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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:

Those with basic Python experience

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts, or others with Python skills who wish to get valuable insights from your data by building data analysis systems from scratch with R.
  • Skill-level: Foundation-level R Data Analysis Projects skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)

About this course

R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it’s one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. This course will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle. You’ll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. You’ll implement time-series modeling for anomaly detection, and understand cluster analysis of streaming data. You’ll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow codes. With the help of these real-world projects, you’ll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The course covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively. By the end of this course, you’ll have a better understanding of data analysis with R, and be able to put your knowledge to practical use without any hassle.

Skills acquired & topics covered

  • A handy guide to take your understanding of data analysis with R to the next level
  • Real-world projects that focus on problems in finance, network analysis, social media, and more
  • From data manipulation to analysis to visualization in R – everything you need to know about building end-to-end data analysis pipelines using R
  • Building end-to-end predictive analytics systems in R
  • Building an experimental design to gather your own data and conduct analysis
  • Building a recommender system from scratch using different approaches
  • Using and leveraging RShiny to build reactive programming applications
  • Building systems for varied domains including market research, network analysis, social media analysis, and more
  • Various R Packages such as RShiny, ggplot, recommenderlab, dplyr, and find out how to use them effectively
  • Communicating modeling results using Shiny Dashboards
  • Performing multi-variate time-series analysis prediction, supplemented with sensitivity analysis and risk modeling

Course breakdown / modules

  • Understanding the recommender systems
  • Retailer use case and data
  • Association rule mining
  • The cross-selling campaign
  • Weighted association rule mining
  • Hyperlink-induced topic search (HITS)
  • Negative association rules
  • Rules visualization
  • Wrapping up

  • Introducing content-based recommendation
  • News aggregator use case and data
  • Designing the content-based recommendation engine
  • Complete R Code

  • Collaborative filtering
  • Recommenderlab package
  • Use case and data
  • Designing and implementing collaborative filtering
  • Complete R Code

  • Time series data
  • Deep neural networks
  • Introduction to the MXNet R package
  • Symbolic programming in MXNet
  • Training test split
  • Complete R code

  • Kernel density estimation
  • Twitter text
  • Sentiment classification
  • Dictionary based scoring
  • Text pre-processing
  • Building a sentiment classifier
  • Assembling an RShiny application
  • Complete R code

  • Introducing our use case
  • Demonstrating the use of RecordLinkage package
  • Stochastic record linkage
  • Machine learning-based record linkage
  • Building an RShiny application
  • Complete R code

  • Streaming data and its challenges
  • Introducing stream clustering
  • Introducing the stream package
  • Use case and data
  • Complete R code

  • Graphs in R
  • Use case and data
  • Data preparation
  • Product network analysis
  • Building a RShiny application
  • The complete R script