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Learning Social Media Analytics with R

  • Course Code: Data Science - Learning Social Media Analytics with R
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 2 Days Audience: This course is geared for those who wants to Tap into the realm of social media and unleash the power of analytics for data-driven insights using R

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Social Media skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Tap into the realm of social media and unleash the power of analytics for data-driven insights using R  
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

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. 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • 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 

Audience & Pre-Requisites 

This course is designed for for beginners who wants to tap into the realm of social media and unleash the power of analytics for data-driven insights using R 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

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

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