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.

Learning Outcomes

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.

Fundamentals
CS 5001
and CS 5003
Intensive Foundations of Computer Science
and Recitation for CS 5001
4
Discrete Structures
CS 5002Discrete Structures4
Programming for Data Science
DS 5010Introduction to Programming for Data Science4
Additional ALIGN Coursework
DS 5020Introduction to Linear Algebra and Probability for Data Science4

Core Requirements

A cumulative GPA of 3.000 or higher is required in the following core courses:

Algorithms
Complete 4 semester hours from the following:4
Algorithms
Fundamentals of Computer Engineering
Data Management and Processing
DS 5110Introduction to Data Management and Processing4
Machine Learning and Data Mining
DS 5220Supervised Machine Learning and Learning Theory4
DS 5230Unsupervised Machine Learning and Data Mining4
Presentation and Visualization
DS 5500Information Visualization: Applications in Data Science4

Electives1

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
Computer/Human Interaction
Web Development
Natural Language Processing
Information Retrieval
Large-Scale Parallel Data Processing
Empirical Research Methods
Fundamentals of Cloud Computing
Special Topics in Artificial Intelligence
Thesis
Project
Special Topics in Database Management
Special Topics in Data Science
College of Engineering
Time Series and Geospatial Data Sciences
Special Topics in Civil Engineering
Computer Vision
High-Performance Computing
Information Theory
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
Applied Econometrics
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
Research Design
Quantitative Techniques
D'Amore-McKim School of Business
Business Analytics Fundamentals
College of Science
Complex Networks and Applications
Statistical Physics
Computational Physics
Network Science Data
Bouvé College of Health Sciences
Introduction to Epidemiology
Biostatistics in Public Health
Social Epidemiology
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