tfcb_2020

MCB 536: Tools for Computational Biology

This document is the syllabus for this course.

Class schedule

Time: 1:00PM-3:00PM (1-2:40 is official class time, extra 20 minutes at end for additional questions and buffer in case of logistical issues), Tue & Thu, Sep 30 - Dec 11 2020

Location: Zoom

Lecture Date Instructor Topic
1 Oct 1 Kate Hertweck Introduction to course
2 Oct 6 Erick Matsen Introduction to the command line
3 Oct 8 Erick Matsen Intro to the command line (continued)
4 Oct 13 Trevor Bedford Introduction to Git and GitHub
5 Oct 15 Trevor Bedford Introduction to data
6 Oct 20 Phil Bradley Introduction to Python
7 Oct 22 Phil Bradley Intro to Python (continued)
8 Oct 27 Jesse Bloom Data structures and biological analyses using Python
9 Oct 29 Jesse Bloom Data structures/biological analyses in Python (continued)
10 Nov 3 Phil Bradley Modeling and machine learning in Python
11 Nov 5 Phil Bradley Modeling/machine learning in Python (continued)
12 Nov 10 Rasi Subramaniam Visualize data using R/ggplot2
13 Nov 12 Rasi Subramaniam Working with data using R/tidyverse
14 Nov 17 Rasi Subramaniam Principles of data visualization
15 Nov 19 Gavin Ha Introduction to sequencing data
16 Nov 24 Gavin Ha Genomic data in R
17 Dec 1 Erick Matsen Introduction to remote computing
18 Dec 3 Kate Hertweck Remote computing on the command line
19 Dec 8 Rasi Subramaniam Course summary and synthesis
20 Dec 10 Kate Hertweck Capstone project

Materials for each lecture will be available in this repository prior to the class session; the link for each topic will take you to the folder containing materials for that class. Please note that materials are considered in draft form until the beginning of the class session in which they will be presented (or if otherwise indicated).

For further assistance, TAs Will Hannon and Maggie Russell will be available to offer assistance just prior to and during the regular class session.

Homework and grading

Homework Assigned Date Due Date Topic
1 Oct 6 Oct 13 Unix command line
2 Oct 13 Oct 20 Reproducible science, Git and GitHub, Markdown
3 Oct 20 Oct 29 Programming in Python
4 Oct 27 Nov 3 Python analysis, lecture 9
5 Nov 5 Nov 12 Modeling and machine learning in Python
6 Nov 10 Nov 19 Data visualization and manipulation in R
7 Nov 19 Dec 1 Genomic data in R
8 Dec 3 Dec 10 Capstone

Course description

This course is designed to introduce computational research methods to graduate students in biomedical science and related disciplines. We expect students will have little to no previous experience in computational methods. This course provides a survey of the most common tools in the field and you should not expect that completion of the course will make you an expert in any single programming language. Rather, you should be equipped with foundational knowledge in reproducible computational science, and can continue learning relevant tools to suit your research interests.

Course objectives: By the end of the course, students should be able to:

Resources and required materials

Instructors

For general inquiries about this course, please contact khertwec at fredhutch.org

Teaching Assistants