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Data Science with R

Business Analytics is study of Business in detail and analyzing its data to get insight of the business. It helps the business to understand the pros and cons of the method they are using and as to what strategy they should adhere for the expansion of the business.
Data Science is giving a huge opportunity for business around the globe to look at data differently and derive actionable insights and deliver tangible benefits. R is a brilliant statistical tool contributed heavily by statisticians, has end to end capabilities to deliver analytics requirements from data ingestion, processing, visualization and modelling.
Our data science with R online training course educates you regarding data science and analytics techniques, along with R programming. This ensures industry expected knowledge, which improves chances of better career.
Why R programming for a Data Science career?
  • R is one of the fastest growing statistical tools and it is estimated that nearly 40% of the organizations delivering analytics projects use it.
  • It’s simple and yet powerful
  • Scalability is being built on Spark and Microsoft Azure platform
Take-away from this course:
  • Understand how to convert business requirement into analytics requirement
  • Understand various analytics techniques like supervised and unsupervised learning
  • Learn R as a statistical tool
Pre-requisites to learn Data Science with R:
  • Some basic programming skills is preferred and not required
  • Even non-programmers can easily pick up R skills
R programing is a powerful medium for creating visuals to help the management understand the current scenario of the business.
Our differentiator for Data Science with R online training course:
  • Highly experienced trainers,
  • Industry aligned course content as well as practice exercises
  • Access to trainer for 1—1 doubt clearance discussion
Click here to check out Business Analytics – Course Orientation video.

Data Science with R Curriculum

Course Detail
  • Introduction to Data and Statistics

  • Basic Statistics – Mean, Medium, Mode, Variance, Standard Deviation (SD)

  • R and R Navigation

  • Data Operations in R

  • Visualization Concepts