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| data analysis with r programming google: Beginner's Guide for Data Analysis using R Programming Jeeva Jose, R programming is an efficient tool for statistical analysis of data. Data science has become critical to each field and the popularity of R is skyrocketing. Organization as large and diverse as Google, Facebook, Microsoft, Bank of America, Ford Motor Company, Mozilla, Thomas Cook, The New York Times, The National Weather Service, Twitter, ANZ Bank, Uber, Airbnb etc . have turned to R for reporting, analyzing and visualization of data, this book is for students and professionals of Mathematics, Statistics, Physics, Chemistry, Biology, Social Science and Medicine, Business, Engineering, Software, Information Technology, Sales, Bio Informatics, Pharmacy and any one, where data needs to be analyzed and represented graphically. |
| data analysis with r programming google: 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 analysis with r programming google: R Programming for Data Analytics Dr.S.Rasheed Mansoor Ali, Dr.N.Prakash, Dr.S.Sivagami, Dr.T.Gunasekar, Dr.G.Stephen, 2024-06-23 Dr.S.Rasheed Mansoor Ali, Assistant Professor, Department of Computer Applications, Jamal Mohamed College (Autonomous), Tiruchirappalli, Tamil Nadu, India. Dr.N.Prakash, Assistant Professor, Department of Management Studies, The American College, Madurai, Tamil Nadu, India. Dr.S.Sivagami, Associate Professor, Department of Computer Science & Engineering , Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India. Dr.T.Gunasekar, Professor, Department of Mathematics, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University), Avadi, Chennai, Tamil Nadu, India. Dr.G.Stephen, Assistant Librarian, St. Xavier's University, Kolkata, West Bengal. |
| data analysis with r programming google: R Programming: An Approach to Data Analytics G. Sudhamathy, C. Jothi Venkateswaran, 2019-06-03 Chapter 1 - Basics of R, Chapter 2 - Data Types in R , Chapter 3 - Data Preparation. Chapter 4 - Graphics using R, Chapter 5 - Statistical Analysis Using R, Chapter 6 - Data Mining Using R, Chapter 7 - Case Studies. Huge volumes of data are being generated by many sources like commercial enterprises, scientific domains and general public daily. According to a recent research, data production will be 44 times greater in 2020 than it was in 2010. Data being a vital resource for business organizations and other domains like education, health, manufacturing etc., its management and analysis is becoming increasingly important. This data, due to its volume, variety and velocity, often referred to as Big Data, also includes highly unstructured data in the form of textual documents, web pages, graphical information and social media comments. Since Big Data is characterised by massive sample sizes, high dimensionality and intrinsic heterogeneity, traditional approaches to data management, visualisation and analytics are no longer satisfactorily applicable. There is therefore an urgent need for newer tools, better frameworks and workable methodologies for such data to be appropriately categorised, logically segmented, efficiently analysed and securely managed. This requirement has resulted in an emerging new discipline of Data Science that is now gaining much attention with researchers and practitioners in the field of Data Analytics. |
| data analysis with r programming google: Learn R Programming in 24 Hours Alex Nordeen, 2020-10-31 R is a programming language developed is widely used for statistical and graphical analysis. It can execute advance machine learning algorithms including earning algorithm, linear regression, time series, statistical inference. R programming language is used by Fortune 500 companies and tech bellwethers like Uber, Google, Airbnb, Facebook, Apple. R provides a data scientist tools and libraries (Dplyr) to perform the 3 steps of analysis 1) Extract 2) Transform, Cleanse 3) Analyze. Table of Contents Chapter 1: What is R Programming Language? Introduction & Basics Chapter 2: How to Download & Install R, RStudio, Anaconda on Mac or Windows Chapter 3: R Data Types, Arithmetic & Logical Operators with Example Chapter 4: R Matrix Tutorial: Create, Print, add Column, Slice Chapter 5: Factor in R: Categorical & Continuous Variables Chapter 6: R Data Frame: Create, Append, Select, Subset Chapter 7: List in R: Create, Select Elements with Example Chapter 8: R Sort a Data Frame using Order() Chapter 9: R Dplyr Tutorial: Data Manipulation(Join) & Cleaning(Spread) Chapter 10: Merge Data Frames in R: Full and Partial Match Chapter 11: Functions in R Programming (with Example) Chapter 12: IF, ELSE, ELSE IF Statement in R Chapter 13: For Loop in R with Examples for List and Matrix Chapter 14: While Loop in R with Example Chapter 15: apply(), lapply(), sapply(), tapply() Function in R with Examples Chapter 16: Import Data into R: Read CSV, Excel, SPSS, Stata, SAS Files Chapter 17: How to Replace Missing Values(NA) in R: na.omit & na.rm Chapter 18: R Exporting Data to Excel, CSV, SAS, STATA, Text File Chapter 19: Correlation in R: Pearson & Spearman with Matrix Example Chapter 20: R Aggregate Function: Summarise & Group_by() Example Chapter 21: R Select(), Filter(), Arrange(), Pipeline with Example Chapter 22: Scatter Plot in R using ggplot2 (with Example) Chapter 23: How to make Boxplot in R (with EXAMPLE) Chapter 24: Bar Chart & Histogram in R (with Example) Chapter 25: T Test in R: One Sample and Paired (with Example) Chapter 26: R ANOVA Tutorial: One way & Two way (with Examples) Chapter 27: R Simple, Multiple Linear and Stepwise Regression [with Example] Chapter 28: Decision Tree in R with Example Chapter 29: R Random Forest Tutorial with Example Chapter 30: Generalized Linear Model (GLM) in R with Example Chapter 31: K-means Clustering in R with Example Chapter 32: R Vs Python: What's the Difference? Chapter 33: SAS vs R: What's the Difference? |
| data analysis with r programming google: R For Programmes Karter Tate & Morgan Holland, 2019-05-02 R for programmers presents valuable apparatuses and strategies for assaulting many sorts of R programming issues, helping you dodge oversights and deadlocks. With over ten years of experience programming in R, the creator delineates the tastefulness, magnificence, and adaptability at the core of R. The book builds up the fundamental aptitudes to deliver quality code that can be utilized as a part of an assortment of conditions. You will learn: The basics of R, including standard information sorts and capacities; Utilitarian programming as a valuable system for taking care of wide classes of issues; The positives and negatives of meta programming; Instructions to compose quick, memory-productive code. This book not just enables current R clients to wind up R developers yet in addition indicates existing software engineers what's exceptional about R. |
| data analysis with r programming google: Data Science with R Programming Basics Dr.Sudhakar.K, Mrs.Geethanjali.S.G, Mrs.Rashmi.D.M, Mrs.Sinchana K.P, 2024-08-13 Dr.Sudhakar.K, Associate Professor, Department of Artificial Intelligence & Data Science, NITTE Meenakshi Institute of Technology, Bangalore, Karnataka, India. Mrs.Geethanjali.S.G, Assistant Professor, Department of Computer Science & Engineering, DON BOSCO Institute of Technology, Bangalore, Karnataka, India. Mrs.Rashmi.D.M, Assistant Professor, Department of Computer Science & Engineering, DON BOSCO Institute of Technology, Bangalore, Karnataka, India. Mrs.Sinchana K.P, Assistant Professor, Department of Computer Science & Engineering, DON BOSCO Institute of Technology, Bangalore, Karnataka, India. |
| data analysis with r programming google: DATA SCIENCE WITH R PROGRAMMING Dr.CARMEL MARY BELINDA.M.J, Dr.K.NATTAR KANNAN, Dr.R.GNANAJEYARAMAN, Dr.U.ARUL, Dr.M.RAMA MOORTHY, 2024-02-07 Dr.CARMEL MARY BELINDA.M.J, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.K.NATTAR KANNAN, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.R.GNANAJEYARAMAN, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical And Technical Sciences, Saveetha University, Chennai, India. Dr.U.ARUL, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.M.RAMA MOORTHY, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. |
| data analysis with r programming google: R Programming and Its Applications in Financial Mathematics Shuichi Ohsaki, Jori Ruppert-Felsot, Daisuke Yoshikawa, 2018-01-31 This book provides an introduction to R programming and a summary of financial mathematics. It is not always easy for graduate students to grasp an overview of the theory of finance in an abstract form. For newcomers to the finance industry, it is not always obvious how to apply the abstract theory to the real financial data they encounter. Introducing finance theory alongside numerical applications makes it easier to grasp the subject. Popular programming languages like C++, which are used in many financial applications are meant for general-purpose requirements. They are good for implementing large-scale distributed systems for simultaneously valuing many financial contracts, but they are not as suitable for small-scale ad-hoc analysis or exploration of financial data. The R programming language overcomes this problem. R can be used for numerical applications including statistical analysis, time series analysis, numerical methods for pricing financial contracts, etc. This book provides an overview of financial mathematics with numerous examples numerically illustrated using the R programming language. |
| data analysis with r programming google: R Programming in Statistics Prof Dr Balasubramanian Thiagarajan, 2022-12-04 Every professional needs to perform statistical analysis in some form of the other. In order to perform this task various software tools are available. Majority of them are paid software. R programming which is an open source tool can be used to perform statistical analysis. Since it is an open source tool many front end GUI’s are available to make the job easier for the user. In this book the most popular GUI RStudio is used. RStudio is a most powerful GUI front end for R programming which has been designed to use all the features of this language with ease. This book has been authored with a novice user in mind. Various steps in statistical analysis have been explained in detail using a large number of screenshots. Codes used have been clearly illustrated. The book has been structured in such a manner to ensure that basic concepts have been clearly explained with the help of screenshots before taking on challenging analytical problems. Towards the end of the book the reader is provided with an additional resource which gives out all the codes used in this book as well as those additional ones that have not found their place in the book. Learning R coding is not difficult provided the reader spends time practicing the same. The reader is encouraged to execute all the codes provided in the R_code manual which has been provided at the end of the book. R programming can be compared to that of SPSS (the popular statistical analytical tool) as far as its ability to perform statistical analysis. One tip the author wishes to provide to the reader who is attempting to make data entry within the RStudio environment. It is always better to import data into RStudio for performing data analysis. Data can be imported from Excel , google spread sheets etc. The reader is encouraged to download the install the software and libraries that have been described in the book and to try them out. Advantages of R Programming : 1. It is a powerful statistical tool 2. It is open source and hence it is free 3. It is an excellent tool that can be used to perform visual analysis of a dataset. It can created different types of charts and graphs, thereby facilitating accurate analysis of data |
| data analysis with r programming google: Real Time Data Analytics and R Programming Dr.J.Chenni Kumaran, Dr.M.Sivaram, Dr.A.Manimaran, Dr.A.Selvakumar, Dr.S. Ramesh, 2024-02-05 Dr.J.Chenni Kumaran, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.M.Sivaram, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.A.Manimaran, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.A.Selvakumar, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.S. Ramesh, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. |
| data analysis with r programming google: Intelligent Data Analysis Deepak Gupta, Siddhartha Bhattacharyya, Ashish Khanna, Kalpna Sagar, 2020-04-17 This book focuses on methods and tools for intelligent data analysis, aimed at narrowing the increasing gap between data gathering and data comprehension, and emphasis will also be given to solving of problems which result from automated data collection, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and so on. This book aims to describe the different approaches of Intelligent Data Analysis from a practical point of view: solving common life problems with data analysis tools. |
| data analysis with r programming google: Global Security, Safety and Sustainability: The Security Challenges of the Connected World Hamid Jahankhani, Alex Carlile, David Emm, Amin Hosseinian-Far, Guy Brown, Graham Sexton, Arshad Jamal, 2017-01-03 This book constitutes the refereed proceedings of the 11th International Conference on Global Security, Safety and Sustainability, ICGS3 2017, held in London, UK, in January, 2017. The 32 revised full papers presented were carefully reviewed and selected from 74 submissions. The papers are organized in topical sections on the future of digital forensics; cyber intelligence and operation; information systems security management; systems security, safety, and sustainability; cyber infrastructure protection. |
| data analysis with r programming google: AI and IoT-Based Technologies for Precision Medicine Khang, Alex, 2023-10-18 In the post-COVID-19 healthcare landscape, the demand for smart healthcare solutions and precision medicine systems has grown significantly. To address these challenges, the book AI and IoT-Based Technologies for Precision Medicine provides a comprehensive resource for doctors, researchers, engineers, and students. By leveraging AI and IoT technologies, the book equips healthcare professionals with advanced tools and methodologies for predictive disease analysis, informed decision-making, and other aspects of precision medicine. This resource bridges the gap between theory and practice, exploring concepts like machine learning, deep learning, computer vision, AI-integrated applications, IoT-based technologies, healthcare data analytics, and biotechnology applications. Through this, the book empowers healthcare practitioners to pioneer innovative solutions that enhance efficiency, accuracy, and security in medical practices. AI and IoT-Based Technologies for Precision Medicine not only offer insights into the potential of AI-powered applications and IoT-equipped techniques in smart healthcare but also foster collaboration among healthcare scholars and professionals. This authoritative guide encourages knowledge sharing and collaboration to harness the transformative potential of AI and IoT, leading to revolutionary advancements in medical practices and healthcare services. With this book as a guide, readers can navigate the evolving landscape of high-tech medicine, taking confident steps toward a cutting-edge and precise medical ecosystem. |
| data analysis with r programming google: XML and Web Technologies for Data Sciences with R Deborah Nolan, Duncan Temple Lang, 2013-11-29 Web technologies are increasingly relevant to scientists working with data, for both accessing data and creating rich dynamic and interactive displays. The XML and JSON data formats are widely used in Web services, regular Web pages and JavaScript code, and visualization formats such as SVG and KML for Google Earth and Google Maps. In addition, scientists use HTTP and other network protocols to scrape data from Web pages, access REST and SOAP Web Services, and interact with NoSQL databases and text search applications. This book provides a practical hands-on introduction to these technologies, including high-level functions the authors have developed for data scientists. It describes strategies and approaches for extracting data from HTML, XML, and JSON formats and how to programmatically access data from the Web. Along with these general skills, the authors illustrate several applications that are relevant to data scientists, such as reading and writing spreadsheet documents both locally and via Google Docs, creating interactive and dynamic visualizations, displaying spatial-temporal displays with Google Earth, and generating code from descriptions of data structures to read and write data. These topics demonstrate the rich possibilities and opportunities to do new things with these modern technologies. The book contains many examples and case-studies that readers can use directly and adapt to their own work. The authors have focused on the integration of these technologies with the R statistical computing environment. However, the ideas and skills presented here are more general, and statisticians who use other computing environments will also find them relevant to their work. Deborah Nolan is Professor of Statistics at University of California, Berkeley. Duncan Temple Lang is Associate Professor of Statistics at University of California, Davis and has been a member of both the S and R development teams. |
| data analysis with r programming google: CLOUD COMPUTING ARCHITECTURE (DESIGN, IMPLEMENTATION, AND SECURITY STRATEGIES) ASHISH KUMAR SHYAMAKRISHNA SIDDHARTH CHAMARTHY RAMYA RAMACHANDRAN RAGHAV AGARWAL, 2024-10-24 In the ever-evolving landscape of the modern world, the synergy between technology and management has become a cornerstone of innovation and progress. This book, Cloud Computing Architecture: Design, Implementation, and Security Strategies, is conceived to bridge the gap between emerging technological advancements in cloud computing and their strategic application in modern IT management. Our objective is to equip readers with the tools and insights necessary to excel in this dynamic intersection of fields. This book is structured to provide a comprehensive exploration of the methodologies and strategies that define the innovation of cloud technologies, particularly in terms of architecture, implementation, and security. From foundational theories to advanced applications, we delve into the critical aspects that drive successful cloud-based solutions in enterprise environments. We have made a concerted effort to present complex concepts in a clear and accessible manner, making this work suitable for a diverse audience, including students, IT managers, and industry professionals. In authoring this book, we have drawn upon the latest research and best practices to ensure that readers not only gain a robust theoretical understanding but also acquire practical skills that can be applied in real-world cloud computing scenarios. The chapters are designed to strike a balance between depth and breadth, covering topics ranging from technological development and cloud architecture design to the strategic management of security in cloud-based systems. Additionally, we emphasize the importance of effective communication, dedicating sections to the art of presenting innovative ideas and solutions in a precise and academically rigorous manner. The inspiration for this book arises from a recognition of the crucial role that cloud computing architecture and security strategies play in shaping the future of digital businesses. We are profoundly grateful to Chancellor Shri Shiv Kumar Gupta of Maharaja Agrasen Himalayan Garhwal University for his unwavering support and vision. His dedication to fostering academic excellence and promoting a culture of innovation has been instrumental in bringing this project to fruition. We hope this book will serve as a valuable resource and inspiration for those eager to deepen their understanding of how cloud computing technologies and management practices can be harnessed together to drive innovation. We believe that the knowledge and insights contained within these pages will empower readers to lead the way in creating secure, scalable cloud solutions that will define the future of enterprise IT. Thank you for joining us on this journey. Authors |
| data analysis with r programming google: Python for R Users Ajay Ohri, 2017-11-01 The definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to code in Python and Python users to program in R. Short on theory and long on actionable analytics, it provides readers with a detailed comparative introduction and overview of both languages and features concise tutorials with command-by-command translations—complete with sample code—of R to Python and Python to R. Following an introduction to both languages, the author cuts to the chase with step-by-step coverage of the full range of pertinent programming features and functions, including data input, data inspection/data quality, data analysis, and data visualization. Statistical modeling, machine learning, and data mining—including supervised and unsupervised data mining methods—are treated in detail, as are time series forecasting, text mining, and natural language processing. • Features a quick-learning format with concise tutorials and actionable analytics • Provides command-by-command translations of R to Python and vice versa • Incorporates Python and R code throughout to make it easier for readers to compare and contrast features in both languages • Offers numerous comparative examples and applications in both programming languages • Designed for use for practitioners and students that know one language and want to learn the other • Supplies slides useful for teaching and learning either software on a companion website Python for R Users: A Data Science Approach is a valuable working resource for computer scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as textbook for students of computer science and statistics. A. Ohri is the founder of Decisionstats.com and currently works as a senior data scientist. He has advised multiple startups in analytics off-shoring, analytics services, and analytics education, as well as using social media to enhance buzz for analytics products. Mr. Ohri's research interests include spreading open source analytics, analyzing social media manipulation with mechanism design, simpler interfaces for cloud computing, investigating climate change and knowledge flows. His other books include R for Business Analytics and R for Cloud Computing. |
| data analysis with r programming google: Learning Google BigQuery Eric Brown, Thirukkumaran Haridass, 2017-12-22 Get a fundamental understanding of how Google BigQuery works by analyzing and querying large datasets About This Book Get started with BigQuery API and write custom applications using it Learn how BigQuery API can be used for storing, managing, and query massive datasets with ease A practical guide with examples and use-cases to teach you everything you need to know about Google BigQuery Who This Book Is For If you are a developer, data analyst, or a data scientist looking to run complex queries over thousands of records in seconds, this book will help you. No prior experience of working with BigQuery is assumed. What You Will Learn Get a hands-on introduction to Google Cloud Platform and its services Understand the different data types supported by Google BigQuery Migrate your enterprise data to BigQuery and query it using the legacy and standard SQL techniques Use partition tables in your project and query external data sources and wild card tables Create tables and data sets dynamically using the BigQuery API Perform real-time inserting of records for analytics using Python and C# Visualize your BigQuery data by connecting it to third party tools such as Tableau and R Master the Google Cloud Pub/Sub for implementing real-time reporting and analytics of your Big Data In Detail Google BigQuery is a popular cloud data warehouse for large-scale data analytics. This book will serve as a comprehensive guide to mastering BigQuery, and how you can utilize it to quickly and efficiently get useful insights from your Big Data. You will begin with getting a quick overview of the Google Cloud Platform and the various services it supports. Then, you will be introduced to the Google BigQuery API and how it fits within in the framework of GCP. The book covers useful techniques to migrate your existing data from your enterprise to Google BigQuery, as well as readying and optimizing it for analysis. You will perform basic as well as advanced data querying using BigQuery, and connect the results to various third party tools for reporting and visualization purposes such as R and Tableau. If you're looking to implement real-time reporting of your streaming data running in your enterprise, this book will also help you. This book also provides tips, best practices and mistakes to avoid while working with Google BigQuery and services that interact with it. By the time you're done with it, you will have set a solid foundation in working with BigQuery to solve even the trickiest of data problems. Style and Approach This book follows a step-by-step approach to teach readers the concepts of Google BigQuery using SQL. To explain various data querying processes, large-scale datasets are used wherever required. |
| data analysis with r programming google: Quantitative Social Science Kosuke Imai, Nora Webb Williams, 2022-07-05 A tidyverse edition of the acclaimed textbook on data analysis and statistics for the social sciences and allied fields Quantitative analysis is an essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it. Quantitative Social Science is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, including business, economics, education, political science, psychology, sociology, public policy, and data science. Proven in classrooms around the world, this one-of-a-kind textbook engages directly with empirical analysis, showing students how to analyze and interpret data using the tidyverse family of R packages. Data sets taken directly from leading quantitative social science research illustrate how to use data analysis to answer important questions about society and human behavior. Emphasizes hands-on learning, not paper-and-pencil statistics Includes data sets from actual research for students to test their skills on Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools Features a wealth of supplementary exercises, including additional data analysis exercises and programming exercises Offers a solid foundation for further study Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides |
| data analysis with r programming google: Introduction to Biomedical Data Science Robert Hoyt, Robert Muenchen, 2019-11-24 Overview of biomedical data science -- Spreadsheet tools and tips -- Biostatistics primer -- Data visualization -- Introduction to databases -- Big data -- Bioinformatics and precision medicine -- Programming languages for data analysis -- Machine learning -- Artificial intelligence -- Biomedical data science resources -- Appendix A: Glossary -- Appendix B: Using data.world -- Appendix C: Chapter exercises. |
| data analysis with r programming google: Big Data for Analytics Dr.P.S.S.Akilashri, Ms.S.Thulasi Bharathi, Mrs.G.Nithya, Mrs.A.Ahadha Parveen, Mrs.B.Divya Prabha, 2023-06-07 Dr.P.S.S.Akilashri, Associate Professor and Head, Department of Computer Science, National College, Tiruchirappalli, Tamil Nadu, India. Ms.S.Thulasi Bharathi, Assistant Professor, Department of Computer Science, St. Josephs College Autonomous, Tiruchirappalli, Tamil Nadu, India. Mrs.G.Nithya, Assistant Professor and Head, Department of Computer Applications, Kongu College of Arts and Science, Karur, Tamil Nadu, India. Mrs.A.Ahadha Parveen, Assistant Professor, Department of Computer Science, Lady Doak College, Madurai, Tamil Nadu, India. Mrs.B.Divya Prabha, Vice Principal, Head of the Department of Computer Science, Sree Gokulam Public School, Chengalpattu, Tamil Nadu, India. |
| data analysis with r programming google: Humanities and Big Data in Ibero-America Ana Gallego Cuiñas, Daniel Torres-Salinas, 2023-11-20 La colección presenta trabajos interdisciplinares que hacen uso de herramientas no solo humanistas sino también digitales para proponer enfoques inéditos sobre Literatura, Lingüística, Teoría Crítica y Filosofía en el espacio multicultural iberoamericano del siglo XXI. Las tres principales líneas de investigación - los corpus lingüísticos digitalizados, la lingüística experimental, y la relación entre Literatura, Crítica y Big Data - combinan el análisis de datos con un pensamiento crítico que trasciende el dataísmo y abre nuevas perspectivas (biopolítica, feminista y decolonial) en las Humanidades Digitales. The series presents interdisciplinary studies harnessing humanistic as well as digital tools to offer innovative approaches to literary studies, linguistics, critical theory and philosophy in the multicultural Ibero-American space of the 21st century. Its three principal lines of research - digital linguistic corpora, experimental linguistics, and the relation between literature, critique and big data - combine data analysis with critical thinking that transcends mere dataism and opens new (biopolitical, feminist, decolonial...) perspectives within Digital Humanities. |
| data analysis with r programming google: Pharmacokinetics and Pharmacodynamics of Novel Drug Delivery Systems: From Basic Concepts to Applications Sankalp A. Gharat, |
| data analysis with r programming google: Handbook of Research on Artificial Intelligence Applications in Literary Works and Social Media Keikhosrokiani, Pantea, Pourya Asl, Moussa, 2022-12-30 Artificial intelligence has been utilized in a diverse range of industries as more people and businesses discover its many uses and applications. A current field of study that requires more attention, as there is much opportunity for improvement, is the use of artificial intelligence within literary works and social media analysis. The Handbook of Research on Artificial Intelligence Applications in Literary Works and Social Media presents contemporary developments in the adoption of artificial intelligence in textual analysis of literary works and social media and introduces current approaches, techniques, and practices in data science that are implemented to scrap and analyze text data. This book initiates a new multidisciplinary field that is the combination of artificial intelligence, data science, social science, literature, and social media study. Covering key topics such as opinion mining, sentiment analysis, and machine learning, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students. |
| data analysis with r programming google: Ultimate Big Data Analytics with Apache Hadoop Simhadri Govindappa, 2024-09-09 TAGLINE Master the Hadoop Ecosystem and Build Scalable Analytics Systems KEY FEATURES ● Explains Hadoop, YARN, MapReduce, and Tez for understanding distributed data processing and resource management. ● Delves into Apache Hive and Apache Spark for their roles in data warehousing, real-time processing, and advanced analytics. ● Provides hands-on guidance for using Python with Hadoop for business intelligence and data analytics. DESCRIPTION In a rapidly evolving Big Data job market projected to grow by 28% through 2026 and with salaries reaching up to $150,000 annually—mastering big data analytics with the Hadoop ecosystem is most sought after for career advancement. The Ultimate Big Data Analytics with Apache Hadoop is an indispensable companion offering in-depth knowledge and practical skills needed to excel in today's data-driven landscape. The book begins laying a strong foundation with an overview of data lakes, data warehouses, and related concepts. It then delves into core Hadoop components such as HDFS, YARN, MapReduce, and Apache Tez, offering a blend of theory and practical exercises. You will gain hands-on experience with query engines like Apache Hive and Apache Spark, as well as file and table formats such as ORC, Parquet, Avro, Iceberg, Hudi, and Delta. Detailed instructions on installing and configuring clusters with Docker are included, along with big data visualization and statistical analysis using Python. Given the growing importance of scalable data pipelines, this book equips data engineers, analysts, and big data professionals with practical skills to set up, manage, and optimize data pipelines, and to apply machine learning techniques effectively. Don’t miss out on the opportunity to become a leader in the big data field to unlock the full potential of big data analytics with Hadoop. WHAT WILL YOU LEARN ● Gain expertise in building and managing large-scale data pipelines with Hadoop, YARN, and MapReduce. ● Master real-time analytics and data processing with Apache Spark’s powerful features. ● Develop skills in using Apache Hive for efficient data warehousing and complex queries. ● Integrate Python for advanced data analysis, visualization, and business intelligence in the Hadoop ecosystem. ● Learn to enhance data storage and processing performance using formats like ORC, Parquet, and Delta. ● Acquire hands-on experience in deploying and managing Hadoop clusters with Docker and Kubernetes. ● Build and deploy machine learning models with tools integrated into the Hadoop ecosystem. WHO IS THIS BOOK FOR? This book is tailored for data engineers, analysts, software developers, data scientists, IT professionals, and engineering students seeking to enhance their skills in big data analytics with Hadoop. Prerequisites include a basic understanding of big data concepts, programming knowledge in Java, Python, or SQL, and basic Linux command line skills. No prior experience with Hadoop is required, but a foundational grasp of data principles and technical proficiency will help readers fully engage with the material. TABLE OF CONTENTS 1. Introduction to Hadoop and ASF 2. Overview of Big Data Analytics 3. Hadoop and YARN MapReduce and Tez 4. Distributed Query Engines: Apache Hive 5. Distributed Query Engines: Apache Spark 6. File Formats and Table Formats (Apache Ice-berg, Hudi, and Delta) 7. Python and the Hadoop Ecosystem for Big Data Analytics - BI 8. Data Science and Machine Learning with Hadoop Ecosystem 9. Introduction to Cloud Computing and Other Apache Projects Index |
| data analysis with r programming google: Neural Network Programming Rob Botwright, 101-01-01 Unlock the Power of AI with Our Neural Network Programming Book Bundle Are you ready to embark on a journey into the exciting world of artificial intelligence? Do you dream of mastering the skills needed to create cutting-edge AI systems that can revolutionize industries and change the future? Look no further than our comprehensive book bundle, Neural Network Programming: How to Create Modern AI Systems with Python, TensorFlow, and Keras. Why Choose Our Book Bundle? In this era of technological advancement, artificial intelligence is at the forefront of innovation. Neural networks, a subset of AI, are driving breakthroughs in fields as diverse as healthcare, finance, and autonomous vehicles. To harness the full potential of AI, you need knowledge and expertise. That's where our book bundle comes in. What You'll Gain · Book 1 - Neural Network Programming for Beginners: If you're new to AI, this book is your perfect starting point. Learn Python, TensorFlow, and Keras from scratch and build your first AI systems. Lay the foundation for a rewarding journey into AI development. · Book 2 - Advanced Neural Network Programming: Ready to take your skills to the next level? Dive deep into advanced techniques, fine-tune models, and explore real-world applications. Master the intricacies of TensorFlow and Keras to tackle complex AI challenges. · Book 3 - Neural Network Programming: Beyond the Basics: Discover the world beyond fundamentals. Explore advanced concepts and cutting-edge architectures like Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). Be prepared to innovate in AI research and development. · Book 4 - Expert Neural Network Programming: Elevate yourself to expert status. Dive into quantum neural networks, ethical AI, model deployment, and the future of AI research. Push the boundaries of AI development with advanced Python, TensorFlow, and Keras techniques. Who Is This Bundle For? · Aspiring AI Enthusiasts: If you're new to AI but eager to learn, our bundle offers a gentle and structured introduction. · Seasoned Developers: Professionals seeking to master AI development will find advanced techniques and real-world applications. · Researchers: Dive into cutting-edge AI research and contribute to the forefront of innovation. Why Us? Our book bundle is meticulously crafted by experts with a passion for AI. We offer a clear, step-by-step approach, ensuring that learners of all backgrounds can benefit. With hands-on projects, real-world applications, and a focus on both theory and practice, our bundle equips you with the skills and knowledge needed to succeed in the ever-evolving world of AI. Don't miss this opportunity to unlock the power of AI. Invest in your future today with Neural Network Programming: How to Create Modern AI Systems with Python, TensorFlow, and Keras. Start your journey into the exciting world of artificial intelligence now! |
| data analysis with r programming google: Cultural and Tourism Innovation in the Digital Era Vicky Katsoni, Thanasis Spyriadis, 2020-02-13 This book explores a wide range of emerging cultural, heritage, and other tourism issues that will shape the future of hospitality and tourism research and practice in the digital and innovation era. It offers stimulating new perspectives in the fields of tourism, travel, hospitality, culture and heritage, leisure, and sports within the context of a knowledge society and smart economy. A central theme is the need to adopt a more holistic approach to tourism development that is aligned with principles of sustainability; at the same time, the book critically reassesses the common emphasis on innovation as a tool for growth-led and market-oriented development. In turn, fresh approaches to innovation practices underpinned by ethics and sustainability are encouraged, and opportunities for the exploration of new research avenues and projects on innovation in tourism are highlighted. Based on the proceedings of the Sixth International Conference of the International Association of Cultural and Digital Tourism (IACuDiT) and edited in collaboration with IACuDiT, the book will appeal to a broad readership encompassing academia, industry, government, and other organizations. |
| data analysis with r programming google: Applied Big Data Analytics in Operations Management Kumar, Manish, 2016-09-30 Operations management is a tool by which companies can effectively meet customers’ needs using the least amount of resources necessary. With the emergence of sensors and smart metering, big data is becoming an intrinsic part of modern operations management. Applied Big Data Analytics in Operations Management enumerates the challenges and creative solutions and tools to apply when using big data in operations management. Outlining revolutionary concepts and applications that help businesses predict customer behavior along with applications of artificial neural networks, predictive analytics, and opinion mining on business management, this comprehensive publication is ideal for IT professionals, software engineers, business professionals, managers, and students of management. |
| data analysis with r programming google: Data Analytics and Machine Learning Pushpa Singh, |
| data analysis with r programming google: Foundations of Programming, Statistics, and Machine Learning for Business Analytics Ram Gopal, Dan Philps, Tillman Weyde, 2023-04-22 Business Analysts and Data Scientists are in huge demand, as global companies seek to digitally transform themselves and leverage their data resources to realize competitive advantage. This book covers all the fundamentals, from statistics to programming to business applications, to equip you with the solid foundational knowledge needed to progress in business analytics. Assuming no prior knowledge of programming or statistics, this book takes a simple step-by-step approach which makes potentially intimidating topics easy to understand, by keeping Maths to a minimum and including examples of business analytics in practice. Key features: · Introduces programming fundamentals using R and Python · Covers data structures, data management and manipulation and data visualization · Includes interactive coding notebooks so that you can build up your programming skills progressively Suitable as an essential text for undergraduate and postgraduate students studying Business Analytics or as pre-reading for students studying Data Science. Ram Gopal is Pro-Dean and Professor of Information Systems at the University of Warwick. Daniel Philps is an Artificial Intelligence Researcher and Head of Rothko Investment Strategies. Tillman Weyde is Senior Lecturer at City, University of London. |
| data analysis with r programming google: R PROGRAMMING Dr.R.GOPINATH, Dr.A.GNANABASKARAN, Mr.P.SATHISHKUMAR, Mr.R.VIJAY SAI , 2023-10-23 Dr.R.GOPINATH, Associate Professor, Department of Computer Science and Engineering, K.S.Rangasamy College of Technology (Autonomous), Tiruchengode, Tamil Nadu, India. Dr.A.GNANABASKARAN, Professor, Department of Computer Science and Engineering, K.S.Rangasamy College of Technology (Autonomous), Tiruchengode, Tamil Nadu, India. Mr.P.SATHISHKUMAR, Associate Professor, Department of Computer Science and Engineering, K.S.Rangasamy College of Technology (Autonomous), Tiruchengode, Tamil Nadu, India. Mr.R.VIJAY SAI, Assistant Professor, Department of Computer Science and Engineering, K.S.Rangasamy College of Technology (Autonomous), Tiruchengode, Tamil Nadu, India. |
| data analysis with r programming google: Data Science for Web3 Gabriela Castillo Areco, 2023-12-29 Be part of the future of Web3, decoding blockchain data to build trust in the next-generation internet Key Features Build a deep understanding of the fundamentals of blockchain analytics Extract actionable business insights by modeling blockchain data Showcase your work and gain valuable experience to seize opportunities in the Web3 ecosystem Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData is the new oil and Web3 is generating it at an unprecedented rate. Complete with practical examples, detailed explanations, and ideas for portfolio development, this comprehensive book serves as a step-by-step guide covering the industry best practices, tools, and resources needed to easily navigate the world of data in Web3. You’ll begin by acquiring a solid understanding of key blockchain concepts and the fundamental data science tools essential for Web3 projects. The subsequent chapters will help you explore the main data sources that can help address industry challenges, decode smart contracts, and build DeFi- and NFT-specific datasets. You’ll then tackle the complexities of feature engineering specific to blockchain data and familiarize yourself with diverse machine learning use cases that leverage Web3 data. The book includes interviews with industry leaders providing insights into their professional journeys to drive innovation in the Web 3 environment. Equipped with experience in handling crypto data, you’ll be able to demonstrate your skills in job interviews, academic pursuits, or when engaging potential clients. By the end of this book, you’ll have the essential tools to undertake end-to-end data science projects utilizing blockchain data, empowering you to help shape the next-generation internet.What you will learn Understand the core components of blockchain transactions and blocks Identify reliable sources of on-chain and off-chain data to build robust datasets Understand key Web3 business questions and how data science can offer solutions Build your skills to create and query NFT- and DeFi-specific datasets Implement a machine learning toolbox with real-world use cases in the Web3 space Who this book is for This book is designed for data professionals—data analysts, data scientists, or data engineers— and business professionals, aiming to acquire the skills for extracting data from the Web3 ecosystem, as it demonstrates how to effectively leverage data tools for in-depth analysis of blockchain transactional data. If you seek hands-on experience, you'll find value in the shared repository, enabling you to experiment with the provided solutions. While not mandatory, a basic understanding of statistics, machine learning, and Python will enhance your learning experience. |
| data analysis with r programming google: Python Programming, Deep Learning Anthony Adams, 2021-12-17 Easily Boost Your Skills In Python Programming & Become A Master In Deep Learning & Data Analysis! 💻 Python is an interpreted, high-level, general-purpose programming language that emphasizes code readability with its notable use of significant whitespace. What makes Python so popular in the IT industry is that it uses an object-oriented approach, which enables programmers to write clear, logical code for all types of projects, whether big or small. Hone your Python Programming skills and gain a sharp edge over other programmers the EASIEST way possible... with this practical beginner’s guide! In his 3-in-1 Python crash course for beginners, Anthony Adams gives novices like you simple, yet efficient tips and tricks to become a MASTER in Python coding for artificial intelligence, neural networks, machine learning, and data science/analysis! Here’s what you’ll get: ✅ Highly innovative ways to boost your understanding of Python programming, data analysis, and machine learning ✅ Quickly and effectively stop fraud with machine learning ✅ Practical and efficient exercises that make understanding Python quick & easy And so much more! As a beginner, you might feel a bit intimidated by the complexities of coding. Add the fact that most Python Programming crash course guides make learning harder than it has to be! ✓ With the help of this 3-in-1 guide, you will be given carefully sequenced Python Programming lessons that’ll maximize your understanding, and equip you with all the skills for real-life application! ★ Thrive in the IT industry with this comprehensive Python Programming crash course! ★ Scroll up, Click on “Buy Now”, and Start Learning Today! |
| data analysis with r programming google: Algorithms for Data Science Brian Steele, John Chandler, Swarna Reddy, 2016-12-25 This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners. |
| data analysis with r programming google: Journalism in the Data Age Jingrong Tong, 2022-03-05 This book is your guide to understanding what journalism is and could be in an age of digital technology and datafication. Journalism today is entwined with the digital. Stories can come from crowdsourcing and content farms. They can incorporate data visualisations and virtual reality. Journalists can find themselves working as self-employed digital entrepreneurs or for tech giants like Google and Facebook. This book explores the development of journalism in this era of digital tech, and big and open data. It explores the crucial new developments of online journalism, data journalism, computational journalism and entrepreneurial journalism, and what this means for our understanding of journalism as a profession, and as a part of society. Using a wealth of international case studies, Jingrong Tong explores contemporary issues such as: AI, Automated news, ‘robot reporters’, and algorithmic accountability. Digital business models, from venture capital to tech start-ups to crowd-funding. Audiences and dissemination in and age of platform capitalism Questions of censorship, democracy and state control. Digital challenges to journalistic autonomy and legitimacy. With clear explanations throughout, Journalism in the Data Age introduces you to a range of ideas, debates and key concepts. It is essential reading for all students of journalism. Dr Jingrong Tong is Senior Lecturer in Digital News Cultures at the University of Sheffield. |
| data analysis with r programming google: Mastering Python Data Visualization Kirthi Raman, 2015-10-27 Generate effective results in a variety of visually appealing charts using the plotting packages in Python About This Book Explore various tools and their strengths while building meaningful representations that can make it easier to understand data Packed with computational methods and algorithms in diverse fields of science Written in an easy-to-follow categorical style, this book discusses some niche techniques that will make your code easier to work with and reuse Who This Book Is For If you are a Python developer who performs data visualization and wants to develop existing knowledge about Python to build analytical results and produce some amazing visual display, then this book is for you. A basic knowledge level and understanding of Python libraries is assumed. What You Will Learn Gather, cleanse, access, and map data to a visual framework Recognize which visualization method is applicable and learn best practices for data visualization Get acquainted with reader-driven narratives and author-driven narratives and the principles of perception Understand why Python is an effective tool to be used for numerical computation much like MATLAB, and explore some interesting data structures that come with it Explore with various visualization choices how Python can be very useful in computation in the field of finance and statistics Get to know why Python is the second choice after Java, and is used frequently in the field of machine learning Compare Python with other visualization approaches using Julia and a JavaScript-based framework such as D3.js Discover how Python can be used in conjunction with NoSQL such as Hive to produce results efficiently in a distributed environment In Detail Python has a handful of open source libraries for numerical computations involving optimization, linear algebra, integration, interpolation, and other special functions using array objects, machine learning, data mining, and plotting. Pandas have a productive environment for data analysis. These libraries have a specific purpose and play an important role in the research into diverse domains including economics, finance, biological sciences, social science, health care, and many more. The variety of tools and approaches available within Python community is stunning, and can bolster and enhance visual story experiences. This book offers practical guidance to help you on the journey to effective data visualization. Commencing with a chapter on the data framework, which explains the transformation of data into information and eventually knowledge, this book subsequently covers the complete visualization process using the most popular Python libraries with working examples. You will learn the usage of Numpy, Scipy, IPython, MatPlotLib, Pandas, Patsy, and Scikit-Learn with a focus on generating results that can be visualized in many different ways. Further chapters are aimed at not only showing advanced techniques such as interactive plotting; numerical, graphical linear, and non-linear regression; clustering and classification, but also in helping you understand the aesthetics and best practices of data visualization. The book concludes with interesting examples such as social networks, directed graph examples in real-life, data structures appropriate for these problems, and network analysis. By the end of this book, you will be able to effectively solve a broad set of data analysis problems. Style and approach The approach of this book is not step by step, but rather categorical. The categories are based on fields such as bioinformatics, statistical and machine learning, financial computation, and linear algebra. This approach is beneficial for the community in many different fields of work and also helps you learn how one approach can make sense across many fields |
| data analysis with r programming google: Computational Methods for Next Generation Sequencing Data Analysis Ion Mandoiu, Alexander Zelikovsky, 2016-09-12 Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms Discusses the mathematical and computational challenges in NGS technologies Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics. |
| data analysis with r programming google: Predictive Analytics Using Statistics and Big Data: Concepts and Modeling Krishna Kumar Mohbey, Arvind Pandey, Dharmendra Singh Rajput, 2020-12-09 This book presents a selection of the latest and representative developments in predictive analytics using big data technologies. It focuses on some critical aspects of big data and machine learning and provides studies for readers. The chapters address a comprehensive range of advanced data technologies used for statistical modeling towards predictive analytics. Topics included in this book include: - Categorized machine learning algorithms - Player monopoly in cricket teams. - Chain type estimators - Log type estimators - Bivariate survival data using shared inverse Gaussian frailty models - Weblog analysis - COVID-19 epidemiology This reference book will be of significant benefit to the predictive analytics community as a useful guide of the latest research in this emerging field. |
| data analysis with r programming google: Analytics and Big Data for Accountants Jim Lindell, 2018-03-23 Analytics is the new force driving business. Tools have been created to measure program impacts and ROI, visualize data and business processes, and uncover the relationship between key performance indicators, many using the unprecedented amount of data now flowing into organizations. Featuring updated examples and surveys, this dynamic book covers leading-edge topics in analytics and finance. It is packed with useful tips and practical guidance you can apply immediately. This book prepares accountants to: Deal with major trends in predictive analytics, optimization, correlation of metrics, and big data. Interpret and manage new trends in analytics techniques affecting your organization. Use new tools for data analytics. Critically interpret analytics reports and advise decision makers. |
| data analysis with r programming google: Remote Sensing and Digital Image Processing with R - Lab Manual Marcelo de Carvalho Alves, Luciana Sanches, 2023-06-30 This Lab Manual is a companion to the textbook Remote Sensing and Digital Image Processing with R. It covers examples of natural resource data analysis applications including numerous, practical problem-solving exercises, and case studies that use the free and open-source platform R. The intuitive, structural workflow helps students better understand a scientific approach to each case study in the book and learn how to replicate, transplant, and expand the workflow for further exploration with new data, models, and areas of interest. Features Aims to expand theoretical approaches of remote sensing and digital image processing through multidisciplinary applications using R and R packages. Engages students in learning theory through hands-on real-life projects. All chapters are structured with solved exercises and homework and encourage readers to understand the potential and the limitations of the environments. Covers data analysis in the free and open-source R platform, which makes remote sensing accessible to anyone with a computer. Explores current trends and developments in remote sensing in homework assignments with data to further explore the use of free multispectral remote sensing data, including very high spatial resolution information. Undergraduate- and graduate-level students will benefit from the exercises in this Lab Manual, because they are applicable to a variety of subjects including environmental science, agriculture engineering, as well as natural and social sciences. Students will gain a deeper understanding and first-hand experience with remote sensing and digital processing, with a learn-by-doing methodology using applicable examples in natural resources. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
