This is an applied course for data scientists with little-to-no programming experience who wish to harness growing digital and computational resources. The focus of the course is on generating reproducible research through the use of programming languages and version control software. Major emphasis is placed on a pragmatic understanding of core principles of programming and packaged implementations of methods. Students will leave the course with basic computational skills implemented through many computational methods and approaches to data science; while students will not become expert programmers, they will gain the knowledge of how to adapt and expand these skills as they are presented with new questions, methods, and data.
By the end of the course, students will:
tidyversepackages (e.g. loops, conditional statements, user-defined functions)
Benjamin Soltoff is Lecturer in Information Science at Cornell University. He is a political scientist with concentrations in American government, political methodology, and law and courts. Additionally, he has training and experience in data science, big data analytics, and policy evaluation. He currently teaches courses in data science, research design, data communication, and web design.