RNA-Seq Data Analysis On-Ramp
6-Week Cohort-Based Course
Runs July 12 to August 23
Make sense of any RNA-seq dataset with this guided computational experience. Designed by a biologist, for biologists.
Biology has been transformed by large-scale, high-dimensional data. And biologists are increasingly expected to have the computational skills to handle this data. But traditional life science training does not equip biologists with these skills. I created this course to address this glaring training gap, starting with RNA-seq data.
Why is RNA-seq data the first data type that biologists should learn to work with?
RNA-seq data is ubiquitous in the life sciences, from neuroscience to cancer biology to immunology. Biologists who learn how to analyze this high-dimensional data instantly unlock petabytes of data to better inform scientific decisions. They become more versatile and valuable scientists in both academic and industry settings.
This course teaches biologists how to make sense of any RNA-seq dataset.
What you'll learn
- How to deal with common issues when preparing RNA-seq data for analysis
- Principal component analysis
- Hierarchical clustering
- Differential expression analysis
- How to frame a biological question as an actionable RNA-seq analysis
What you'll get
- 6 R notebooks of content with 2000+ lines of code, instructions, and exercises
- Answer keys to every exercise
- A coding environment in the cloud that requires zero troubleshooting. It just runs.
- Demonstration of your skills through an independent project (download example below!)
- Bonus: 4 additional R notebooks that challenge you to expand the scope and significance of your project.
- Bonus: Outstanding projects will receive a special shoutout and formal recommendation on LinkedIn by me!
Click on image to download an example of the independent project you will complete by the end of the course
How you'll grow professionally
- No longer rely on others for RNA-seq data analysis
- Know how to strengthen scientific decisions in your research with RNA-seq data
- Add an in-demand computational skill to your resume
- Become more competitive for grad schools, jobs, or promotions
Who this course is for
Biologists working in academia or industry - staff scientists, postdocs, research assistants, etc. - who want to demonstrate growth in computational skills and extend impact at work
Students in a life science PhD program who want strengthen their thesis with a computational component and be more competitive for jobs after graduation
Students in a life science bachelor’s or master’s program who want be more competitive for research lab positions, grad schools, or jobs after graduation
Course details
Pre-requisites
- Familiarity with RStudio
- Familiarity with data manipulation using base R and/or tidyverse
- Familiarity with creating plots using ggplot2
- Familiarity with ChatGPT
Time commitment
- 8-12 hours per week
Class format
- We will work through an R notebook a week, or 6 R notebooks in 6 weeks. All of the instructions along with exercises are written in these notebooks directly.
- We will start off each week with a live session in which I walk through the R notebook to be completed for the week. Every live session will be recorded and made available for asynchronous viewing.
- We will also have a Q&A session in the middle of each week to help people get unstuck.
- An online chat room designated for the course will be available for students to help one another.
- You will upload your completed notebook by the end of the week to the online chat room to show what you have done.
Special features
Live sessions: Weekly live code walk-throughs to help you hit the ground running
Office hours: Weekly open Q&A sessions to keep you on track
Lifetime access: Revisit course content and recordings whenever you need to
Community of peers: Stay accountable and share insights with like-minded professionals in a designated chat room
Real-life LinkedIn promotion: Share concrete examples of what you have learned with your network on LinkedIn
Refund guarantee: Students can request a full refund at any time until the halfway point of the course, no questions asked
Course syllabus
Meet the instructor
Dean started his graduate training at Harvard and is currently a principal scientist in computational biology at Novartis. He has 8 years of experience as a computational biologist, spanning both academia and industry. Dean made the transition from 100% wet-lab to 100% dry-lab work, so he knows firsthand the many obstacles that prevent biologists from quickly learning computational skills.
Dean has 33,000+ followers on LinkedIn, 18,000+ subscribers to his LinkedIn newsletter, and 2,000+ downloads of his free course, Bare Minimum R. A primary focus of his LinkedIn content is how biologists can accelerate their learning of computational skills to adapt to a data-dominant future.
Dean has served on industry panels for Harvard, MIT, and Carnegie Mellon career events. Dean was also an invited speaker, for both professional development and scientific talks, to the Festival of Genomics & Biodata to an audience of 2000+ attendees.
