The MSCS with a specialization in Data Science (MSCS-DS) program provides the skills to develop computer solutions that require expertise in data science. This program is unique in that students who complete the program receive both an MSCS degree and a specialization within data science. This combination is very attractive as technology companies are looking for developers that have experience in data science. Students complete both a set of core courses that part of all MSCS degrees at Seattle University plus a set of data science courses. The program culminates in a real-world capstone data science project.
Required MSCS Courses (22 credits)
Data Science Specialization Courses (26 credits)
Choose one of the following three data science electives (5 credits):
Capstone project (5 credits): CPSC 5830 - Data Science Capstone Project
Data Science Course Descriptions
MATH 5315 - Mathematical Foundations of Data Science (3 credits)
Introduction to the mathematical and statistical concepts a student in the Master of Science in Computer Science / Data Science Specialization will be exposed to in their coursework. The concepts belong to the areas of probability, statistics, and linear algebra.
CPSC 5305 - Introduction to Data Science (2 credits)
This course introduces the field of data science. Intended for students planning to take more specialized technical courses in the area of data science, this course provides an overview of the field: the methodology by which data-intensive problems are identified, defined, solved, and operationalized. In addition, the student will be exposed to common problems and technologies both in the analytic and systems areas within the field, and will get hands-on experience with common tools and techniques. Finally, the ethical, privacy, and security implications of building data-intensive applications will be discussed.
CPSC 5310 - Machine Learning (5 credits)
This course introduces machine learning foundations, concepts, and algorithms and their applications in analyzing massive amounts of data to find interesting patterns that can be used to assist decision making or provide predictions. Topics include decision trees, Bayesian classification, clustering, sequence clustering, association rules, time series analysis, neural networks. Students are expected to analyze real-world data.
CPSC 5320 - Visual Analytics (3 credits)
This course introduces techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science.
CPSC 5330 - Big Data Analytics (3 credits)
This course covers the Hadoop architecture and the Hadoop ecosystem of tools. Students will learn to apply Hadoop and related Big Data technologies such as MapReduce, and Spark in developing analytics and solving problems that process vast quantities of data.
CPSC 5340 - Text Processing and Search (5 credits)
This course addresses the formal and practical techniques for building Information Retrieval (IR) and text processing systems, including source and query models, evaluation, ranking, and relevance issues, and efficient indexing and retrieval through search engines.
CPSC 5350 - Social Media Analytics (5 credits)
This course introduces concepts and algorithms that are required for analysis of data available on social media. This course introduces Web techniques, social networks and analysis, network analysis and graph theory, information extraction, link analysis, and Web mining, to study emerging problems with social media.
CPSC 5600 - Parallel Computing (5 credits)
Fundamentals of parallel computing with an emphasis on parallel programming and algorithms. Parallel algorithmic analysis and development using divide and conquer, map and reduce, and data decomposition. Parallel computing implementations and performance factors.
CPSC 5830 - Data Science Capstone Project (5 credits)
Teams of three to four students solve a data science problem from definition to implementation and presentation. Problems may be proposed by the student teams, the instructor, or an external industrial partner. Students will be expected to define the problem scope, obtain and prepare data sets, perform analyses and/or build big-data processing systems, evaluate and present results effectively.
Frequently Asked Questions
Yes, students need to have an undergraduate degree in computer science or closely related field. Students that do not have this background can take the Certificate in Computer Science Fundamentals (link to https://www.seattleu.edu/scieng/computer-science/certificates/cs-fundamentals/) and then join the MSCS-DS program.
Students need to have completed three quarters of Calculus before entering the program. In the program, students need to take MATH 5315. Students that have taken a probability course (a reasonably advanced course with a calculus prerequisite) and a linear algebra course can be waived from MATH 5315.
Since the MSCS-DS curriculum has more specific requirements, it requires more advanced planning. Students interested in the fast track for MSCS-DS should consult their advisor during their junior year.