Course DescriptionThis course is an introduction to R, analyzing and exploring data with R, and using R with a database. It focuses on statistics for model building and evaluation. Topics cover experimental research, correlation analysis, regression, confidence intervals, and group comparisons, and parametric and non-parametric models.
What Will You Learn?
This course provides data analytics technical skills for conducting quantitative analyses on data and big data.
Learn how to:
- Utilize real datasets with graphs and numerical measures.
- Conduct a uni-variate and bi-variate analyses on datasets in R.
- Flag potential outliers for further investigation.
- Illustrate graphically the probability distribution of variables.
- Differentiate between discrete and continuous probability distributions.
- Calculate binomial probabilities using normal approximations.
- Investigate whether a sample size is large enough to apply the central limit theorem.
- Apply testing and estimation techniques for simple linear regression analyses in R.
Course materials, video lectures and discussions are delivered and facilitated online within the D2L Learning Management System.
Throughout the semester, student questions related to course content may be answered either by the instructor on discussion board or by an online tutor via email. For more information, please email Anne-Marie Brinsmead, Program Director, at email@example.com
- Applied Analytics and Statistics for 21st Century Decision-Making : Required Courses (Option 1), Required Courses (Option 2)
- Business Decision Analysis : Electives (select 4)
- Computer Programming Applications : Electives (select 2)
- Data Analytics, Big Data, and Predictive Analytics : Required Courses
- Financial Predictive Data Analytics : Electives (select 2)
- Health Informatics : Electives (select 3)
- Scientific Research Policy and Ethics : Required Courses (select 3)