Fall 2024
INF 385T.09 Special Topics in Information Science: Data Wrangling
DESCRIPTION
Learning key data wrangling maneuvers in abstract and implementations in SQL, Excel, R Tidyverse, and Python Pandas. Maneuvers in data transformations include Nest, Pivot, Mutate (inc. separate/unite), Group/Summarize and Rectangling. Projects include working with "wild caught" data datasets (usually CSV or JSON) and computational notebook environments (e.g., iPython, Jupyter, Rmarkdown, Quarto). Fall 2024 has changes from previous syllabus now that we have Database Design and Introduction to Programming. Nonetheless, the previous syllabus is still useful as it links to course materials that show the teaching approach and type of assignments. http://howisonlab.github.io/datawrangling/#Schedule_of_classes
COURSE NOTES
I hope to revise the data wrangling course this upcoming semester. The plan is to focus on learning key data wrangling maneuvers in SQL, Excel, R Tidyverse, and Pandas. So the data modeling parts would also include tidydata concepts. This will make the course overlap less with Database Management (that Ying Ding is teaching) and the introduction to programming courses now available in the school. Projects will largely be the same (working with "wild caught" CSV datasets). Previous syllabus still useful: http://howisonlab.github.io/datawrangling/#Schedule_of_classes
PREREQUISITES
Graduate standing.
RESTRICTIONS
Restricted to graduate students in the School of Information through registration periods 1 and 2. Outside students will be permitted to join our waitlists beginning with registration period 3.