Abstract

Software engineering principles are frequently mentioned as a solution to data science's productivity problem. Unfortunately, rarely in a comprehensive format to be actionable or adopted for data-intensive use. In this talk, I will present the Clear Architecture framework that enables practitioners to structure their projects and manage changes throughout their lifecycles. The audience will also learn about a minimum set of programming concepts to make this a reality. The key takeaway is that, as a data scientist, you can take care of your codebase with only a few techniques and a little effort.

Laszlo SragnerGiang

Founder @ Hypergolic

Kokchun is a data scientist and teacher who strives to inspire people to pursue the beauty of programming and mathematics. He is constantly sharpening his technological and pedagogical skills to do this successfully. His pedagogical strategy is based on a combination of structure from special pedagogy and clear visualizations for storytelling. Currently, he works at IT-Högskolan, where he built the data science program with industry partners.