Spring 2019 Course Bulletin

New! CompSci 307D and Renaming CompSci 308

Software Design and Implementation

CompSci 308 has been renamed to Advanced Software Design and Implementation. Despite the name change, CompSci 308 will remain largely the same as in previous semesters.  Advanced was added to its title because it requires students to learn more on their own in order to move faster to prepare for a larger scale final project (8-10 people over 6 weeks).

CompSci 307D is a new course titled Software Design and Implementation. CompSci 307D will be geared to students who want a more guided introduction to the software design which means, ultimately, a smaller final project (4-5 people over 4 weeks). Additionally, CompSci 307 will emphasize code testing in addition to design.

CompSci 307D may be more suitable to students with less programming experience or who want practical ways to better design and code their projects, while CompSci 308 may be more suitable to students with more programming experience or a deeper interest in real world software development.

You cannot take both CompSci 307D and CompSci 308.

Renumbering CompSci 190 (old) → CompSci 116 (current)

Foundations of Data Science

CompSci 116 is an introduction to data science. The course meets twice a week with students working in teams to solve structured data-driven problems in class. This course will prepare you to take CompSci 201.

Course Description:

Given data arising from some real-world phenomenon, how does one turn that data into knowledge and that knowledge into action? Students will learn critical concepts and skills in computer programming and statistical inference in the process of conducting analysis of real-world datasets. The course will be data and project driven. In completing projects, students will consider where data comes from, what it represents and what it does not, what the analyses mean, and how to relate this understanding to the deluge of data and analytics they encounter every day. Students will write computer programs for projects using the Python programming language. The course utilizes a structured form of small group learning that that emphasizes student preparation out of class and application of knowledge in class. Topics include Visualization, Simulation, Testing Hypothesis, Sampling, Estimation, Prediction. No prior programming experience or statistics coursework is required.

Renumbering CompSci 571 (old) → CompSci 671 (current)

Machine Learning

Course Description: Theoretical and practical issues in modern machine learning techniques. Topics include statistical foundations, supervised and unsupervised learning, decision trees, hidden Markov models, neural networks, and reinforcement learning. Minimal overlap with Computer Science 570.

Stats 561D Is Now Cross-listed as CompSci 571

The course Stats 561D titled Probabilistic Machine Learning will be cross-listed as CompSci 571. The old CompSci 571, Machine Learning, will be renumbered to CompSci 671.