Data Science for Software Engineers: busting software myths with R

Is agile development really the secret to success? Do some languages actually cause more defects than others? This talk describes a series of meaningful lessons that...

Data Science for Software Engineers: busting software myths with R

February 4, 2020

The software engineering world is full of claims about best practices, languages, packages, styles, and workflows, but most software engineering students are never taught how to find, read, and interpret actual evidence on those topics. Is agile development really the secret to success? Do some languages actually cause more defects than others? This talk describes a series of meaningful lessons that explore research in software engineering for the beginner R programmer by teaching students to interpret and replicate research findings while learning meaningful results for their field in addition to common statistical methods. The lessons serve as a primer for software engineers to participate in a data-driven society; from advertising and business to combating misinformation and helping user experience.

A 5 minute presentation in our Lightning Talks series


About the speaker

Yim Register

Yim is a PhD student studying machine learning literacy for self-advocacy. They are working on discovering innovative and joyful ways to teach ML and AI concepts. They believe in a world where we can all participate in, and be critical of, the algorithms in our newsfeeds, political institutions, grocery stores, justice systems, advertising, and more.