COURSE DESCRIPTION: This course introduces students to R, a powerful and versatile programming language used extensively in data analysis and statistical computing. The course will cover basic programming concepts, data manipulation and wrangling, documentation, visualization, and the use of AI (ChatGPT) as a tool to help write your R code. This course is designed for students with little to no programming background.

OBJECTIVES: To learn fundamental R programming, as well as how to write, execute, debug, and annotate R syntaxes.

To learn different data types, programming concepts, and toolsets, and apply that knowledge to develop good programming habits for tidying, manipulating, and visualizing data.

To learn data management skills as well as appropriate tools for continuing learning long after the course has completed.

RECOMMENDED MATERIALS: Students must own a laptop computer with administrative rights to be able to install R and R-Studio. (Program installation will be covered in the first session for students who need assistance.)

For Module Two: Survival Analysis: A Self-learning Text by David Kleinbaum and Mitchel Klein (3rd edition) Springer; ISBN: 978-1-4419-6645-2 (Print) 978-1-4419-6646-9 (Online) NOTE: this textbook is available via the Einstein library.

PREREQUISITES: Non

SUITABLE FOR 1ST YEAR STUDENTS: Yes

STUDENT ASSESSMENTS: 50% completion of all assignments. 50% attendance and participation in in-class exercises and workshops.

In person attendance is voluntary but expected. Academic credit is only granted to students who attend at least 2/3 of sessions. Students will be encouraged to apply techniques learned in class to data sets they are working with in their other classes or in their labs, and to use class time to improve their existing skills even if covering a topic with which they already have familiarity.

CREDIT HOURS: 1.0