other programs, such as Microsoft Excel Make data dance to your tune ? learn how to reshape and manipulate data, merge data sets, split and combine data, perform calculations on vectors and arrays, and much more Visualize it ? learn to use R's powerful data visualization features to create beautiful and informative graphical presentations of your data Get statistical ? find out how to do simple statistical analysis, summarize your variables, and conduct classic statistical tests, such as t-tests Expand and customize R ? get the lowdown on how to find, install, and make the most of add-on packages created by the global R community for a wide variety of purposes Open the book and find: Help downloading, installing, and configuring R Tips for getting data in and out of R Ways to use data frames and lists to organize data How to manipulate and process data Advice on fitting regression models and ANOVA Helpful hints for working with graphics How to code in R What R mailing lists and forums can do for you |
| data.table cheat sheet: Practical Data Science with R, Second Edition John Mount, Nina Zumel, 2019-11-17 Summary Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever expanding field of data science. You’ll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. About the technology Evidence-based decisions are crucial to success. Applying the right data analysis techniques to your carefully curated business data helps you make accurate predictions, identify trends, and spot trouble in advance. The R data analysis platform provides the tools you need to tackle day-to-day data analysis and machine learning tasks efficiently and effectively. About the book Practical Data Science with R, Second Edition is a task-based tutorial that leads readers through dozens of useful, data analysis practices using the R language. By concentrating on the most important tasks you’ll face on the job, this friendly guide is comfortable both for business analysts and data scientists. Because data is only useful if it can be understood, you’ll also find fantastic tips for organizing and presenting data in tables, as well as snappy visualizations. What's inside Statistical analysis for business pros Effective data presentation The most useful R tools Interpreting complicated predictive models About the reader You’ll need to be comfortable with basic statistics and have an introductory knowledge of R or another high-level programming language. About the author Nina Zumel and John Mount founded a San Francisco–based data science consulting firm. Both hold PhDs from Carnegie Mellon University and blog on statistics, probability, and computer science. |
| data.table cheat sheet: Practical R for Mass Communication and Journalism Sharon Machlis, 2018-12-21 Do you want to use R to tell stories? This book was written for you—whether you already know some R or have never coded before. Most R texts focus only on programming or statistical theory. Practical R for Mass Communication and Journalism gives you ideas, tools, and techniques for incorporating data and visualizations into your narratives. You’ll see step by step how to: Analyze airport flight delays, restaurant inspections, and election results Map bank locations, median incomes, and new voting districts Compare campaign contributions to final election results Extract data from PDFs Whip messy data into shape for analysis Scrape data from a website Create graphics ranging from simple, static charts to interactive visualizations for the Web If you work or plan to work in a newsroom, government office, non-profit policy organization, or PR office, Practical R for Mass Communication and Journalism will help you use R in your world. This book has a companion website with code, links to additional resources, and searchable tables by function and task. Sharon Machlis is the author of Computerworld’s Beginner’s Guide to R, host of InfoWorld’s Do More With R video screencast series, admin for the R for Journalists Google Group, and is well known among Twitter users who follow the #rstats hashtag. She is Director of Editorial Data and Analytics at IDG Communications (parent company of Computerworld, InfoWorld, PC World and Macworld, among others) and a frequent speaker at data journalism and R conferences. |
| data.table cheat sheet: Learning R Programming Kun Ren, 2016-10-28 Become an efficient data scientist with R About This Book Explore the R language from basic types and data structures to advanced topics Learn how to tackle programming problems and explore both functional and object-oriented programming techniques Learn how to address the core problems of programming in R and leverage the most popular packages for common tasks Who This Book Is For This is the perfect tutorial for anyone who is new to statistical programming and modeling. Anyone with basic programming and data processing skills can pick this book up to systematically learn the R programming language and crucial techniques. What You Will Learn Explore the basic functions in R and familiarize yourself with common data structures Work with data in R using basic functions of statistics, data mining, data visualization, root solving, and optimization Get acquainted with R's evaluation model with environments and meta-programming techniques with symbol, call, formula, and expression Get to grips with object-oriented programming in R: including the S3, S4, RC, and R6 systems Access relational databases such as SQLite and non-relational databases such as MongoDB and Redis Get to know high performance computing techniques such as parallel computing and Rcpp Use web scraping techniques to extract information Create RMarkdown, an interactive app with Shiny, DiagramR, interactive charts, ggvis, and more In Detail R is a high-level functional language and one of the must-know tools for data science and statistics. Powerful but complex, R can be challenging for beginners and those unfamiliar with its unique behaviors. Learning R Programming is the solution - an easy and practical way to learn R and develop a broad and consistent understanding of the language. Through hands-on examples you'll discover powerful R tools, and R best practices that will give you a deeper understanding of working with data. You'll get to grips with R's data structures and data processing techniques, as well as the most popular R packages to boost your productivity from the offset. Start with the basics of R, then dive deep into the programming techniques and paradigms to make your R code excel. Advance quickly to a deeper understanding of R's behavior as you learn common tasks including data analysis, databases, web scraping, high performance computing, and writing documents. By the end of the book, you'll be a confident R programmer adept at solving problems with the right techniques. Style and approach Developed to make learning easy and intuitive, this book comes packed with a wide variety of statistical and graphical techniques and a wealth of practical information for anyone looking to get started with this exciting and powerful language. |
| data.table cheat sheet: Excel Data Analysis For Dummies Paul McFedries, 2018-10-30 Take Excel to the next level Excel is the world’s leading spreadsheet application. It’s a key module in Microsoft Office—the number-one productivity suite—and it is the number-one business intelligence tool. An Excel dashboard report is a visual presentation of critical data and uses gauges, maps, charts, sliders, and other graphical elements to present complex data in an easy-to-understand format. Excel Data Analysis For Dummies explains in depth how to use Excel as a tool for analyzing big data sets. In no time, you’ll discover how to mine and analyze critical data in order to make more informed business decisions. Work with external databases, PivotTables, and Pivot Charts Use Excel for statistical and financial functions and data sharing Get familiar with Solver Use the Small Business Finance Manager If you’re familiar with Excel but lack a background in the technical aspects of data analysis, this user-friendly book makes it easy to start putting it to use for you. |
| data.table cheat sheet: R Markdown Yihui Xie, J.J. Allaire, Garrett Grolemund, 2018-07-27 R Markdown: The Definitive Guide is the first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of R and other languages. In this book, you will learn Basics: Syntax of Markdown and R code chunks, how to generate figures and tables, and how to use other computing languages Built-in output formats of R Markdown: PDF/HTML/Word/RTF/Markdown documents and ioslides/Slidy/Beamer/PowerPoint presentations Extensions and applications: Dashboards, Tufte handouts, xaringan/reveal.js presentations, websites, books, journal articles, and interactive tutorials Advanced topics: Parameterized reports, HTML widgets, document templates, custom output formats, and Shiny documents. Yihui Xie is a software engineer at RStudio. He has authored and co-authored several R packages, including knitr, rmarkdown, bookdown, blogdown, shiny, xaringan, and animation. He has published three other books, Dynamic Documents with R and knitr, bookdown: Authoring Books and Technical Documents with R Markdown, and blogdown: Creating Websites with R Markdown. J.J. Allaire is the founder of RStudio and the creator of the RStudio IDE. He is an author of several packages in the R Markdown ecosystem including rmarkdown, flexdashboard, learnr, and radix. Garrett Grolemund is the co-author of R for Data Science and author of Hands-On Programming with R. He wrote the lubridate R package and works for RStudio as an advocate who trains engineers to do data science with R and the Tidyverse. |
| data.table cheat sheet: An R Companion for Applied Statistics I Danney Rasco, 2020-01-28 Of Key Functions; Chapter 10: Bivariate Pearson Correlation; Checking Assumptions; Performing Pearson's Bivariate Correlation; Considering Alternatives; Summary of Key Functions; Chapter 11: Bivariate Regression; Checking Assumptions; Performing Bivariate Regression; Chapter 12: Independent-Samples t Test; Checking Assumptions; Performing Independent-Samples t Tests; Presenting Results; Considering Alternatives; Summary of Key Functions; Chapter 13: One-Way Between-Subjects Analysis of Variance; Checking Assumptions; Performing One-Way Between-Subjects ANOVA Tests; Presenting Results; Considering Alternatives; Summary of Key Functions; Chapter 14: Paired-Samples t Test; Checking Assumptions; Performing Paired-Samples t Tests; Presenting Results; Considering Alternatives; Summary of Key Functions; Chapter 15: |
| data.table cheat sheet: Excel Data Analysis For Dummies Paul McFedries, 2018-11-13 Take Excel to the next level Excel is the world’s leading spreadsheet application. It’s a key module in Microsoft Office—the number-one productivity suite—and it is the number-one business intelligence tool. An Excel dashboard report is a visual presentation of critical data and uses gauges, maps, charts, sliders, and other graphical elements to present complex data in an easy-to-understand format. Excel Data Analysis For Dummies explains in depth how to use Excel as a tool for analyzing big data sets. In no time, you’ll discover how to mine and analyze critical data in order to make more informed business decisions. Work with external databases, PivotTables, and Pivot Charts Use Excel for statistical and financial functions and data sharing Get familiar with Solver Use the Small Business Finance Manager If you’re familiar with Excel but lack a background in the technical aspects of data analysis, this user-friendly book makes it easy to start putting it to use for you. |
| data.table cheat sheet: SQL For Dummies Allen G. Taylor, 2011-02-23 See how SQL interfaces with today's environments Start building and using relational databases with SQL's newest features The database may be the twenty-first century filing cabinet, but building one is a little more complex than sliding drawers into a metal box. With this book to guide you through all the newest features of SQL, you'll soon be whipping up relational databases, using SQL with XML to power data-driven Web sites, and more! Discover how to * Use SQL in a client/server system * Build a multitable relational database * Construct nested and recursive queries * Set up database security * Use SQL within applications * Map SQL to XML |
| data.table cheat sheet: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| data.table cheat sheet: Statistical Analysis with R For Dummies Joseph Schmuller, 2017-03-20 Understanding the world of R programming and analysis has never been easier Most guides to R, whether books or online, focus on R functions and procedures. But now, thanks to Statistical Analysis with R For Dummies, you have access to a trusted, easy-to-follow guide that focuses on the foundational statistical concepts that R addresses—as well as step-by-step guidance that shows you exactly how to implement them using R programming. People are becoming more aware of R every day as major institutions are adopting it as a standard. Part of its appeal is that it's a free tool that's taking the place of costly statistical software packages that sometimes take an inordinate amount of time to learn. Plus, R enables a user to carry out complex statistical analyses by simply entering a few commands, making sophisticated analyses available and understandable to a wide audience. Statistical Analysis with R For Dummies enables you to perform these analyses and to fully understand their implications and results. Gets you up to speed on the #1 analytics/data science software tool Demonstrates how to easily find, download, and use cutting-edge community-reviewed methods in statistics and predictive modeling Shows you how R offers intel from leading researchers in data science, free of charge Provides information on using R Studio to work with R Get ready to use R to crunch and analyze your data—the fast and easy way! |
| data.table cheat sheet: PHP Cheatsheet for Beginners Riwanto Megosinarso, 2005-05 PHP is a server-side scripting language that is gaining popularity for creating dynamic web pages. It is easy to use, cross-platform, and most importantly, open source. PHP is widely used together with Apache and MySQL database. PHP Cheatsheet for Beginners presents a collection of basic PHP scripts, ranging from the simple task of passing variables between HTML pages, processing forms and working with MySQL database, to somewhat more complex tasks such as creating your own calendar, sending a multipart email, making a bilingual form and more. Every script can be run on its own, so you can quickly try it and see how it works. It is packed with detailed explanations for each script, to help you understand why they work. Aimed at those who have some background in another programming language, PHP Cheatsheet for Beginners sticks to the basics so that you will have a solid base from which to try more complex tasks. The examples and the explanations will equip you with all the tools you need to begin using PHP and start creating a dynamic website. |
| data.table cheat sheet: Effective Data Visualization Stephanie D. H. Evergreen, 2016-04-22 Written by sought-after speaker, designer, and researcher Stephanie D. H. Evergreen, Effective Data Visualization shows readers how to create Excel charts and graphs that best communicate data findings. This comprehensive how-to guide functions as a set of blueprints—supported by research and the author’s extensive experience with clients in industries all over the world—for conveying data in an impactful way. Delivered in Evergreen’s humorous and approachable style, the book covers the spectrum of graph types available beyond the default options, how to determine which one most appropriately fits specific data stories, and easy steps for making the chosen graph in Excel. |
| data.table cheat sheet: Mastering Data Analysis with R Gergely Daroczi, 2015-09-30 Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization About This Book Handle your data with precision and care for optimal business intelligence Restructure and transform your data to inform decision-making Packed with practical advice and tips to help you get to grips with data mining Who This Book Is For If you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic. What You Will Learn Connect to and load data from R's range of powerful databases Successfully fetch and parse structured and unstructured data Transform and restructure your data with efficient R packages Define and build complex statistical models with glm Develop and train machine learning algorithms Visualize social networks and graph data Deploy supervised and unsupervised classification algorithms Discover how to visualize spatial data with R In Detail R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently. This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage. Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods. Style and approach Covering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples. |
| data.table cheat sheet: Practical Data Science Andreas François Vermeulen, 2018-02-21 Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets. The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You'll Learn Become fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling of polyglot data types in a data lake for repeatable results Who This Book Is For Data scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers |
| data.table cheat sheet: HTML 5.1 & CSS3 Ultimate Cheatsheet Sergey Mavrody, 2015-08-30 Today you can find a wealth of HTML5 information on the web including references, tutorials and tips. There are also a very good books available which concentrate on certain HTML5 features. However there is always a need for a relatively concise summary of all that information in one handy reference-style book. This book is an essential technical dictionary for professional web designers and developers, conveniently summarizing over 3000 pages of (X)HTML5 and CSS3 specifications and covering the most common and fundamental concepts and specs, including tags, attributes, properties and selectors. The author’s goal was to create a one-stop reference source, which is comprehensive but still concise, simple, easy-to-read, and structured. |
| data.table cheat sheet: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 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 |
| data.table cheat sheet: An R Companion for Applied Statistics II Danney Rasco, 2020 An R Companion for Applied Statistics II: Multivariable and Multivariate Techniques breaks the language of the R software down into manageable chunks in order to help students learn how to use R to analyze multivariate data. The book has been designed to be an R companion to Rebecca M. Warner′s Applied Statistics II: Third Edition, and includes end-of-chapter instructions for replicating the examples from that book in R. |
| data.table cheat sheet: Cybersecurity for Small Networks Seth Enoka, 2022-12-06 A guide to implementing DIY security solutions and readily available technologies to protect home and small-office networks from attack. This book is an easy-to-follow series of tutorials that will lead readers through different facets of protecting household or small-business networks from cyber attacks. You’ll learn how to use pfSense to build a firewall, lock down wireless, segment a network into protected zones, configure a VPN (virtual private network) to hide and encrypt network traffic and communications, set up proxies to speed up network performance and hide the source of traffic, block ads, install and configure an antivirus, back up your data securely, and even how to monitor your network for unauthorized activity and alert you to intrusion. |
| data.table cheat sheet: Teacher's Skills Tests For Dummies Colin Beveridge, Andrew Green, 2014-04-09 If you’re preparing for the newly revised Numeracy and Literacy Skills Tests, Teacher’s Skills Tests For Dummies is your one-stop for both exams, providing you with subject-matter review, revision and practice tests you need to tackle the tests with confidence and succeed. Written by expert authors in Maths, English, and Education (with the credentials to prove it), Teacher’s Skills Tests For Dummies provides you with: - A review of the key maths and English concepts you need to know to do well - Full length practice tests and tons of additional practice questions - Online accessible audio tests for spelling and mental arithmetic — to better prepare you for the actual test - Tips and tricks (along with mistakes to avoid) to become a better test taker With this book — and a bit of work on your part — you’ll be positioned to pass your skills tests and gain that coveted place on a teacher-training course. |
| data.table cheat sheet: Bioimage Data Analysis Workflows Kota Miura, Nataša Sladoje, 2019-10-17 This Open Access textbook provides students and researchers in the life sciences with essential practical information on how to quantitatively analyze data images. It refrains from focusing on theory, and instead uses practical examples and step-by step protocols to familiarize readers with the most commonly used image processing and analysis platforms such as ImageJ, MatLab and Python. Besides gaining knowhow on algorithm usage, readers will learn how to create an analysis pipeline by scripting language; these skills are important in order to document reproducible image analysis workflows. The textbook is chiefly intended for advanced undergraduates in the life sciences and biomedicine without a theoretical background in data analysis, as well as for postdocs, staff scientists and faculty members who need to perform regular quantitative analyses of microscopy images. |
| data.table cheat sheet: Statistics With R Jenine K. Harris, 2019-12-19 Drawing on examples from across the social and behavioral sciences, Statistics With R: Solving Problems Using Real-World Data introduces foundational statistics concepts with beginner-friendly R programming in an exploration of the world’s tricky problems faced by the R Team characters. |
| data.table cheat sheet: A General Introduction to Data Analytics João Moreira, Andre Carvalho, Tomás Horvath, 2018-07-18 A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples. Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniques A unique illustrative example that extends throughout all the chapters Exercises at the end of each chapter and larger projects at the end of each of the text’s two main parts Together with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic. The book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms. |
| data.table cheat sheet: SQL Notes Er. Durgesh Singh, Dr. Ajay Kumar Bharti, This book is based on the Microsoft Sql Server with clarifications of the all concepts and suitable example of all the related topics. We tried to cover the all topics related to Sql. Basically the Name of the book is Given SQL Notes means this book is totally focused on the crack of goals. |
| data.table cheat sheet: Excel for Fantasy Football John Broberg, 2011-07-30 Don't get stuck with canned cheat sheets anymore! Finally, there's a better way to rank NFL players. Now you too can build cheat sheets completely customized to your league. With Excel for Fantasy Football, you have total control of your player rankings and cheat sheets. Power up your draft strategy, with this instructive step-by-step guide. * Customize your cheat sheet to your league's point scheme. * Effectively rank players with proven (but simple) methods. * Make ingenious rapid decisions at the draft. Let this be the year you finally take control of your cheat sheets and drafts! Imagine: Turn your play time into learning time (and gain valuable job skills). * PivotTable * VLOOKUP functions * Blended RANK'ings This might be the best investment you'll ever make. |
| data.table cheat sheet: R and Data Mining Yanchang Zhao, 2012-12-31 R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. - Presents an introduction into using R for data mining applications, covering most popular data mining techniques - Provides code examples and data so that readers can easily learn the techniques - Features case studies in real-world applications to help readers apply the techniques in their work |
| data.table cheat sheet: Handbook of Price Impact Modeling Kevin T Webster, 2023-05-05 Builds a market simulator to back test trading algorithms Implements closed-form strategies that optimize trading signals Measures liquidity risk and stress test portfolios for fire sales Analyze algorithms’ performance controlling for common trading biases Estimates price impact models using the public trading tape |
| data.table cheat sheet: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| data.table cheat sheet: AWS Certified Solutions Architect - Associate (SAA-C03) Cert Guide Mark Wilkins, 2023-06-05 This is the eBook version of the print title. Note that the eBook does not provide access to the practice test software that accompanies the print book. Learn, prepare, and practice for AWS Certified Solutions Architect - Associate (SAA-C03) exam success with this Cert Guide from Pearson IT Certification, a leader in IT Certification. Master AWS Certified Solutions Architect - Associate (SAA-C03) exam topics Assess your knowledge with chapter-ending quizzes Review key concepts with exam preparation tasks AWS Certified Solutions Architect - Associate (SAA-C03) Cert Guide from Pearson IT Certification prepares you to succeed on the exam by directly addressing the exam's official objectives as stated by Amazon. Leading Cloud expert Mark Wilkins shares preparation hints and test-taking tips, helping you identify areas of weakness and improve both your conceptual knowledge and hands-on skills. Material is presented in a concise manner, focusing on increasing your understanding and retention of exam topics. The book presents you with an organized test preparation routine using proven series elements and techniques. Exam topic lists make referencing easy. Chapter-ending Exam Preparation Tasks help you drill on key concepts you must know thoroughly. Review questions help you assess your knowledge, and a final preparation chapter guides you through tools and resources to help you craft your final study plan. Well regarded for its level of detail, study plans, assessment features, and challenging review questions and exercises, this study guide helps you master all the topics on the AWS Certified Solutions Architect - Associate (SAA-C03) exam, including Secure Architectures: Secure access to AWS resources, secure workloads and applications, data security controls Resilient Architectures: Scalable and loosely coupled architectures, highly available and fault-tolerant architectures High-Performing Architectures: High-performing and scalable storage solutions; high-performing and elastic compute solutions; high-performing database solutions, scalable network architecture, data ingestion, and transformations solutions Cost-Optimized Architectures: Cost-optimized storage solutions, compute solutions, and database solutions; cost-effective network architectures |
| data.table cheat sheet: Statistical Inference via Data Science: A ModernDive into R and the Tidyverse Chester Ismay, Albert Y. Kim, 2019-12-23 Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for tidy and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels. |
| data.table cheat sheet: Data Science Field Cady, 2020-11-25 Tap into the power of data science with this comprehensive resource for non-technical professionals Data Science: The Executive Summary – A Technical Book for Non-Technical Professionals is a comprehensive resource for people in non-engineer roles who want to fully understand data science and analytics concepts. Accomplished data scientist and author Field Cady describes both the “business side” of data science, including what problems it solves and how it fits into an organization, and the technical side, including analytical techniques and key technologies. Data Science: The Executive Summary covers topics like: Assessing whether your organization needs data scientists, and what to look for when hiring them When Big Data is the best approach to use for a project, and when it actually ties analysts’ hands Cutting edge Artificial Intelligence, as well as classical approaches that work better for many problems How many techniques rely on dubious mathematical idealizations, and when you can work around them Perfect for executives who make critical decisions based on data science and analytics, as well as mangers who hire and assess the work of data scientists, Data Science: The Executive Summary also belongs on the bookshelves of salespeople and marketers who need to explain what a data analytics product does. Finally, data scientists themselves will improve their technical work with insights into the goals and constraints of the business situation. |
| data.table cheat sheet: Exam Ref 70-778 Analyzing and Visualizing Data with Microsoft Power BI Daniil Maslyuk, 2018-06-07 Prepare for Microsoft Exam 70-778–and help demonstrate your real-world mastery of Power BI data analysis and visualization. Designed for experienced BI professionals and data analysts ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the MCSA level. Focus on the expertise measured by these objectives: Consume and transform data by using Power BI Desktop Model and visualize data Configure dashboards, reports, and apps in the Power BI Service This Microsoft Exam Ref: Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you have experience consuming and transforming data, modeling and visualizing data, and configuring dashboards using Excel and Power BI |
| data.table cheat sheet: ProPack I Valerie Stetson, Guy Sharrock, Susan Hahn, 2004-08-02 |
| data.table cheat sheet: Visualizing History’s Fragments Ashley R. Sanders, |
| data.table cheat sheet: Excel Dashboards & Reports for Dummies Michael Alexander, 2016-01-26 Make the most of your data using the power of Excel When you think of data, do you think of endless rows and columns in spreadsheets? Excel Dashboards and Reports For Dummies, 3rd Edition shows you how to make the most of your data—and puts an end to mind-numbing spreadsheets by exploring new ways to conceptualize and present key information. There's often a gap between handling data and synthesizing it into meaningful reports, and this approachable text bridges this gap with quick and accessible information that answers key questions, like how to meaningfully capture data trends, how to show relationships in data, and when it's better to show variances than actual data values. As a leading spreadsheet application, Microsoft Excel is the go-to data software. This tool allows you to use dashboard reports that leverage gauges, maps, charts, sliders, and other visual elements to present complex data in a manner that's easy to understand. Using Excel dashboards effectively can improve your professional capabilities by leaps and bounds. Analyze and report on large amounts of data in a meaningful way Look at data from different perspectives, and better visualize the information you're presenting by quickly slicing data on the fly Automate redundant reporting and analysis functions, making your data analysis and reporting routine more efficient Create visualizations, dashboards, and what-if analyses that are as visually appealing as they are substantial Excel Dashboards and Reports For Dummies, 3rd Edition is a fantastic resource if you're looking to spice up your reporting! |
| data.table cheat sheet: DOS Cheat Sheet Jennifer Fulton, 1995 Each section is broken into task-based lessons which cover the basic steps first, followed by more in-depth information. Essential steps are highlighted in a second color for ease of use and handwritten tips are in the margin. The first page of each lesson is a cheat sheet of the basic steps covered in that lesson for a handy reference. |
| data.table cheat sheet: R编程学习指南 Posts & Telecom Press, Kun Ren, 2024-05-15 高效数据分析师实用指南 Key Features 通过简单且实践性强的方式,让读者逐步建立对R语言的理解 通过实际操作实例,让读者亲身体验强大的R工具,并总结R的使用方法 Book DescriptionR 是一个开源、跨平台的科学计算和统计分析软件包,它提供了丰富多样的统计功能和强大的数据分析功能。随着数据科学的快速发展,R已经成为数据分析领域非常流行的语言。 本书通过15章内容,向读者全面讲解了R的基础知识和编程技巧。本书不仅介绍了R的安装、基本对象、工作空间管理、基本表达式、基本对象操作、字符串的使用等基础内容,还对数据处理、R的内部机制、元编程、面向对象编程、数据库操作、数据操作进行了讲解,同时也涉及高性能计算、网页爬虫和效率提升等重要主题。 本书面向数据领域的从业人员,尤其适合想要通过学习R编程及相关工具提升数据处理效率的读者阅读,也适合计算机或统计相关专业的学生参考使用。通过阅读本书,读者将全面掌握R的相关特性及其在数据处理和分析方面的应用,极大地提升自己的专业技能。What you will learn 探索 R 的基本功能,熟悉常用的数据结构; 在R中,使用统计学、数据挖掘和可视化、根查找和最优化的基本函数来处理数据; 了解 R 的计算模式、环境和元编程技术; 掌握 R 中的面向对象编程; 访问 SQLite 等关系型数据库和MongoDB 等非关系型数据库; 了解 Rcpp 等高性能计算技术; 使用网页爬虫技术提取信息; 创建 R Markdown,使用 Shiny 创建交互式应用程序,使用DiagramR或者ggvis扩展包创建交互式图表等。 Who this book is for 本书面向数据领域的从业人员,尤其适合想要通过学习R编程及相关工具提升数据处理效率的读者阅读,也适合计算机或统计相关专业的学生参考使用。通过阅读本书,读者将全面掌握R的相关特性及其在数据处理和分析方面的应用,极大地提升自己的专业技能。 |
| data.table cheat sheet: Excel 2016 For Dummies Greg Harvey, 2015-10-02 Excel 2016 For Dummies (9781119077015) is now being published as Excel 2016 For Dummies (9781119293439). While this version features an older Dummies cover and design, the content is the same as the new release and should not be considered a different product. Let your Excel skills sore to new heights with this bestselling guide Updated to reflect the latest changes to the Microsoft Office suite, this new edition of Excel For Dummies quickly and painlessly gets you up to speed on mastering the world's most widely used spreadsheet tool. Written by bestselling author Greg Harvey, it has been completely revised and updated to offer you the freshest and most current information to make using the latest version of Excel easy and stress-free. If the thought of looking at spreadsheet makes your head swell, you've come to the right place. Whether you've used older versions of this popular program or have never gotten a headache from looking at all those grids, this hands-on guide will get you up and running with the latest installment of the software, Microsoft Excel 2016. In no time, you'll begin creating and editing worksheets, formatting cells, entering formulas, creating and editing charts, inserting graphs, designing database forms, and more. Plus, you'll get easy-to-follow guidance on mastering more advanced skills, like adding hyperlinks to worksheets, saving worksheets as web pages, adding worksheet data to an existing web page, and so much more. Save spreadsheets in the Cloud to work on them anywhere Use Excel 2016 on a desktop, laptop, or tablet Share spreadsheets via email, online meetings, and social media sites Analyze data with PivotTables If you're new to Excel and want to spend more time on your actual work than figuring out how to make it work for you, this new edition of Excel 2016 For Dummies sets you up for success. |
| data.table cheat sheet: The Data Wrangler's Handbook Kyle Banerjee, 2019-08-05 Data manipulation and analysis are far easier than you might imagine—in fact, using tools that come standard with your desktop computer, you can learn how to extract, manipulate, and analyze data (and metadata) of any size and complexity. |
| data.table cheat sheet: CompTIA Data+ DA0-001 Exam Cram Akhil Behl, Siva G Subramanian, 2023-01-03 CompTIA® Data+ DA0-001 Exam Cram is an all-inclusive study guide designed to help you pass the CompTIA Data+ DA0-001 exam. Prepare for test day success with complete coverage of exam objectives and topics, plus hundreds of realistic practice questions. Extensive prep tools include quizzes, Exam Alerts, and our essential last-minute review CramSheet. The powerful Pearson Test Prep practice software provides real-time assessment and feedback with two complete exams. Covers the critical information needed to score higher on your Data+ DA0-001 exam! Understand data concepts, environments, mining, analysis, visualization, governance, quality, and controls Work with databases, data warehouses, database schemas, dimensions, data types, structures, and file formats Acquire data and understand how it can be monetized Clean and profile data so it;s more accurate, consistent, and useful Review essential techniques for manipulating and querying data Explore essential tools and techniques of modern data analytics Understand both descriptive and inferential statistical methods Get started with data visualization, reporting, and dashboards Leverage charts, graphs, and reports for data-driven decision-making Learn important data governance concepts |
DataTables | Javascript table library
Advanced tables, instantly DataTables is a Javascript HTML table enhancing library. It is a highly flexible tool, built upon the foundations of progressive enhancement, that adds all of these …
Download - DataTables
Try it on StackBlitz StackBlitz is a browser based IDE where you can setup an entire development environment directly in your browser. Click the button below to create a demo based on the …
Examples index - DataTables
One of the best ways to learn how to do anything new (including software APIs!) is to get your hands dirty as quickly as possible. These examples will show you how to perform tasks …
Installation - DataTables
A download builder is available which will build a customised package for you - based on the features and styling you want for your DataTable. The resulting files are available on the …
DataTables example - Bootstrap 5
Nov 28, 2008 · As with previous versions of Bootstrap, DataTables can also be integrated seamlessly with Bootstrap 5. This integration is done simply by including the DataTables …
DataTables React component
Aug 21, 2024 · The component accepts a number of properties that can be used to configure the DataTable, including ajax, columns and options. Please refer to the documentation for full details.
Manual - DataTables
Version information: The documentation here is the latest release version of DataTables. However, most concepts apply for older versions as well and the documentation is marked to …
DataTables releases
All released versions of DataTables are available on this CDN. For new projects, please use only the latest version and update older projects to the latest version ...
API - DataTables
new DataTable.Api( selector ); $( selector ).DataTable(); $( selector ).dataTable().api(); The result from each is an instance of the DataTables API object which has the tables found by the …
Reference - DataTables
DataTables and its extensions have a wealth of options which can be configured using the initialisation object that is passed into the $ ().DataTable () constructor when creating a new table.
DataTables | Javascript table library
Advanced tables, instantly DataTables is a Javascript HTML table enhancing library. It is a highly flexible tool, built upon the foundations of progressive enhancement, that adds all of these …
Download - DataTables
Try it on StackBlitz StackBlitz is a browser based IDE where you can setup an entire development environment directly in your browser. Click the button below to create a demo based on the …
Examples index - DataTables
One of the best ways to learn how to do anything new (including software APIs!) is to get your hands dirty as quickly as possible. These examples will show you how to perform tasks ranging …
Installation - DataTables
A download builder is available which will build a customised package for you - based on the features and styling you want for your DataTable. The resulting files are available on the …
DataTables example - Bootstrap 5
Nov 28, 2008 · As with previous versions of Bootstrap, DataTables can also be integrated seamlessly with Bootstrap 5. This integration is done simply by including the DataTables …
DataTables React component
Aug 21, 2024 · The component accepts a number of properties that can be used to configure the DataTable, including ajax, columns and options. Please refer to the documentation for full details.
Manual - DataTables
Version information: The documentation here is the latest release version of DataTables. However, most concepts apply for older versions as well and the documentation is marked to …
DataTables releases
All released versions of DataTables are available on this CDN. For new projects, please use only the latest version and update older projects to the latest version ...
API - DataTables
new DataTable.Api( selector ); $( selector ).DataTable(); $( selector ).dataTable().api(); The result from each is an instance of the DataTables API object which has the tables found by the …
Reference - DataTables
DataTables and its extensions have a wealth of options which can be configured using the initialisation object that is passed into the $ ().DataTable () constructor when creating a new table.
