Grading/Assessment

This is a cross-listed class between the undergraduate and graduate programs: students taking the course for graduate credit will be required to complete additional (a) questions and coding exercises on problems sets and exams, and (b) DataCamp courses/assessments.

The following assessments will count in equal measure towards the final grade:

Problem Sets

Five problem sets assigned every other week starting from week 2, with the worst grade being omitted. Each problem set will include a mix of theory and coding questions to be completed and submitted online through Canvas within 2 calendar weeks after being assigned.

DataCamp courses; Quiklabs (for GCP/AWS)

You will be asked to complete specific courses, assessments, and modules on selected topics. These involve hands-on coding with various Python libraries and working with big data algorithms in the cloud.

Exams

The course will feature two take-home exams, the first one after 6 weeks of classes and the second after 12 weeks. There is no final exam in the course.

Group Project (3-4 students per group)

The project will be based on real-world data requiring data analysis using mining and machine learning techniques taught in class, and will be assigned in the 6th or 7th week. Each group will be assessed on completion and presentation rubrics including a preliminary, online lightning-round of presentations (around the 11th week) to track intermediate progress, a final 15-20 minutes presentation in-person in the last week (attendance at Camden is mandatory that day), peer review, and a comprehensive final report on the project.