Description

This course gives participants the opportunity to master foundational knowledge and skills needed for data analysis, along with a discussion of common challenges and pitfalls. Participants will be introduced to various methods of data preparation, and to some intrinsic limitations of data and data analysis, and to easily avoidable pre-analysis mistakes.

Following the course, the participants have the option of working on a guided project, getting feedback from the instructor.

Learning Outcomes

At the end of this course the participant will be able to:
  • Select appropriate methods to prepare their data for analysis
  • Anticipate challenges and limitations inherent to data and desired analysis outcomes
  • Apply data cleaning strategies to their data
  • Conduct simple analyses
  • Build simple data science pipelines to provide actionable insights

Additional Information

  • Participants must be comfortable with the concepts introduced in a Probability and Statistics university-level course.
  • Participants are required to have a laptop/PC on which the current version of R/RStudio is installed (for which they may require administrative authorisation to install packages).

Duration

12 hours

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Dr. Boily is a professor in the Mathematics Department and has offered upwards of 200+ training days in data science, analysis, visualization, and machine learning to 60+ cohorts of professionals in the Ottawa region since 2015.