Training Duration (Days)
- A strong quantitative background with a solid understanding of basic statistics, as would be found in a statistics 101 level course.
- 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.
- Experience with SQL.
By the end of this course, you will be able to:
- Immediately participate as a data science team member.
- Work with large data sets and generate insights.
- Build predictive and classification models.
- Manage a data analytics project through the entire lifecycle.