About Nanocourses

2018 Fall Semester

Course Dates
Introduction to R for Beginners, Level 1 July 12, 19
Computational Image Analysis September 6, 7, 10, 11
Python, Level 1 October 19, 26, November 2, 9
MatLab November 6, 7
Machine Learning TBA

Upcoming Courses


Introduction to R for Beginners, Level 1

July 12th & 19th, 2018

9:00 am – 5:00 pm both days, Room NB2.100A

Do you want to be to do simple statistical analyses yourself? Do you find yourself spending time and effort generating the same plots and statistics for each project? R is a freely available language and programming environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modeling, statistical tests, time series analysis, classification, clustering, etc.

This training is on two consecutive Fridays from 9am to 5pm. You will need to bring a laptop computer with the latest version of R-Studio installed. If you already have R-studio, please make sure you have the latest version of R installed.

Training Topics:

Course Size: 15 students

Academic Credit: 1 credit hour special topics


Previous Nanocourses


Genomics Analysis, May 2018

May 30th-June 1st, 2018

9:00 am – 5:00 pm all days
Genomic Analysis: May 30 - 31 in Room NB2.100A
Immune Profiling: June 1 in Room NL3.120

Are you interested in genetic variation in cancer or inherited disease? Are you intested in looking for genetic risk loci? This course cover variate detection, annotation and visualization.

Topics covered will include:

Course Size: 15 students

Academic Credit: 1 credit hour special topics


Gene Expression and Regulation

June 8th & 15th, 2018

9:00 am – 5:00 pm both days, Room NB2.100A

Are you interested in gene expression and regulation? This course is designed to cover NGS sequence analysis to determine gene expression (RNASeq) and gene regulation (ChIPSeq and ATACSeq).

Course Size: 15 students

Academic Credit: 1 credit hour special topics


NCBI Workshops presented by NIH

March 28th & 29th, 2018

Location: NG3.112

NCBI offers a series of modular workshops on related set of NCBI resources. Each module consists of a 3-hour largely hands-on session emphasizing a different set of NCBI resources specifically designed for the UTSW community. Please bring your laptop computer.

Topics will include: - NCBI Tools for NIH Grantees and those wanting to be Grantees - NCBI Resources for Human Clinical/Phenotype Variation Research - NCBI Resources for Clinical Microbiology Research - NCBI Tools for NIH Grantees and those wanting to be Grantees

Course Size: Unlimited, no registration.

Academic Credit: Students who sign in for all sessions can receive 1 credit hour special topics


Machine Learning I

March 8th & 9th, 2018

Are you interested in machine learning? This course is an introductory course for students to learn the basics. Programming experience in Python is mandatory.

Course Size: 15 students

Academic Credit: 1 credit hour special topics


Introduction to Python I

February 27th & 28th, 2018

9:00 am – 5:00 pm both days, Room NB2.100A

This nanocourse will introduce Python for scientific computing. Python is an open-source, fun, easy to learn, and powerful programming language. With deep community support and wide ranging deployment across many domains, Python is a worthy tool for projects large and small that any computational scientist should keep on hand.

Topics for this two day (all-day) course will include:

The course will be interactive, with lectures followed by hands-on learning and exercises. No previous programming experience is necessary. Familiarity with basic programming/scripting concepts is helpful.

Course Size: 15 students

Academic Credit: 1 credit hour special topics


Introduction to R for Beginners, Level II

Two consecutive Thursdays, January 11th & 18th, 2018

9:00 am – 5:00 pm both days, Room NB2.100A

Have you already taken the R for Beginner 1 and now want to build up your skills? Do you want to create interactive plots or perform complicated genomics analsysis with Bioconductor? Do you understand dataframes, matries and vectors but need more practice on more sophisticaled analysis? In this continuation of the R Beginner 1 Nanocourse, we will review what you learned in R Beginner Level 1 and demonstrate how to merge and search excel sheets, create small scripts for repetitive tasks, generate interactive plots and use bioinformatics packages from Bioconductor. Students will also have a chance to present their own data challenges and come up with analysis strategies.

You will need to bring a laptop computer with the latest version of R-Studio installed. If you already have R-studio, please make sure you have the latest version of R installed.

Training Topics:

Course Size: 15 students

Academic Credit: 1 credit hour special topic

Registration for this course is closed. Please contact Rebekah Craig if you would like to be notified of future training opportunities.


Computational Image Analysis

September 14th - 15th and 18th - 19th, 2017

This nanocourse offers an introduction to state-of-the-art computer vision methods to convert image data into quantitative information. The four-day intensive course covers image analysis fundamentals using theory lectures and hands-on computer exercises using popular image analysis programs such as ImageJ, CellProfiler and Matlab. Biomedical scientists will gain the background to (1) search for and evaluate existing image analysis software, and (2) start devising their own image analysis pipeline/software. The course will also include and "image analysis therapy" session where the class can brainstorm about each other's image analysis problems.

The course is open to any interested person at UTSW, provided they utilize imaging and are interested in computational image analysis for their research. Some background in mathematics and programming is a plus, e.g., completion of the Mathematical Foundations of Quantitative Biology course and the Matlab bootcamp.

Time: This is a four-day course that runs from 9 a.m. to 5 p.m. each day

Class Location: Room NG3.202

Training Topics:

Course Size: 15 students

Academic Credit: 2 credit hours special topics

This course is also part of the Computational and Systems Biology curriculum


Introduction to R for New Users

July 21st and July 28th, 2017

Do you want to be to do simple statistical analyses yourself? Do you find yourself spending time and effort generating the same plots and statistics for each project? R is a freely available language and programming environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modeling, statistical tests, time series analysis, classification, clustering, etc.

This training is on two consecutive Fridays from 9am to 5pm. You will need to bring a laptop computer with the latest version of R-Studio installed. If you already have R-studio, please make sure you have the latest version of R installed.

Class Location: NL6.125

Training Topics: