MSDS Curriculum & Courses

Faculty teaching

The University of Arizona MS in Data Science, ranked the #9 program in the nation by Fortune, empowers students with the in-demand skills they need to transform data into actionable insights through data collection, exploration, analysis and manipulation.

The STEM-designated degree, which is offered at our main campus in Tucson, Arizona and online, requires 30 total units and can typically be completed in 18 months for full-time students.


MSDS Student Competencies

Students who graduate from the University of Arizona MS in Data Science will have the following competencies:

Competency 1

Students will establish the ability to exercise the four key techniques of computational thinking: decomposition, pattern recognition, abstraction, and algorithms.

Competency 2

Students will obtain the skills of collecting, manipulating, and analyzing different types of data at different scales, and interpreting the results properly.

Competency 3

Students will acquire the skills to communicate the results of their work to interdisciplinary teams, using appropriate visualizations, multi-media, or artistic performance.

Competency 4

Students will demonstrate an understanding of information and data ethics, including ethical and legal requirements of data privacy and security, and the values of the information fields to serve diverse user groups.


Master's Plan of Study

As an MSDS student, you will work with your faculty advisor to develop a Master’s Plan of Study during your first few months in the program. The Plan of Study, which must be submitted to the Graduate College no later than your second semester in the program, identifies:

  1. Courses you intend to transfer from other institutions (if any)
  2. Courses already completed at the University of Arizona which you intend to apply toward the graduate degree (if any)
  3. Additional coursework to be completed to fulfill degree requirements

The Plan of Study must have the approval of the director of graduate studies before it can be submitted to the Graduate College.

Questions about GradPath should be directed to Holly Brown, director of graduate programs and global student services, at brownhb@arizona.edu.

Course Transfers

A maximum of 6 units can be transferred toward your MSDS from other institutions. Please reach out to the program advisor over email and provide (1) the list of courses that you’d like to transfer, (2) the closest course to each of the potential transfers in the list of pre-approved courses and (3) the syllabus for each of the courses that you are intending to transfer.


Curriculum

The following curriculum is effective Fall 2024. View the curriculum applicable to MSDS students admitted for Fall 2023, Spring 2024 or Summer 2024 in the appropriate section below.

Students have the choice of completing the MSDS alone or using sets of courses in order to attain one or more graduate certificates at the same time. Please see corresponding units' web pages for more information about their graduate certificates (e.g., Linguistics). Learn more about the Graduate Certificate in Foundations of Data Science or visit our Graduate Certificates page for more information about all College of Information Science graduate certificates.

Any non-core courses with the INFO prefix or out-of-department courses are also considered electives.

Elective courses include:

AREC 548: Introduction to Statistical Methods in Economics
AREC 549: Applied Econometric Analysis
AREC 559: Advanced Applied Econometrics
BIOS 511: Healthcare Data Science
BIOS 576D: Data Management and the SAS Programming Language
BIOS 684: General Linear and Mixed Effects Models
BIOS 686: Survival Analysis
COGS 500: Computational Skills for the Research Lab
CSC 577: Introduction to Computer Vision
CSC 583: Text Retrieval and Web Search
CSC 585: Algorithms for Natural Language Processing
ECE 509: Cyber Security - Concept, Theory, Practice
ECE 523 Engineering Applications of Machine Learning and Data Analytics
ECE 524: Fundamentals of Cloud Security
ECE 578: Fundamentals of Computer Networks
ECE 579: Principles of Artificial Intelligence
ECE 696B: Advanced Topics in Electrical Engineering
EDP 641: Selected Applications of Statistical Methods
ENGR 595A: Science, Health and Engineering Policy and Diplomacy
GIST 603A Geog. Info. Systems Programming/Automation
GIST 603B: WebGIS
GIST 604B Open Source GIS
HED 612: Introduction to Multivariate Regression and Quantitive Program Evaluation
HED 696C Topics in Higher Education
HWRS 528: Fundamentals: Systems Approach to Hydrologic Modeling
HWRS 642: Analysis of Hydrologic Systems
INFO 514: Computational Social Science
INFO 515: Organization of Information
INFO 521: Introduction to Machine Learning
INFO 525: Algorithms for Games
INFO 529: Applied Cyberinfrastructure Concepts
INFO 531: Data Warehousing and Analytics in the Cloud
INFO 536: Data Science and Public Interests
INFO 555: Applied Natural Language Processing
INFO 556: Text Retrieval and Web Search
INFO 557: Neural Networks
INFO 570: Data Base Development and Management
INFO 571: Introduction to Information Technology
INFO 578: Science Information and its Presentation
INFO 579 Database Design in SQL
INFO 580: Data Standards for the Semantic Web
INFO 587: Information Seeking Behaviors
ISTA 410/510: Bayesian Modeling and Inference
LING 508: Computational Techniques for Linguists
LING 539: Statistical Natural Language Processing (Cross-listed: INFO and CSC 539)
LING 578 Speech Technology
LING 581: Advanced Computational Linguistics
LING 582: Advanced Statistical Natural Language Processing
MATH 547M: Statistical Machine Learning
MCB 516A: Bioinformatics and Functional Genomic Analysis
PHSC 612: Patient-Reported Health Outcomes
SIE 496: Special Topics in Systems and Industrial Engineering: Optimization for Machine Learning
SIE 522: Engineering Decision Making Under Uncertainty
SIE 530: Engineering Statistics
SIE 532: Sports Analytics
SIE 533: Fundamentals of Data Science for Engineers
SIE 540: Survey of Optimization Methods
SIE 545: Fundamentals of Optimization
SIE 577: Introduction to Biomedical Informatics
SIE 578: Artificial Intelligence for Health and Medicine
SIE 640: Large-Scale Optimization
SIE 645: Nonlinear Optimization
SIE554A: The Systems Engineering Process
STAT 564: Theory of Probability
STAT 571A: Advanced Statistical Regression Analysis
STAT 571B: Design of Experiments

Complete a total of 3 units for the required internship or capstone project:


Internship or Capstone Project

Either an internship or a capstone project for a total of 3 units is required as part of the MSDS. A maximum of 6 units of experiential coursework (capstone or internship) can be applied toward graduation.

Internship

The internship is intended to provide an opportunity for students to build on what they have mastered in the program and practice the knowledge and skills in the real world, whether corporate, institutional, nonprofit or otherwise. The internship should be relevant to student's degree competencies and contribute to the development and enforcement of the student's knowledge and skill sets in the fields of data science and information science.

College of Information Science master's students have interned at a wide range of organizations, including:

  • Amazon
  • Avirtek
  • CyVerse
  • Freeport-McMoRan
  • Genentech
  • iDE Global
  • Intel
  • Labcorp Drug Development
  • Lightsense Technology
  • Lum.ai
  • Lunewave
  • Mayo Clinic
  • NuvOx Pharma
  • Onebridge
  • Pima County Public Library
  • Pitney Bowes
  • Roche
  • RNC Mobile Services
  • Tesla
  • The University of Arizona
  • Tucson Police Department
  • U.S. Food and Drug Administration (FDA)
  • Viasat
  • Vue Data

For additional information about internships, including resources for finding an internship and select internship postings, view the InfoSci Internships & Mentorships page:

College of Information Science Internship Information & Resources



Capstone Project

The 1- to 3-unit MSDS capstone project is an opportunity for students to showcase what they have mastered in the program. It is based on a project plan that includes project goals, master's competencies addressed by the project, system design, implementation schedule, assessment plan and milestones. The project contributes to the development and enforcement of the student's knowledge and skill sets in the field of information science.

The capstone project must exercise all competencies required for the MSDS and must also have a software development component. Students will deposit capstone project code in GitHub or another source code repository.

To declare capstone projects, students follow these steps:

  • Contact your staff advisor to request enrollment during open registration
  • Upon completing the capstone project, submit a report (5,000-6,000 words in length) in the form of an academic paper, documenting what has been accomplished and explaining how the competencies have been demonstrated.
  • Your supervisor(s) will complete a competencies evaluation form, evaluate the project and assign a pass/fail grade.


MSDS Degree Requirements
for Students Admitted Fall 2023 - Summer 2024

MS Data Science students who began their program in Fall 2023, Spring 2024 or Summer 2024 have different requirements from those above:

Ready to transform your future in data science?

Learn more about the Master of Science in Data Science by contacting us at infosci-grad@arizona.edu, or review the admissions process and begin your application now:

Start Your Application