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Author: Raghav Bali
Publisher: Packt Publishing Ltd
ISBN: 1787125467
Category : Computers
Languages : en
Pages : 394
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Book Description
Tap into the realm of social media and unleash the power of analytics for data-driven insights using R About This Book A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media data Learn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms. Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering. Who This Book Is For It is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise. What You Will Learn Learn how to tap into data from diverse social media platforms using the R ecosystem Use social media data to formulate and solve real-world problems Analyze user social networks and communities using concepts from graph theory and network analysis Learn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channels Understand the art of representing actionable insights with effective visualizations Analyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so on Learn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many more In Detail The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data. The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights. Style and approach This book follows a step-by-step approach with detailed strategies for understanding, extracting, analyzing, visualizing, and modeling data from several major social network platforms such as Facebook, Twitter, Foursquare, Flickr, Github, and StackExchange. The chapters cover several real-world use cases and leverage data science, machine learning, network analysis, and graph theory concepts along with the R ecosystem, including popular packages such as ggplot2, caret,dplyr, topicmodels, tm, and so on.
Author: Raghav Bali
Publisher: Packt Publishing Ltd
ISBN: 1787125467
Category : Computers
Languages : en
Pages : 394
Get Book Here
Book Description
Tap into the realm of social media and unleash the power of analytics for data-driven insights using R About This Book A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media data Learn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms. Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering. Who This Book Is For It is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise. What You Will Learn Learn how to tap into data from diverse social media platforms using the R ecosystem Use social media data to formulate and solve real-world problems Analyze user social networks and communities using concepts from graph theory and network analysis Learn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channels Understand the art of representing actionable insights with effective visualizations Analyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so on Learn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many more In Detail The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data. The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights. Style and approach This book follows a step-by-step approach with detailed strategies for understanding, extracting, analyzing, visualizing, and modeling data from several major social network platforms such as Facebook, Twitter, Foursquare, Flickr, Github, and StackExchange. The chapters cover several real-world use cases and leverage data science, machine learning, network analysis, and graph theory concepts along with the R ecosystem, including popular packages such as ggplot2, caret,dplyr, topicmodels, tm, and so on.
Author: Ryan A. Estrellado
Publisher: Routledge
ISBN: 1000200906
Category : Education
Languages : en
Pages : 331
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Book Description
Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a "learn by doing" approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.
Author: Subodha Kumar
Publisher: CRC Press
ISBN: 1000515338
Category : Technology & Engineering
Languages : en
Pages : 68
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Book Description
Social Media Analytics and Practical Applications: The Change to the Competition Landscape provides a framework that allows you to understand and analyze the impact of social media in various industries. It illustrates how social media analytics can help firms build transformational strategies and cope with the challenges of social media technology. By focusing on the relationship between social media and other technology models, such as wisdom of crowds, healthcare, fintech and blockchain, machine learning methods, and 5G, this book is able to provide applications used to understand and analyze the impact of social media. Various industries are called out and illustrate how social media analytics can help firms build transformational strategies and at the same time cope with the challenges that are part of the landscape. The book discusses how social media is a driving force in shaping consumer behavior and spurring innovations by embracing and directly engaging with consumers on social media platforms. By closely reflecting on emerging practices, the book shows how to take advantage of recent advancements and how business operations are being revolutionized. Social Media Analytics and Practical Applications is written for academicians and professionals involved in social media and social media analytics.
Author: Hadley Wickham
Publisher: "O'Reilly Media, Inc."
ISBN: 1491910364
Category : Computers
Languages : en
Pages : 521
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Book Description
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Author: A Ohri
Publisher: Springer Science & Business Media
ISBN: 1461443423
Category : Business & Economics
Languages : en
Pages : 322
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Book Description
This book examines common tasks performed by business analysts and helps the reader navigate the wealth of information in R and its 4000 packages to create useful analytics applications. Includes interviews with corporate users of R, and easy-to-use examples.
Author: Chris Chapman
Publisher: Springer
ISBN: 9783319144351
Category : Business & Economics
Languages : en
Pages : 0
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Book Description
This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis. Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications.
Author: Karen E. Sutherland
Publisher: Springer Nature
ISBN: 9811546584
Category : Business & Economics
Languages : en
Pages : 431
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Book Description
This textbook provides a lively introduction to the fast-paced and multi-faceted discipline of social media management with international examples and perspectives. Aside from focusing on practical application of marketing strategy, the textbook also takes students through the process of strategy development, ethical and accurate content curation, and strategy implementation, through detailed explanations of content creation. Combining theory and practice, Strategic Social Media Management teaches students how to take a strategic approach to social media from an organisational and business perspective, and how to measure results. Richly supported by robust and engaging pedagogy and cases in each chapter, it integrates perspectives from public relations, marketing and advertising, and examines key topics such as risk, ethics, privacy, consent, copyright issues, and crises management. It also provides dedicated coverage of content strategy and campaign planning and execution. Reflecting the demands of contemporary practice, advice on self-care for social media management is also offered, helping to protect people in this emerging profession from the negativity that they can experience online when managing an organisation’s social media presence. After reading this textbook, students will be able to develop a social media strategy, curate accurate and relevant content, and create engaging social media content that tells compelling stories, connects with target audiences and supports strategic goals and objectives. This is an ideal textbook for students studying social media strategy, marketing and management at undergraduate level. It will also be essential reading for marketing, public relations, advertising and communications professionals looking to hone their social media skills and strategies.
Author: Vikas (Vik) Kumar
Publisher: Packt Publishing Ltd
ISBN: 1787283224
Category : Computers
Languages : en
Pages : 258
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Book Description
Add a touch of data analytics to your healthcare systems and get insightful outcomes Key Features Perform healthcare analytics with Python and SQL Build predictive models on real healthcare data with pandas and scikit-learn Use analytics to improve healthcare performance Book Description In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists’ work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples. What you will learn Gain valuable insight into healthcare incentives, finances, and legislation Discover the connection between machine learning and healthcare processes Use SQL and Python to analyze data Measure healthcare quality and provider performance Identify features and attributes to build successful healthcare models Build predictive models using real-world healthcare data Become an expert in predictive modeling with structured clinical data See what lies ahead for healthcare analytics Who this book is for Healthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare.
Author: Fridolin Wild
Publisher: Springer
ISBN: 3319287915
Category : Computers
Languages : en
Pages : 287
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Book Description
This book introduces Meaningful Purposive Interaction Analysis (MPIA) theory, which combines social network analysis (SNA) with latent semantic analysis (LSA) to help create and analyse a meaningful learning landscape from the digital traces left by a learning community in the co-construction of knowledge. The hybrid algorithm is implemented in the statistical programming language and environment R, introducing packages which capture – through matrix algebra – elements of learners’ work with more knowledgeable others and resourceful content artefacts. The book provides comprehensive package-by-package application examples, and code samples that guide the reader through the MPIA model to show how the MPIA landscape can be constructed and the learner’s journey mapped and analysed. This building block application will allow the reader to progress to using and building analytics to guide students and support decision-making in learning.
Author: Simon Kingsnorth
Publisher: Kogan Page Publishers
ISBN: 1398605980
Category : Business & Economics
Languages : en
Pages : 417
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Book Description
Build an effective and practical digital marketing strategy with this bestselling guide, covering everything from automation and analytics to integrating AI. Digital Marketing Strategy is a global bestseller, and a one-stop guide to structuring and building a more strategic approach to digital marketing. Now fully updated, this third edition covers the integration of AI in marketing, e-commerce, marketing automation, affiliate marketing and how to use digital analytical tools, plus new strategies for the latest cookie changes and privacy protection. Digital Marketing Strategy will show you how to effectively select, align and manage digital channels and operations, to streamline a successful digital marketing strategy for measurable, optimized results. Recommended by the Chartered Institute of Marketing (CIM), it is supported by real-world case studies from the likes of Coca-Cola, Spotify, Airbnb, Adidas and Hostelworld as well as checklists, key terms and insights from leading industry practitioners to help you develop your own digital marketing strategy. This book is an invaluable guide for both digital marketing students and entry-level to mid-management marketing professionals. Accompanying online resources consist of practical implementation guides spanning SEO, paid-search, email, lead-generation, as well as presentation slides and activity sheets.
