Major Requirements (pre-Fall 2024)

Benchmark I

Each track also requires Benchmark II - a discipline-specific course introducing students to the discipline. (See tracks below for specific courses).

INST126: Introduction to Programming for Information Science or GEOG276: Principles of Python Programming and Geocomputing

BSOS233: Data Science for the Social Sciences 

An introduction to modern methods of data analysis for social scientists. This course emphasizes teaching students who have no previous coding experience how to analyze data and extract meaning in a social science context. Students will gain critical programming skills and learn inferential thinking through examples and projects with real-world relevance.

Core Courses and Capstone

All students take foundational courses in programming, statistics, mathematics, and data science, as well as upper-level courses in database design, data privacy and security, ethics, data sources and manipulation, data visualization, survey fundamentals, and questionnaire design. Students finish the program by completing a required project-based learning capstone. 

INST326 or BSOS326 or GEOG376: Object-Oriented Programming
Python has become the most powerful programming language in advanced statistics and data analytics. It includes expansive packages for data handling and processing, including the latest developments in machine learning, and offers Integrated Development Environments (IDE) for code development, testing, debugging, and graphical representation. In addition, python is deployed on virtually all high performance computing clusters, taking advantage of multi-processing, large memory, and GPU enhanced computing environments. These courses offer a thorough introduction to python, with the option to specialize in either program design (INST326), data science (BSOS326), or geographic information systems (GEOG376).

INST327: Database Design and Modeling
Introduction to databases, the relational model, entity-relationship diagrams, user-oriented database design and normalization, and Structured Query Language (SQL). Through labs, tests, and a project, students develop both theoretical and practical knowledge of relational database systems.

INST366: Privacy, Security and Ethics for Big Data
Evaluates major privacy and security questions raised by big data, Internet of things (IoT), wearables, ubiquitous sensing, social sharing platforms, and other AI-driven systems. Covers history of research ethics and considers how ethical frameworks can and should be applied to digital data.

INST414: Data Science Techniques
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.

INST447: Data Sources and Manipulation
Examines approaches to locating, acquiring, manipulating, and disseminating data. Imperfection, biases, and other problems in data are examined, and methods for identifying and correcting such problems are introduced. The course covers other topics such as automated collection of large data sets, and extracting, transforming, and reformatting a variety of data and file types.

INST462: Introduction to Data Visualization
Exploration of the theories, methods, and techniques of visualization of information, including the effects of human perception, the aesthetics of information design, the mechanics of visual display, and the semiotics of iconography.

SURV400: Fundamentals of Survey and Data Science
The course introduces the student to a set of principles of survey and data science that are the basis of standard practices in these fields. The course exposes the student to key terminology and concepts of collecting and analyzing data from surveys and other data sources to gain insights and to test hypotheses about the nature of human and social behavior and interaction. It will also present a framework that will allow the student to evaluate the influence of different error sources on the quality of data.

SURV430: Fundamentals of Questionnaire Design
Introduction to the scientific literature on the design, testing and evaluation of survey questionnaires, together with hands-on application of the methods discussed in class.

INST492: Capstone
The capstone provides a platform for students where they can apply a subset of the concepts, methods, and tools they learn as part of the Information Science program to addressing an information problem or fulfilling an information need.