MSIS Curriculum, Subplans & Courses

 

Faculty lecturing

Ranked the #11 program in the nation for machine learning by TechGuide, the UArizona 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 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 UArizona 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, multimedia 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.

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.

This course introduces fundamental ideas of the Information Age, focusing on the value, organization, use, and processing of information. The course is organized as a survey of these ideas, with readings from the research literature. Specific topics (e.g., visualization, retrieval) will be covered by guest faculty who research in each of these areas.

The field of Human Computer Interaction (HCI) encompasses the design, implementation, and evaluation of interactive computing systems. This course will provide a survey of HCI theory and practice. The course will address the presentation of information and the design of interaction from a human-centered perspective, looking at relevant perceptive, cognitive, and social factors influencing in the design process. It will motivate practical design guidelines for information presentation through Gestalt theory and studies of consistency, memory, and interpretation. Technological concerns will be examined that include interaction styles, devices, constraints, affordances, and metaphors. Theories, principles and design guidelines will be surveyed for both classical and emerging interaction paradigms, with case studies from practical application scenarios. As a central theme, the course will promote the processes of usability engineering, introducing the concepts of participatory design, requirements analysis, rapid prototyping, iterative development, and user evaluation. Both quantitative and qualitative evaluation strategies will be discussed.

This course provides an overview of the various concepts and skills required for effective data visualization. It presents principles of graphic design, programming skills, and statistical knowledge required to build compelling visualizations that communicate effectively to target audiences. Visualization skills addressed in this course include choosing appropriate colors, shapes, variable mappings, and interactivity based on principles of color perception, pre-attentive processing, and accessibility.

Choose three courses (minimum 9 units) from the following:

This course is a hands-on, project-based approach to understanding and designing art installations. Enrollees will learn principles, tools, and techniques of rapid prototyping and installation design, and will collaborate to design and implement a large-scale installation by the end of the semester. The course lectures will also provide an overview of the history, theory, and aesthetics of installation art. Graduate-level requirements include writing an analytical paper comparing several recent installation projects in relation to themes found in contemporary art (e.g., Artificial Life, Body/Identity Politics, Social Media/Hacktivism, Virtual or Augmented Reality, Databases and Information Visualization). The paper should be 15-20 pages in length.

Virtual reality (VR) is an emerging technology that has recently been widely used in various areas, such as education, training, well-being, and entertainment. VR offers a highly immersive experience as the head mounted displays surround a 360-degree view of the user. It encompasses many disciplines, such as computer science, human computer interaction, game design and development, information science, and psychology. This course merges a theoretical and practical approach to give students the necessary knowledge that is required to design, develop, and critique virtual reality games and applications.

Algorithms is a crucial component of game development. This course will provide students with an in-depth introduction to algorithm concepts for game development. The course will cover basic algorithm and data structures concepts, basic math concepts related to game algorithms, physics and artificial intelligence based game algorithms that are supplemented with modern examples. Unity Game Engine along with C# programming language will be used throughout the class.

This course provides a comprehensive survey of video game production practices. Students work on game development assignments for presentation in a professional portfolio. The course topics include: collaborative technologies, software design patterns for games, spatial transformations, and technical considerations surrounding game art, such as authoring sprites, 3D models, animations, texture mapping, and writing shaders. Students will be given periodic assignments that reinforce lessons from class.

 

Game development is a vast field with many advanced concepts. This course aims to teach students
such concepts, techniques and mechanisms in Unity, covering procedural content generation, design
patterns, artificial intelligence, shaders and postprocessing effects, animation, custom interactions and
gestures, and performance optimization. The students are expected to have fundamental game
development knowledge in Unity and C#. The course is heavily hands-on and project oriented. Students
will implement the covered concepts on small-scaled Unity project templates using C# and also develop
a larger-scaled final term project. At the end of the course, students will have gained advanced game
development skills that can be applied to future jobs or self-development.

Study of the user interface in information systems, of human computer interaction, and of website design and evaluation. Graduate-level requirements include group work and longer examinations.

  • Choose three elective courses with the INFO prefix
  • Up to two elective courses may be substituted from other academic units with advisor approval

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

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. 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 field of Information Science. The student should propose an internship plan and then identify an internship site supervisor, who typically is external. The site supervisor and the graduate advisor of the school need to approve the plan prior to course registration. The plan should include goals for the internship, degree competencies addressed by the internship, expected tasks to be completed, work schedule, and the assessment plan. The amount of the work should be appropriate for the units registered (3 units = 135 hours). The internship may be paid or unpaid. Student may take an internship in the same organization where student is employed, but work planed for the internship need to have a clear separation from the work expected by the employment. At the conclusion of the internship, the site supervisor is expected to submit a written assessment of student's work.

Capstone Project is intended to provide an opportunity for students to show off what they have mastered in the program. The project should be relevant to MS degree competencies and contribute to the development and enforcement of the student's knowledge and skill sets in the field of Information Science. The student should propose a project plan and the faculty advisor should approve it before registration. The project plan should include goals for the project, MS competencies addressed by the project, system design, an implementation schedule, and the assessment plan. The project plan should also include reasonable milestones and check points. The amount of the work should be appropriate for a 3-unit course. The primary faculty advisor must be an SI faculty, but faculty members from other units may participate in advising the student.

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.

This course introduces fundamental ideas of the Information Age, focusing on the value, organization, use, and processing of information. The course is organized as a survey of these ideas, with readings from the research literature. Specific topics (e.g., visualization, retrieval) will be covered by guest faculty who research in each of these areas.

Machine learning describes the development of algorithms, which can modify their internal parameters (i.e., "learn") to recognize patterns and make decisions based on example data. These examples can be provided by a human, or they can be gathered automatically as part of the learning algorithm itself. This course will introduce the fundamentals of machine learning, will describe how to implement several practical methods for pattern recognition, feature selection, clustering, and decision making for reward maximization, and will provide a foundation for the development of new machine learning algorithms.  

This course provides an overview of the various concepts and skills required for effective data visualization. It presents principles of graphic design, programming skills, and statistical knowledge required to build compelling visualizations that communicate effectively to target audiences. Visualization skills addressed in this course include choosing appropriate colors, shapes, variable mappings, and interactivity based on principles of color perception, pre-attentive processing, and accessibility.

Choose three courses (minimum 9 units) from the following:

Bayesian modeling and inference is a powerful modern approach to representing the statistics of the world, reasoning about the world in the face of uncertainty, and learning about it from data. It cleanly separates the notions of representation, reasoning, and learning. It provides a principled framework for combining multiple source of information such as prior knowledge about the world with evidence about a particular case in observed data. This course will provide a solid introduction to the methodology and associated techniques, and show how they are applied in diverse domains ranging from computer vision to molecular biology to astronomy.  Graduate-level requirements include different exams requiring greater depth of understanding of topics, and will be assigned questions based on graduate-student specific assignments topics.

This course will introduce students to the concepts and techniques of data mining for knowledge discovery. It includes methods developed in the fields of statistics, large-scale data analytics, machine learning, pattern recognition, database technology and artificial intelligence for automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns. Topics include understanding varieties of data, data preprocessing, classification, association and correlation rule analysis, cluster analysis, outlier detection, and data mining trends and research frontiers. We will use software packages for data mining, explaining the underlying algorithms and their use and limitations. The course include laboratory exercises, with data mining case studies using data from many different resources such as social networks, linguistics, geo-spatial applications, marketing and/or psychology.

This course introduces the key concepts underlying statistical natural language processing. Students will learn a variety of techniques for the computational modeling of natural language, including: n-gram models, smoothing, Hidden Markov models, Bayesian Inference, Expectation Maximization, Viterbi, Inside-Outside Algorithm for Probabilistic Context-Free Grammars, and higher-order language models.  Graduate-level requirements include assignments of greater scope than undergraduate assignments. In addition to being more in-depth, graduate assignments are typically longer and additional readings are required.

This course provides a broad technical introduction to the tools, techniques and concepts of artificial intelligence. The course will focus on methods for automating decision making under a variety of conditions, including full and partial information, and dealing with uncertainty. Students will gain practical experience writing programs that use these techniques to solve a variety of problems.

Topics include problem solving (search spaces, uninformed and informed search, games, and constraint satisfaction), principles of knowledge representation and reasoning (propositional and first-­‐order logic, logical inference, planning), and representing and reasoning with uncertainty (decision theory, reinforcement learning, Bayesian networks, probabilistic inference, basic discrete-­‐time probabilistic models).

Most of web data today consists of unstructured text. This course will cover the fundamental knowledge necessary to organize such texts, search them a meaningful way, and extract relevant information from them. This course will teach natural language processing through the design and development of end-to-end natural language understanding applications, including sentiment analysis (e.g., is this review positive or negative?), information extraction (e.g., extracting named entities and their relations from text), and question answering (retrieving exact answers to natural language questions such as "What is the capital of France" from large document collections). We will use several natural language processing toolkits, such as NLTK and Stanford's CoreNLP. The main programming language used in the course will be Python, but code written in Java or Scala will be accepted as well.  Graduate-level requirements include implementing more complex, state-of-the-art algorithms for the three proposed projects. This will require additional reading of conference papers and journal articles.

Most of the web data today consists of unstructured text. Of course, the fact that this data exists is irrelevant, unless it is made available such that users can quickly find information that is relevant for their needs. This course will cover the fundamental knowledge necessary to build such systems, such as web crawling, index construction and compression, boolean, vector-based, and probabilistic retrieval models, text classification and clustering, link analysis algorithms such as PageRank, and computational advertising. The students will also complete one programming project, in which they will construct one complex application that combines multiple algorithms into a system that solves real-world problems.  Graduate level requirements include implementing more complex, state-of-the-art algorithms for the programming project, which might require additional reading of research articles. Written assignments will have additional questions for graduate students.

Neural networks are a branch of machine learning that combines a large number of simple computational units to allow computers to learn from and generalize over complex patterns in data. Students in this course will learn how to train and optimize feed forward, convolutional, and recurrent neural networks for tasks such as text classification, image recognition, and game playing.

  • Choose three elective courses with the INFO prefix
  • Up to two elective courses may be substituted from other academic units with advisor approval

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

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. 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 field of Information Science. The student should propose an internship plan and then identify an internship site supervisor, who typically is external. The site supervisor and the graduate advisor of the school need to approve the plan prior to course registration. The plan should include goals for the internship, degree competencies addressed by the internship, expected tasks to be completed, work schedule, and the assessment plan. The amount of the work should be appropriate for the units registered (3 units = 135 hours). The internship may be paid or unpaid. Student may take an internship in the same organization where student is employed, but work planed for the internship need to have a clear separation from the work expected by the employment. At the conclusion of the internship, the site supervisor is expected to submit a written assessment of student's work.

Capstone Project is intended to provide an opportunity for students to show off what they have mastered in the program. The project should be relevant to MS degree competencies and contribute to the development and enforcement of the student's knowledge and skill sets in the field of Information Science. The student should propose a project plan and the faculty advisor should approve it before registration. The project plan should include goals for the project, MS competencies addressed by the project, system design, an implementation schedule, and the assessment plan. The project plan should also include reasonable milestones and check points. The amount of the work should be appropriate for a 3-unit course. The primary faculty advisor must be an SI faculty, but faculty members from other units may participate in advising the student.

 

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.

iSchool 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

The student should propose an internship plan and then identify an internship site supervisor, who typically is external. The site supervisor and the graduate advisor of the school need to approve the plan prior to course registration. The plan should include:

  • Goals for the internship
  • Degree competencies addressed by the internship
  • Expected tasks to be completed
  • Work schedule
  • Assessment plan

The amount of the work should be appropriate for the units registered (3 units = 135 hours). The internship may be paid or unpaid. A student may take an internship in the same organization where the student is employed, but work planned for the internship needs to have a clear separation from the work expected by the employment.

At the conclusion of the internship, the site supervisor is expected to submit a written assessment of student's work.

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

iSchool 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.

To declare capstone projects, students follow these steps:

  1. Identify your iSchool faculty supervisor.
  2. Request an experience via Handshake (mandatory).
  3. 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.
  4. 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

This course introduces fundamental ideas of the Information Age, focusing on the value, organization, use, and processing of information. The course is organized as a survey of these ideas, with readings from the research literature. Specific topics (e.g., visualization, retrieval) will be covered by guest faculty who research in each of these areas.

This course introduces fundamental methods for both qualitative and quantitative research in information studies. Additionally, the seminar introduces the student to established and emerging areas of scholarly research in Schools of Information to encourage them to identify a personal research agenda. The seminar is organized in two main parts: the first part introduces relevant research methods (quantitative and qualitative), whereas the second part overviews specific research directions currently active in the School of Information. The second part of the seminar will be covered by guest faculty who research in each of the covered areas.

Introduction to the theories and practices used in the organization of information. Overview of national and international standards and practices for access to information in collections.

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 School of Information.

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:

Directed Research courses are intended to cover advanced material outside of or beyond the scope of current course offerings. In such courses, the student will work on a research project under the direct supervision of a School of Information faculty member. The research topic should be relevant to MS degree competencies and contribute to the development of the student’s knowledge and skill sets in the field of Information Science. The student should propose a research plan including the expected outcome and the faculty advisor should approve it before registration. The research plan should include a problem statement, proposed research methods, expected outcome, a schedule of research activities and meeting schedule between the student and the faculty advisor, and the assessment of the student performance. The amount of the work should be appropriate for the requested credits. The primary faculty advisor must be an SI faculty, but faculty members from other units may participate in advising the student.

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. 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 field of Information Science. The student should propose an internship plan and then identify an internship site supervisor, who typically is external. The site supervisor and the graduate advisor of the school need to approve the plan prior to course registration. The plan should include goals for the internship, degree competencies addressed by the internship, expected tasks to be completed, work schedule, and the assessment plan. The amount of the work should be appropriate for the units registered (3 units = 135 hours). The internship may be paid or unpaid. Student may take an internship in the same organization where student is employed, but work planed for the internship need to have a clear separation from the work expected by the employment. At the conclusion of the internship, the site supervisor is expected to submit a written assessment of student's work.

 

Ready to transform your future in information science?

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

Start Your Application