SDSI - Social Data Science, INFO
SDSI414 Data Science Techniques (3 Credits)
An exploration of how to extract insights from large-scale datasets. The course will cover the complete analytical funnel from data extraction and cleaning to data analysis and insights interpretation and visualization. The data analysis component will focus on techniques in both supervised and unsupervised learning to extract information from datasets. Topics will include clustering, classification, and regression techniques. Through homework assignments, a project, exams and in-class activities, students will practice working with these techniques and tools to extract relevant information from structured and unstructured data.
Prerequisite: Minimum grade of C- in MATH115 (or higher) and STAT100; and a minimum grade of C- from INST126 or GEOG276; and a minimum grade of C- from one of the following (INST201, INST301, BSOS233, or SDSB233); and a minimum grade of C- from one of the following (AASP101, ANTH210, ANTH260, ECON200, ECON201, GEOG202, GVPT170, PSYC100, or SOCY100); and a minimum grade of C- from BSOS233 or INST314.
Recommended: Minimum C- in MATH140 and (INST326, BSOS326, or GEOG376).
Jointly offered with: INST414.
Restriction: Must be in Social Data Science program.
SDSI492 Integrated Capstone for Social Data Science (3 Credits)
The capstone provides a platform for Social Data Science students where they can apply a subset of the concepts, methods, and tools they learn as part of the SDSC program to address a problem in data science or information.
Prerequisite: Minimum grade of C- in BSOS326 or SDSB326, INST327, INST366, SURV400, INST462, and INST414 or SDSI414.
Jointly offered with: INST490, INST491.
Restriction: Must be in Social Data Science program; and must have earned a minimum of 90 credits; and permission of INFO-College of Information Studies.