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Course Skill Level:


Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Beginners with basic Python experience

Who should attend & recommended skills

  • This course is designed for beginners who want to tap into the realm of social media and unleash the power of analytics for data-driven insights using R.
  • Skill-level: Foundation-level Social Media skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)

About this course

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.

Skills acquired & topics covered

  • 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
  • Data access, retrieval, cleaning, and curation methods for data originating from various social media platforms.
  • Visualizing and analyzing 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.
  • How to tap into data from diverse social media platforms using the R ecosystem
  • Using social media data to formulate and solve real-world problems
  • Analyzing user social networks and communities using concepts from graph theory and network analysis
  • Detecting opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channels
  • Understanding the art of representing actionable insights with effective visualizations
  • Analyzing data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so on
  • Leverage popular R packages such as ggplot2, topic models, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many more

Course breakdown / modules

  • Understanding social media
  • Social media analytics
  • Getting started with R
  • Data types
  • Data analytics
  • Machine learning
  • Text analytics

  • Understanding Twitter
  • Revisiting analytics workflow
  • Trend analysis
  • Sentiment analysis
  • Follower graph analysis
  • 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

  • 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

  • Environment setup
  • Understanding GitHub
  • Accessing GitHub data
  • Analyzing repository activity
  • Analyzing repository trends
  • Analyzing language trends

  • Understanding StackExchange
  • Data Science and StackExchange
  • Demographics and data science
  • Challenges

  • A Flickr-ing world
  • Accessing Flickr’s data
  • Understanding Flickr data
  • Understanding interestingness – similarities
  • Are your photos interesting?
  • Challenges

  • News data – news is everywhere
  • Sentiment trend analysis
  • Topic modeling
  • Summarizing news articles
  • Challenges to news data analysis