Data Science, MS—Align Program
The innovative ALIGN bridge program to the Master of Science in Data Science is designed for students with a BS/BA degree from all backgrounds. During the first semester of year one, students are expected to take foundational courses in computer science fundamentals, as well as a course in data structures/discrete mathematics. During their second semester, students will take coursework in programming for data science, as well as linear algebra and probability. Upon successful completion of the second semester, students in good standing will matriculate into the Master of Science in Data Science program.
The Master of Science in Data Science program is designed to give students a comprehensive framework for reasoning about data. Students will engage in extensive coursework intended to develop depth in data collection, storage, retrieval, manipulation, visualization, modeling, and interpretation. Students will also be able to choose elective courses from a variety of offerings in Khoury, the College of Engineering (COE), and throughout the campus to explore areas that generate data or specialized data science applications. Successful program graduates will be well-positioned to attain data scientist and data engineer positions in a fast-growing field or to progress into doctoral degrees in related disciplines.
Students who successfully complete the MS degree will be able to:
- Collect data from numerous sources (databases, files, XML, JSON, CSV, and web APIs) and integrate them into a form in which the data is fit for analysis.
- Use R and Python to explore data, produce summary statistics, perform statistical analyses; use standard data mining and machine-learning models for effective analysis.
- Select, plan, and implement storage, search, and retrieval components of large-scale structured and unstructured repositories.
- Retrieve data for analysis, which requires knowledge of standard retrieval mechanisms such as SQL and XPath, but also retrieval of unstructured information such as text, image, and a variety of alternate formats.
- Manage, process, analyze, and visualize data at scale. This outcome allows students to handle data where conventional information technology fails.
- Match the methodological principles and limitations of machine learning and data mining methods to specific applied problems and communicate the applicability and the advantages/disadvantages of the methods in the specific problem to nondata experts.
- Carry out the full data analysis workflow, including unsupervised class discovery, supervised class comparison, and supervised class prediction; summarize, interpret, and communicate the analysis of results.
- Organize visualization of data for analysis, understanding, and communication; choose appropriate visualization method for a given data type using effective design and human perception principles.
- Develop methods for modeling, analyzing, and reasoning about data arising in one or more application domains such as social science, health informatics, web and social media, climate informatics, urban informatics, geographical information systems, business analytics, bioinformatics, complex networks, public health, and game design.
Complete all courses and requirements listed below unless otherwise indicated.
Students should refer to the course numbering table for graduate course leveling.
ALIGN Bridge Coursework
Students will be required to complete two or more of the following bridge courses to be determined by faculty mentor.
A grade of B or higher is required in each course.
and CS 5003
|Intensive Foundations of Computer Science|
and Recitation for CS 5001
|CS 5002||Discrete Structures||4|
|Programming for Data Science|
|DS 5010||Introduction to Programming for Data Science||4|
|Additional ALIGN Coursework|
|DS 5020||Introduction to Linear Algebra and Probability for Data Science||4|
A cumulative GPA of 3.000 or higher is required in the following core courses:
|Complete 4 semester hours from the following:||4|
|Fundamentals of Computer Engineering|
|Data Management and Processing|
|DS 5110||Introduction to Data Management and Processing||4|
|Machine Learning and Data Mining|
|DS 5220||Supervised Machine Learning and Learning Theory||4|
|DS 5230||Unsupervised Machine Learning and Data Mining||4|
|Presentation and Visualization|
|DS 5500||Information Visualization: Applications in Data Science||4|
|Complete 12 semester hours from the following:||12|
|College of Computer and Information Science|
|Foundations of Artificial Intelligence|
|Reinforcement Learning and Sequential Decision Making|
|Database Management Systems|
|Natural Language Processing|
|Large-Scale Parallel Data Processing|
|Empirical Research Methods|
|Fundamentals of Cloud Computing|
|Special Topics in Artificial Intelligence|
|Special Topics in Database Management|
|Special Topics in Data Science|
|College of Engineering|
|Time Series and Geospatial Data Sciences|
|Special Topics in Civil Engineering|
|Advanced Computer Vision|
|Advanced Machine Learning|
|Data Mining in Engineering|
|Statistical Methods in Engineering|
|Designing Advanced Data Architectures for Business Intelligence|
|College of Social Sciences and Humanities|
|Dynamic Modeling for Environmental Decision Making|
|Big Data for Cities|
|Geographic Information Systems for Urban and Regional Policy|
|Urban Theory and Science|
|Advanced Spatial Analysis of Urban Systems|
|Perspectives on Social Science Inquiry|
|D'Amore-McKim School of Business|
|Business Analytics Fundamentals|
|College of Science|
|Complex Networks and Applications|
|Network Science Data|
|Bouvé College of Health Sciences|
|Introduction to Epidemiology|
|Biostatistics in Public Health|
|College of Arts, Media and Design|
|Game Design and Analysis|
|Data-Driven Player Modeling|
Program Credit/GPA Requirements
40–48 total semester hours required
Minimum 3.000 GPA required
Students taking electives worth less than 4 credits (i.e., Bouvé courses) should enroll for an accompanying data science project course in the same semester to bring the cumulative credits to 4. In order to earn this additional credit, students are expected to work with faculty to design an additional project in line with the curricular aims of their chosen elective and the data science core learning outcomes.