Data Science Training Program
Friday, February 28, 2020

Alex Pico

Gladstone Institutes

Scooter Morris

UC San Francisco

This course is a complement to “Introduction to Network Biology and Cytoscape” offered in October 2019. Following a brief review of the key concepts of network analysis, you will embark on a deep-dive into data visualization and advanced Cytoscape features. You will work through three prepared use-cases demonstrating various omics data types and strategies, and have a chance to apply what you learn to your own data.

By the end of this course you will:

  • Know when and how to use Cytoscape in your research
  • Be able to handle multiple data types in Cytoscape
  • Know how to transform and merge network data from multiple sources
  • Master customized data visualization
  • Be able to navigate the greater Cytoscape ecosystem

Prerequisites: You should already be familiar with Cytoscape and the fundamentals of network analysis (see prior course).

Bring a laptop with the latest version of Cytoscape installed.

Details

Dates
February 28, 2020
Time
1:00-5:00pm PST
Location

UCSF Mission Hall, Room 1407
Contact(s)





The Gladstone Data Science Training Program was started in 2018 to provide trainees with learning opportunities and hands-on workshops to improve their skills in bioinformatics and computational analysis. This program offers a series of workshops throughout the year to enable trainees to gain new skills and get support with their questions and data.

Getting There

UCSF Mission Hall, Room 1407

Diversity, Equity, and Inclusion

At Gladstone, we are committed to providing events and professional development activities that resonate with our community’s diverse members. Our goal is to develop creative programming that encompasses a wide variety of ideas and perspectives to inspire, educate, and engage with everyone within our walls.

We want to effect positive change through our events and activities by providing a platform for discussions on important topics related to increasing diversity and inclusiveness in the sciences.