STA 102: Intro to Biostatistics

STA 102 is an introductory course in statistics and data science motivated by timely applications from the health sciences, biomedical research, and public health. Students will understand common statistical methods and their suitability in answering specific research questions of interest, conduct rigorous, reproducible analysis using R, interpret results in context and translating them to language accessible to allied health science researchers, and critique statistical usage in the field in order to evaluate data-based claims and decisions.


Course info

Lectures

Section 001      Tue and Thur 3:05p - 4:20p      Soc Sci 136

Labs

Lab 01      Mon 1:25p - 2:40p      Perkins LINK 071 (Classroom 5)

Lab 02      Mon 3:05p - 4:20p      Old Chemistry 003

Lab 03      Mon 4:40p - 5:55p      Perkins LINK 071 (Classroom 5)



Teaching team and office hours

Updated and Effective March 23, 2020: Due to the global coronavirus pandemic, all in-person scheduled meetings and office hours will be switched to Zoom meetings. See Sakai for more details and updated schedule. All times listed are US Eastern Time.

Instructor Yue Jiang T/Th 4:30 - 5:30p Old Chemistry 207
TAs Felicia Guo Th 11a - 1p Old Chemistry 025
Brian Kundinger W 8:30 - 9:30a Old Chemistry 025
Phuc Nguyen W 2 - 3p Old Chemistry 025
Jishen Yin Th 10 - 11a Old Chemistry 203B

Additional instructor office hours are available by appointment.

Texts and software

No textbooks are required for this course, but the following two textbooks may be used as supplemental resources. All lecture notes will be posted to the class website and Sakai page.
Principles of Biostatistics Pagano and Gavreau CRC Press, 2nd Edition, 2018
OpenIntro Statistics Diez, Barr, Çetinkaya-Rundel CreateSpace, 4th Edition, 2019

R is the primary software package for use in STA 102. The free user interface R Studio is highly recommended. All homework and lab assignments are to be completed in R Markdown using the templates posted to the class Sakai page.

This course makes heavy use of the tidyverse, a collection of open source R packages (especially ggplot2 for visualization and dplyr for data manipulation). A free, helpful online resource for R is Wickham and Grolemund, R for Data Science.

Materials

You should bring a fully-charged laptop, tablet with keyboard, or comparable device to every lecture and lab session.