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Statistical Computing in R

R is the de facto standard for statistical analysis in a wide range of disciplines such as engineering, computer science, genomics, and finance. This two-part workshop will help undergraduates to get started with R’s abilities, ranging from data structure to visualization. Designed for students without any programming experience, this two-part series will better prepare you for introductory statistics courses and quantitative research at Princeton.

Introductory Workshop Part 2

In the second session, you will learn for-loops, conditional statements, and data visualization. From scatter plots to histograms, visualizing data is a crucial step in exploring and analyzing data. And, for-loops and conditional statements enable you to automate time consuming statistical tasks. You will work with a real data set to practice these crucial functions, and in the process, you will learn how to organize your statistical analysis (i.e., a script).

Instructor Bio: Neo Chung, is a 5th year graduate student in Quantitative and Computational Biology. Motivated by large-scale genomic studies, he develops statistical learning methods in R for application to a wide range of biomedical and genomic data. Previously, Neo has worked as an Assistant-in-Instruction for an introductory statistics course, and led workshops in statistical programming at the McGraw Center and J Street Library & Media Center. 
Tuesday, April 8, 7:30-9:30 p.m., Friend 004

To learn more about the workshops and complete an important pre-workshop activity go to this webiste: