Learn computing basics for cancer research. Explore shared resources, etiquette, and decision-making in this 11-hour beginner course.
Learn computing basics for cancer research. Explore shared resources, etiquette, and decision-making in this 11-hour beginner course.
This course provides researchers and investigators with a comprehensive understanding of computing basics and various computing options for cancer informatics. Designed for those new to informatics work, it covers fundamental computing terminology, concepts about computer systems, shared computing resources, and their appropriate use. The curriculum explores computing resources specific to cancer research and guides participants in making informed computing decisions. Through seven modules, learners will gain insights into how computers work, data storage and processing, file sizes, and computing capacity. The course also introduces shared computing etiquette and research platforms designed for cancer researchers. By the end, participants will be equipped to make better-informed decisions about computing resources for their cancer research projects.
4.6
(11 ratings)
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Instructors:
English
What you'll learn
Understand basic computing terminology and concepts
Learn how computers and shared computing resources work
Explore differences between various computing resource options
Understand appropriate etiquette for shared computing resources
Discover computing resources designed specifically for cancer research
Gain insights into making informed computing resource decisions
Skills you'll gain
This course includes:
4 Hours PreRecorded video
Access on Mobile, Tablet, Desktop
FullTime access
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There are 7 modules in this course
This course offers a comprehensive introduction to computing for cancer informatics, designed for researchers with limited to intermediate experience in informatics research. It covers fundamental computing concepts, shared computing resources, and their application in cancer research. The curriculum is structured to provide learners with a solid understanding of how computers and computing systems work, the differences between various computing resources, and the etiquette for using shared resources. Participants will explore computing resources specifically designed for cancer research and learn important considerations for making computing decisions. The course aims to equip research leaders with the knowledge to effectively manage and analyze large datasets from multiple sources, addressing one of the key challenges in cancer informatics.
Welcome
Module 1 · 19 Minutes to complete
Basic Building Block of Computers
Module 2 · 2 Hours to complete
Binary data to computations
Module 3 · 2 Hours to complete
Computing Resources
Module 4 · 1 Hours to complete
Shared Computing Etiquette
Module 5 · 1 Hours to complete
Research Platforms
Module 6 · 1 Hours to complete
Data Management Decisions
Module 7 · 2 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Pioneering Data Science Education and Computational Biology Innovation
Carrie Wright, PhD, serves as a Senior Staff Scientist at Fred Hutchinson Cancer Center and holds an affiliated faculty position at Johns Hopkins Bloomberg School of Public Health, where she focuses on making data science and computational biology more accessible to diverse audiences. Her expertise spans multiple domains, teaching courses including "AI for Decision Makers," "AI for Efficient Programming," "Avoiding AI Harm," "Best Practices for Ethical Data Handling," "Data Management and Sharing for NIH Proposals," and "Write Smarter with Overleaf and LaTeX." Her distinguished career includes significant contributions as a former Assistant Scientist in Biostatistics at Johns Hopkins and postdoctoral research at the Lieber Institute for Brain Development, where she studied genetic mechanisms in psychiatric disease. As a member of the Open Case Studies team, the Genomic Data Science Community Network, and chair of the ITCR OPEN Group, she demonstrates her commitment to advancing science, medicine, and social justice through accessible data science education. Her innovative work includes co-founding the LIBD rstats club and teaching at various institutions, including the Baltimore Underground Science Space and Johns Hopkins Center for Talented Youth.
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4.6 course rating
11 ratings
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