Course Offerings

In this class we'll use history and readings to not only understand the current state of the information field, but how we got here. Seeing that, students will understand that they have the power to shape and improve the information field. Students will also work in in-class teams to cement ideas and connect to other students in the class. We work to answer the question of why UX designers, archivists, AI ethicists, and librarians are all in the same graduate program. Ultimately the goal is to connect, understand, and inspire.

As the culminating experience of the MSIS program, INF 388L allows every student to apply their unique skillsets and learnings to a professional project that is focused on a real-world problem or initiative. The course is designed to support your capstone journey throughout the semester as you work on your project with your project Field Supervisor. As an asynchronous course, students and instructors communicate via Canvas and various discussion prompts. Progress in the course is measured through updates and documents submitted directly to Canvas. During the semester, time is allotted for 1-on-1 meetings between student and instructor, and for small group meetings, as needed. Summary of Course Goals 1. Deliver a professional-level project/solution to showcase your knowledge, skills, and abilities. 2. Take direction and feedback from a supervisor working in your applied field of study. 3. Strengthen communication and presentation skills. 4. Manage expectations around project goals, schedule, and deliverables.

The class focuses on developing problem solving skills using Python as a programming language. Starting from procedural function development, we also explore object-oriented techniques, and discuss simple data structures that are often used in software development. The students usually do a few programming assignments, take a midterm, and submit a final project.

This class explores various data science models, both traditional and the state of the art techniques. The course is designed to provide mathematical and computational basis such as Linear Algebra, Optimization techniques, and probabilistic modeling for different types of machine learning models. The goal of the class is provide a foundational basis for data science techniques. The class focuses on PSETs and a final data science project.

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