MSIS Curriculum, Subplans & Courses

 

Faculty lecturing

Ranked the #11 program in the nation for machine learning by TechGuide, the University of Arizona Master of Science in Information Science is a transformative, interdisciplinary journey that gives students the advanced skills they need to implement information methods across organizations and industries.

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

Students select one of two subplans:

HUMAN-CENTERED COMPUTING

Human-centered computing courses explore topics like simulations, virtual reality, human-computer interaction, user experience and personal data-collection. The subplan includes an additional core course in human-centered computing and a variety of focused electives.

MACHINE LEARNING

Machine learning focuses on the interpretation and management of large amounts of data by automating the processes by which models of data are built. The subplan includes an additional core course in machine learning and a variety of focused electives.


MSIS Student Competencies

Students who graduate from the University of Arizona MS in Information 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 MSIS 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 MSIS 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.


Human-Centered Computing Subplan

Human-centered computing courses explore topics like simulations, virtual reality, human-computer interaction, user experience and personal data-collection. The subplan includes an additional core course in human-centered computing and a variety of focused electives.

Click a link below to view course information:

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


Machine Learning Subplan

Machine learning focuses on the interpretation and management of large amounts of data by automating the processes by which models of data are built. The emphasis prepares graduates—who understand the complexities of machine learning as a particular kind of data science—to be scientific leaders across sectors. The subplan includes an additional core course in machine learning and a variety of focused electives. The Machine Learning Subplan is ranked the #11 program in the nation by TechGuide.

Click a link below to view course information.

  • Choose three elective courses.
  • Up to two elective courses may be substituted from other academic units with advisor approval.
AREC 548: Introduction to Statistical Methods in Economics
AREC 548: Introduction to Statistical Methods in Economics
AREC 549: Applied Econometric Analysis
AREC 559: Advanced Applied Econometrics
BAT 502: Fundamentals of Computing in Biosystems Analytics
BIOS 511: Healthcare Data Science
BIOS 551: Health Data Management and Visualization
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
COGS 510: Computing for Neuroimagers
CSC 522: Parallel and Distributed Computing
CSC 536: Software Engineering
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 513: Web Development and the Internet of Things
ECE 523 Engineering Applications of Machine Learning and Data Analytics
ECE 524: Fundamentals of Cloud Security
ECE 540: Quantum Sensing and Quantum Machine Learning
ECE 569: High Performance Computing: Technology, Architecture and Algorithms
ECE 569: High Performance Computing: Technology, Architecture and Algorithms
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
GAME 526: Game Artificial Intelligence
GAME 562: 3D Modeling for Games
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
HSD 592: Directed Research
HWRS 501: Tools for Data Handling and Analysis in Water, Weather and Climate
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 524: Virtual Reality
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 539: Statistical Natural Language Processing (Cross-listed LING 539)
INFO 550: Artificial Intelligence
INFO 555: Applied Natural Language Processing
INFO 556: Text Retrieval and Web Search
INFO 557: Neural Networks
INFO 560: Serious STEM Games
INFO 570: Database Development and Management
INFO 571: Introduction to Information Technology
INFO 577: Information Security
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
INFO 608: Managing the Information Organization
INFO 621: Advanced Machine Learning Applications
ISTA 410/510: Bayesian Modeling and Inference
LING 508: Computational Techniques for Linguists
LING 529: Human Language Technology I
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
MIS 547: Fundamentals of Cloud Computing and its Design Strategies
MIS 584: Big Data Technologies
PHSC 612: Patient-Reported Health Outcomes
PTYS 595B: Special Topics in Planetary Science: Statistics and Bayesian Data Analysis
SFWE 503: Software Project Management
SIE 496: Special Topics in Systems and Industrial Engineering: Optimization for Machine Learning
SIE 520: Stochastic Modeling I
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 549: Optimization for Machine Learning
SIE 577: Introduction to Biomedical Informatics
SIE 578: Artificial Intelligence for Health and Medicine
SIE 596: Special Topics in Systems and Industrial Engineering: Optimization for Machine Learning
SIE 640: Large-Scale Optimization
SIE 645: Nonlinear Optimization
SIE554A: The Systems Engineering Process
SPWE 501: Software Assurance and Security
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 and capstone project:

 


Internship or Capstone Project

An internship or capstone project of 1 to 3 units is required as part of the MSIS.

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 MSIS 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 MSIS and must also have a software development component. Students will deposit capstone project code in GitHub or another source code repository.

View recent capstone project summaries, including keywords and faculty advisors.

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.

Curriculum & Courses for Students
Admitted Prior to Spring 2023

For students admitted prior to Spring 2023, view the MSIS curriculum and courses:

Core Courses

9 units total

Experiential Courses: Internship & Capstone 

Complete 3 units total:

  • INFO 693: Internship (1–3 units) 
  • INFO 692: Directed Research (1–3 units) 

More information on experiential courses is available on our internships and individual studies pages.

Capstone Project

Complete 3 units:

  • Register for INFO 698: Capstone Project.
  • The project will evaluate all competencies required for the MSIS degree.
  • Project must have a software development component with code deposited in GitHub or other source code repository.
  • Course may be repeated once if you do not obtain a satisfactory score the first time.
  • Project must be supervised by at least one faculty member in the College of Information Science.

You must submit your application in Handshake. More information can be found on the individual studies page.

Upon completing the capstone project, students 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. The student's supervisor(s) will complete the competencies evaluation form. The Graduate Committee (or its subcommittee), plus the supervisors, will evaluate the project and competencies and assign a pass/fail grade. 

Elective Courses

  • 15 units total
  • No more than 6 non-INFO (out-of-department) units are allowed (if a student wants to petition for a non-INFO course that is not on the pre-approved list to count as an elective, they must send this request to the MS INFO Academic Advisor, attaching the course's syllabus and a detailed description of which MS INFO competencies the course addresses)
  • Any non-core courses with the INFO prefix is considered elective
  • The following out-of-department courses are also pre-approved for electives:

 

Ready to transform your future in information science?

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

Start Your Application