To better prepare students for the academic rigor of the Master of Science in Data Science program, there are two self-paced bridge courses available — a refresher course in statistics as well as a course in programming using Python and R. These self-paced courses are designed to help you review statistical and programming knowledge before classes begin.
Programming Bridge: Python and R
In this course, students build on their existing programming skills, learning to use both the Python and R programming languages. Students are introduced to both languages and receive general, guided examples of using both languages. Topics covered include using variables, abstract data types, custom classes and inheritance, package management, and numerical methods. Statistical and numeric packages are introduced as well as linear algebra operations, simple visualization, and vectorized computation. Upon course completion, students will have a basic understanding of how to use these languages as a basis for performing data analysis. Building experience through exploratory examples is encouraged.
numpy, scipy, matplotlib,
Bridge to Statistics
In this course, students work through a thorough refresher of the methods and theories learned in their introduction to statistics course. The course starts from the beginning with basic measures of center and spread which prepares the student for a section on the normal distribution and perhaps the most fundamental theory in statistics: the Central Limit Theorem (CLT). This is followed by discussions on one- and two-sample confidence intervals and hypothesis tests for the difference of means. The course concludes with a section on the use and interpretation of simple linear regression techniques which leaves the student prepared to begin a much more in-depth study of statistical methods provided in the Statistical Foundations for Data Science course. The course is largely example-based and provides opportunities for students to work with the instructor to solve problems with both handwritten and SAS-based solutions.