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Making Data Science with R Effective

When you are trying to leverage data science in business decisions, a structured approach is more effective rather than straight forward jumping into applying techniques and tools.

Data science with R can easily allow you to do data visualizations, analytics techniques, machine learning algorithms, statistical methods, and integration with Big data technologies, however the focus should be to solve the correct problem.

A business has a requirement which should be first converted into a correctly defined problem statement. Business usually articulates a requirement at a very broad level such as:

  • We want to improve sales
  • Our cost is too much
  • Customer satisfaction survey shows poor results

This broad articulation needs to be converted into a well-defined specific problem statement to solve using data sciences with R.

The steps mentioned below can help in this translation:

  • Identify what dimensions contribute to the business area
  • Assess the importance for each of the dimensions
  • Pin point if any area is being missed or ignored
  • Now start with a hypothesis or assumption that you think might explain the problem under focus
  • Define the problem statement and parameters in measurable terms to solve
  • Identify and accumulate the data for these parameters
  • Apply data analytical techniques that help in proving or disapproving your hypothesis

Only after you have defined the problem statement clearly should you look for the appropriate tools, advanced analytics techniques or Big Data technologies. The focus of the Data Science with R online training is to help you make that translation and make data analysis effective.

April 27, 2018