## J Street Media Center Workshops in Statistical Computing, Data Mining, Text Analysis and Data Visualization

Interested in statistical computing, data mining, text analysis and/or data visualization? Join us for an upcoming workshop on statistical analysis and data expression in **R** and** LaTeX**.

"**R**" is an open source (FREE) and highly flexible, programming language and software environment" that allows for all the processes listed above. Used in a range of disciplines such as computational biology, quantitative finance, sociology, political science and digital humanities. R has become an increasingly popular program for researchers in academic, corporate and non-profit sectors.

LaTeX is a document markup language and document preparation system that allows the author(s) to focus on content rather than visual presentation. LaTeX allows the writer to set up document elements--chapter, section, table, figure, etc.--and go about the business of writing, while LaTeX handles your content's presentation.

**Our workshops:**

2/13 - Intro to R • 7pm – 10pm **Session is CLOSED. We may attempt a 2nd section, check: www.princeton.edu/jstreet **

**To register: http://ow.ly/hutXF**

2/20 - Data Visualization in R: Social Sciences & Humanities • 7pm - 9pm

**To register: http://ow.ly/huubd**

2/26 - Intro to LaTeX • 7pm – 8pm **Session is CLOSED. We may attempt a 2nd section, check: www.princeton.edu/jstreet**

**To register: http://ow.ly/huvT1**

2/27 - Statistical Programming in R: Physical & Life Sciences • 7pm - 9pm

**To register: http://ow.ly/huukd**

All sessions meet in 204 Wilcox, Wilson College

**Our instructors**:

**Alex Ruder** is a 5th year Ph.D. candidate in the Politics Department at Princeton University. His research interests include the statistical analysis of text data. Alex has previously taught R statistical programming at the undergraduate and graduate level.

**Neo Christopher Chung** is a Ph.D. candidate in Quantitative and Computational Biology at Princeton University. Motivated by modern genomic technologies, he develops statistical methods for high-dimensional data. Neo's diverse teaching experience ranges from biosciences to statistical computing.