In writing about your data wrangling class, I’m am impressed with how easy it was to think of things it allowed me to do/experience even almost a year later. I feel like it was done in such a way that learning about telling a story with data and producing high quality work was easy. There were lots of opportunities to learn from each other and help each other succeed. Then, at the end of it all, I was able to “publish” my final report to LinkedIn, which made me work hard to make it be something professional and worth looking at. The class translated easily into things to add to my professional portfolio. - Colton Kopsa -
Note: You can find the historical order and format of this website (Fall 2017 - Spring 2020) here.
Current trends among industries ranging from Insurance to Manufacturing (as well as many other professions) are pushing the need for data literacy. MATH/CS 335 - Data Wrangling and Visualization provides learners with the technical skills needed to competently work with and visualize data. More specifically MATH/CS 335 will allow learners to use data-driven programming in R for the handling, formatting, and visualization of messy and complex data. Learners will implement data wrangling techniques and the grammar of graphics process in visualizing complex data.
The course is designed to be accessible to all majors as much as possible (introductory programming and statistics are prerequisites). The concepts in this course nicely complement the skills in many other disciplines on campus, such as business and communication, social sciences, and hard sciences. This course is the primary course that pulls many of the varied courses in the data science degree into the cohesive tool set that data scientists will use in industry.
Learners that are driven by curiosity and interested in how decisions are made will be attracted to this course (sometimes called data intuition). Those that have a more empathetic approach to how the world works, people think, and how problems are solved will also have interest. Finally, those that have an eye for visualization and how information is communicated will be benefit from this course[^1].
The primary difference in the type of course participants will be between those that are completing a data science (or statistics) degree and those that are from other degrees on campus but see the need for data interpretation and programming. This class will support students that know they need these analytical/programming capabilities in their respective areas of study and/or employment. With the strong demand for employees with programming and analytics capability across the US and world this class will give participants a significant advantage over others in their respective domains.
Use data-driven programming in R for the handling, formatting, and visualization of messy and complex data. Students will implement data wrangling techniques and the grammar of graphics process in visualizing complex data.
At the completion of this course, successful students will be able to:
The syllabus provides context for the details of how the class will operate and how grades will be earned.
