R essentials for Data Scientists

Live Classroom
Duration: 5 days
Live Virtual Classroom
Duration: 5 days
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The R Essentials for Data Scientists training program enables participants to work with applications in OpenSource environments including the R language. R is a functional programming environment used commonly by data scientists and data analysts though it can be easily used by non-programmers as well. Either ways, working with R language is a much sought after skill for data analysts and data scientists. The course discusses using R with various libraries to effectively apply Spark/R and Sparkly/R. Participants will learn to come up with effective solutions to common real-life scenarios encountered when performing different analysis.

What You'll Learn

  • R language and mathematics
  • Working with R vectors
  • Reading and writing data from files, and categorizing data in factors
  • Working with dates and perform date math
  • Working with multiple dimensions and DataFrame essentials
  • Fundamentals of data science and how to use R with it
  • Visualization in R
  • Application of R in Spark


  • Common problems with Excel
  • The R environment
  • Hello, R
  • CRAN

  • Simple Math with R
  • Working with Vectors
  • Functions
  • Comments and code structure
  • Using packages

  • Vector properties
  • Creating, combining and iterating
  • Passing and returning vectors in functions
  • Logical vectors

  • Working with dates
  • Date formats and formatting
  • Time manipulation and operations

  • Adding a second dimension
  • Indices and named rows and columns n a Matrix
  • Matrix calculations
  • n-Dimensional arrays
  • Data frames
  • Lists

  • AI grouping theory
  • K-means
  • Linear regression
  • Logistic regression
  • Elastic net

  • Powerful data through visualization: Communicating the message
  • R in Spark
  • Demos
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Who should attend

The course is highly recommended for –

  • Data scientists
  • Data analysts
  • Machine learning professionals


Participants need to have familiarity with data analytics.

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