AI for Efficient Programming (The Course)
Course Info
This course includes 1 attempt.
This course on AI for efficient programming explores the use of AI large language models such as ChatGPT, Bard, and others and their potential benefits and challenges. Through examples and hands-on activities, students will develop an understanding of the ways in which AI can speed up software development tasks and free up time for more creative and strategic work. By the end of the course, students will be equipped to navigate the rapidly changing landscape of software development and use AI chatbots in a way that maximizes benefits and efficiency while limiting harm as much as possible.
CS, CW, and HB were supported by the National Cancer Institute (NCI) under Grant UE5CA254170. AMH and EMH were supported by the AnVIL Project through cooperative agreement awards from the National Human Genome Research Institute with cofunding from OD/ODSS to the Broad Institute (U24HG010262) and Johns Hopkins University (U24HG010263).
Course Material
- About this Course
- 1 Introduction
- Quiz: Introduction10 attempts allowed
- 2 Ethics of Using AI
- Quiz: Ethics of Using AI10 attempts allowed
- 3 Writing Code
- Quiz: Writing Code10 attempts allowed
- 4 Refactoring Code
- Quiz: Refactoring10 attempts allowed
- 5 Annotating Your Code
- Annotation with AI10 attempts allowed
- 6 Understanding Unfamiliar Code
- Quiz: Understanding Unfamiliar Code10 attempts allowed
- 7 AI for Bioinformatics
- 8 Languages and Cloud Computing
- About the Authors
- References
Instructors
Ava Hoffman is a Senior Staff Scientist at Fred Hutchinson Cancer Center where she is a founding member of the Fred Hutch Data Science Lab. She is also Associate Faculty at the Johns Hopkins Bloomberg School of Public Health where she teaches Intro to R programming and Baltimore Community Data Science. You can learn more about Ava at her website: avahoffman.com
Carrie Wright is a Senior Staff Scientist at the Fred hutchinson Cancer Center and a member of the Fred Hutchinson Data Science Lab. Previously Carrie was an Assistant Scientist in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and a faculty member of the Johns Hopkins Data Science Lab. Her work focuses on making data science and informatics more approachable and accessible. She is also a faculty member of the open case studies project, the Informatics Technology for Cancer Research (ITCR) Training Network (ITN), and a co-founder of the LIBD rstats club, a community designed to encourage others to learn more about R programming and statistics. Learn more about Carrie at https://carriewright11.github.io/.
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