A new course Data Science Programming Methods was first taught in the Spring 2019 term. It will be offered again in this fall 2019 term. The instructor is Dirk Eddelbuettel who also designed the course.
Statistics and Data Science are focused on making sense of data – and face an
ever-increasing demand for their work. Yet at the same time, data sets increase in size
and scope. Proper tooling is essential to meet these challenges, and as applied work in
data analysis is in effect applied computational work, we will learn the computational
tools and programming methods to meet these data science challenges. Proficiency at
the shell, familiarity with
git version control, sufficient understanding of SQL, and
of course acquiring actual expertise in R programming are the goals of this course to
prepare students for the coming computational challenges. We will use RStudio Cloud
instances so students are not required to install and maintain all required components.
Prior programming experience (in R or another language) will certainly be helpful, but
is not a formal requirement for taking the course.
Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data.
This courses introduces key concepts for computational literacy in a data science context:
This course is both fast paced. We cover a considerable amount of material. It may still be a little rough at the edges as it is only second iteration of this course — and delivered online.
Note that the CBTF tests generally require an on-campus presence.