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About the course

Data science is an automated method to analyse massive volumes of data from various sources and extract insights from it. Small and large corporate today are sitting on a gold mine of data, but their biggest challenge is extracting business insights from this data to take effective business decisions. Data analytics is the science of examining raw data to draw business- related conclusions from it and model and predict business outcomes.

This comprehensive course can help an individual gain experience on the power of data and how to explore the data using R as a tool to get valuable insights for the business. This course may also help in the improvement of organization’s performance by doing trend analysis and pattern study. The course shall help to run various algorithms using R which is one of the most effective tool to uncover the patterns within the data and compare the results and insights.

Who should attend?

  • Working professionals who intend to build their career in the field of data analytics
  • Professionals who are currently in the Data Analytics domain
  • Fresh graduates and young professionals
  • Entrepreneurs
  • Professionals from the quality team
  • Six Sigma Professionals
  • IT Professionals

Course benefits

This program may help you to:

  • Understand your business data better and be able to generate trend and get insights
  • Take appropriate and faster business decisions which is data driven
  • Increase efficiency and reduce cost for your business/ domain
  • Gauge customer internal/external needs and satisfaction
  • Uncover new growth opportunities for the business
  • Diagnose the business problem faster

Course coverage

  • Introduction to analytics
  • Introduction to R
  • Basic building blocks of R
  • Working with data in R: Importing, exporting and data wrangling
  • Functions, loops, and data frames in R
  • Descriptive statistics using R
  • Inferential statistics using R
  • Visualization using R
  • Linear, non-linear and logistic regression
  • Classification
  • Clustering
  • Machine learning Algorithms
  • Decision trees
  • Neural Networks
For queries, feedback or assistance

Contact EY Virtual Academy Support