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Working in a hands-on learning environment, led by our Social Media Analytics with R expert instructor, students will learn about and explore: 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.

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Last Updated

January 6, 2021

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Course Description

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 course 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 course 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.

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Course Curriculum

    • Getting Started with R and Social Media Analytics 00:00:00
    • Understanding social media 00:00:00
    • Social media analytics 00:00:00
    • Getting started with R 00:00:00
    • Data types 00:00:00
    • Data analytics 00:00:00
    • Machine learning 00:00:00
    • Text analytics 00:00:00
    • Twitter – What’s Happening with 140 Characters 00:00:00
    • Understanding Twitter 00:00:00
    • Revisiting analytics workflow 00:00:00
    • Trend analysis 00:00:00
    • Sentiment analysis 00:00:00
    • Follower graph analysis 00:00:00
    • Analyzing Social Networks and Brand Engagements with Facebook 00:00:00
    • Accessing Facebook data 00:00:00
    • Analyzing your personal social network 00:00:00
    • Analyzing an English football social network 00:00:00
    • Analyzing English Football Club’s brand page engagements 00:00:00
    • Foursquare – Are You Checked in Yet? 00:00:00
    • Foursquare – the app and data 00:00:00
    • Category trend analysis 00:00:00
    • Recommendation engine – let’s open a restaurant 00:00:00
    • The sentimental rankings 00:00:00
    • Venue graph – where do people go next? 00:00:00
    • Challenges for Foursquare data analysis 00:00:00
    • Analyzing Software Collaboration Trends I – Social Coding with GitHub 00:00:00
    • Environment setup 00:00:00
    • Understanding GitHub 00:00:00
    • Accessing GitHub data 00:00:00
    • Analyzing repository activity 00:00:00
    • Analyzing repository trends 00:00:00
    • Analyzing language trends 00:00:00
    • Analyzing Software Collaboration Trends II – Answering Your Questions with StackExchange 00:00:00
    • Understanding StackExchange 00:00:00
    • Data Science and StackExchange 00:00:00
    • Demographics and data science 00:00:00
    • Challenges 00:00:00
    • Believe What You See – Flickr Data Analysis 00:00:00
    • A Flickr-ing world 00:00:00
    • Accessing Flickr’s data 00:00:00
    • Understanding Flickr data 00:00:00
    • Understanding interestingness – similarities 00:00:00
    • Are your photos interesting? 00:00:00
    • Challenges 00:00:00
    • News – The Collective Social Media! 00:00:00
    • News data – news is everywhere 00:00:00
    • Sentiment trend analysis 00:00:00
    • Topic modeling 00:00:00
    • Summarizing news articles 00:00:00
    • Challenges to news data analysis 00:00:00

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