Training Level


Training Duration (Days)


Training Mode

Physical classroom


  1. A strong quantitative background with a solid understanding of basic statistics, as would be found in a statistics 101 level course.
  2. Experience with a scripting language, such as Java, Perl, or Python (or R). Many of the lab examples taught in the course use R (with an RStudio GUI), which is an open source statistical tool and programming.
  3. Experience with SQL.

Career Track

  1. Data Scientist
  2. Data Analyst

Industry Skill

Accreditation Body

Dell EMC

Skill Outcome

By the end of this course, you will be able to:

  1. Immediately participate as a data science team member.
  2. Work with large data sets and generate insights.
  3. Build predictive and classification models.
  4. Manage a data analytics project through the entire lifecycle.