BSMS 222, Fall 2019 Time: Monday (5) and Wednesday (5) Location: Room B125, Hana Science Hall, Seoul Campus
Joon-Yong An, PhD Assistant Professor of Human Genetics School of Biosystem and Biomedical Science, College of Health Science
Coordinating Unit: School of Biosystem and Biomedical Science, College of Health Science Level: Undergraduate
Statistics is fundamental in modern biology because it is integral in the design, analysis, and interpretation of experiments. The main aim of the course is to develop a critical understanding of the foundations of statistical application and data science skills in biological research. The course will introduce concepts and hand-on skills that can help you tackle real-world data analysis challenges.
The aim of this course is to familiarise students with the discipline of biostatistics and basic analytic skills. Students will develop an appreciation of modern application in biostatistics while gaining a detailed understanding of the analytic fundamentals from data modality to programming language. Over the semester, students will be placed into hardcore training in programming exercise. We are learning R, a programming language and free software environment for statistical computing and graphics.
After successfully completing this course you should be able to:
1 Understand the basic concepts in biostatistics.
2 Understand the basic skill sets in R programming language.
3 Become confident in using R for data analysis.
4 Gain hand-on experience in data visualization.
5 Gain hand-on experience in data handling and wrangling.
"Introduction to Data Science", Rafael A. Irizarry
Students are expected to submit their assignment before every class.
Additional resources:
Introduction, R & Rstudio (9/2)
Programming basics part 1 (9/11)
Programming basics part 2 (9/16)
Tidyverse part 2 & importing data (9/23)
Data visualization in practice(10/2)
Data visualization principles (10/7)
Robust summaries (10/9)
Import data and UNIX part1 (10/14)
Import data and UNIX part2 (10/16)
UNIX tutorial (10/28)
UNIX tutorial (10/30)
Probability part 2 (11/6)
Random variables part 1 (11/11)
Random variables part 2 (11/13)
Statistical Inference part 1 (11/18)
Statistical Inference part 2 (11/20)
Statistical Model2 (12/2)
Statistical Model3 (12/4)
Supplementary or placeholder (12/9)
Supplementary or placeholder (12/11)
Students will be asked to submit a assignment for every class. An assignment is that students will write down the R markdown for the class and submit it vis Slack.
Students will be asked to have a quiz for coding test.
Both midterm and final exam will be deciphering R codes and interpreting data tables or figures with statistical terms.