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.
Degree Requirements – M.S. Information Science
The master’s degree is designed to help you develop advanced skills in applying information methods and to become a competitive information professional. The degree requires 30 total units and can typically be completed in 1.5 years for full-time students.
Plan of Study
You should work with your faculty to develop a Master’s Plan of Study during your first few months in the program. The Plan of Study should be submitted to the Graduate College no later than your second semester in the program.
The Master’s Plan of Study identifies 1) courses you intend to transfer from other institutions; 2) courses already completed at the University of Arizona which you intend to apply toward the graduate degree, and 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.
***Students admitted Spring 2023 and after will choose a sub-plan. Click on sub-plan to see requirements.***
Human-Centered Computing Sub-Plan
Human-Centered Computing courses explore topics like simulations, virtual reality, human-computer interaction, user experience, and personal data-collection.
See the RequirementsMachine Learning Sub-Plan
The Machine Learning Sub-Plan will prepare graduates to be innovative scientific leaders across sectors, graduates who understand the complexities of machine-learning as a particular kind of data science.
See the Requirements***Requirements below are for M.S. Information student admitted prior to Spring 2023.***
Core Courses
- 9 units total
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
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 M.S. 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, you will submit a report (5000-6000 words in length) in the form of an academic paper, documenting what has been accomplished and explain how the competencies have been demonstrated. Your 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 (https://ischool.arizona.edu/ms-student-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:
This is a senior level seminar about the culture of graphic design and its relationship to the culture at large. Through readings and in depth discussions we will explore the discourse of design from the 1950s to the present. Readings, presentations and discussions will cover philosophical, historical, social, political, cultural, environmental and ethical aspects of professional design practice.
Digital Arts Studio Critique Seminar is a class in the ongoing evolution of developing presentational skills and a forum for the presentation and critique of works and processes created by students enrolled in the Digital Arts M.F.A. plan of study.
The practice of modern medicine in a highly regulated, complex, sociotechnical enterprise is a testament to the future healthcare system where the balance between human intelligence and artificial expertise will be at stake. The goal of this course is to introduce the underlying concepts, methods, and the potential of intelligent systems in medicine. We will explore foundational methods in artificial intelligence (AI) with greater emphasis on machine learning and knowledge representation and reasoning, and apply them to specific areas in medicine and healthcare including, but not limited to, clinical risk stratification, phenotype and biomarker discovery, time series analysis of physiological data, disease progression modeling, and patient outcome prediction. As a research and project-based course, student(s) will have opportunities to identify and specialize in particular AI methods, clinical/healthcare applications, and relevant tools.
Probabilistic graphical modeling and inference is a powerful modern approach to representing the combined statistics of data and models, 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.
Data visualization is a research area that focuses on the use of visualization techniques to help people understand and analyze data. Visualization allows us to perceive relationships, patterns, and trends. While statistical techniques may determine correlations among the data, visualization helps us frame what questions to ask. Providing efficient and effective data visualization is a difficult challenge in many real world examples. One challenge lies in developing algorithmically efficient methods to visualize large and complex data sets. Another challenge is to develop effective visualizations that make the underlying patterns and trends easy to see. Even tougher is the challenge of providing interactive access, analysis, and filtering. All of these tasks become still more difficult with the size of the data sets arising in modern applications.
This course will explore current research problems in visualizing large and complex data such as social networks with hundreds of thousands of participants and millions of relationships. Modeling such data and developing effective visualization tools is a challenging theoretical and practical task. This course will focus on classical as well as modern methods through projects that utilize real world large datasets from Netflix, IMDB, DBLP, and the Tree of Life.
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Emphasis on DBMS architecture and implementation issues such as storage structures, multidimensional index structures, query optimization, concurrency control and recovery, and parallel database systems. |
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Students will learn why machine learning is a fundamentally different way of writing computer programs, and why this approach is often a uniquely attractive way of solving practical problems. Machine learning is all about automatic ways for computers to find patterns in datasets; students will learn both advantages and unique risks that this approach offers. They will learn the fundamental computational methods, algorithms, and perspectives which underlie current machine learning methods, and how to derive and implement many of them. |
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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 these 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. |
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This course covers important algorithms useful for natural language processing (NLP), including distributional similarity algorithms such as word embeddings, recurrent and recursive neural networks (NN), probabilistic graphical models useful for sequence prediction, and parsing algorithms such as shift-reduce. This course will focus on the algorithms that underlie NLP, rather than the application of NLP to various problem domains. |
This course will introduce the fundamental concepts of geographic information systems technology (GIST). It will emphasize equally GISystems and GIScience. Geographic information systems are a powerful set of tools for storing and retrieving at will, transforming and displaying spatial data from the real world for a particular set of purposes. In contrast, geographic information science is concerned with both the research on GIS and with GIS. As Longley et.al., notes (2001, vii) ¿GIS is fundamentally an applications-led technology, yet science underpins successful applications.¿ This course will combine an overview of the general principles of GIScience and how this relates to the nature and analytical use of spatial information within GIS software and technology. Students will apply the principles and science of GIST through a series of practical labs using ESRI¿s ArcGIS software.
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.
{Taught off numbered years} Focuses on development and maintenance of healthcare databases for application in solving healthcare problems. Design methods, database structures, indexing, data dictionaries, retrieval languages and data security are presented.
Focuses on the theoretical basis of healthcare informatics with an emphasis on management and processing of healthcare data, information, and knowledge. Healthcare vocabulary and language systems, and basic database design concepts are addressed.
This course introduces the student to fundamentals of database analysis, design, and implementation. Emphasis is on practical aspects of business process analysis and the accompanying database design and development. Topics covered include: conceptual design of databases using the entity relationship model, relational design and normalization, SQL and PL/SQL, web based database design, and implementation using Oracle or some other modern Database Management Systems. Students are required to work with a local client organization in understanding their business requirements, developing a detailed set of requirements to support business processes, and designing and implementing a web based database application to support their day- to-day business operations and decision making. Students will acquire hands-on-experience with a state-of-the-art database management system such as Oracle or Microsoft SQL Server, and web-based development tools.
Corporations today are said to be data rich but information poor. For example, retailers can easily process and capture millions of transactions every day. In addition, the widespread proliferation of economic activity on the Internet leaves behind a rich trail of micro-level data on consumers, their purchases, retailers and their offerings, auction bidding, music sharing, so on and so forth. Data mining techniques can help companies discover knowledge and acquire business intelligence from these massive datasets. This course will cover data mining for business intelligence. Data mining refers to extracting or "mining" knowledge from large amounts of data. It consists of several techniques that aim at discovering rich and interesting patterns that can bring value or "business intelligence" to organizations. Examples of such patterns include fraud detection, consumer behavior, and credit approval. The course will cover the most important data mining techniques --- classification, clustering, association rule mining, visualization, prediction --- through a hands-on approach using XL Miner and other specialized software, such as the open-source WEKA software.
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Visualizing data is an important step in understanding data, exploring relationships, and "making a case." The goal of this class is to introduce students to principles and tools of data visualizations, and create visualizations using appropriate tools for two different but related purposes: (1) exploration; and (2) presentation. The first part is about trying to understand the data and test hypotheses that drive the data visualization effort, and formulate a story; the second part is to convey that finding to others in a convincing manner. |
This course is to help master-level graduate students develop necessary skills of collecting, storing and managing, exploring, processing and computing big data for business purposes. Topics covered in this course will include big data collection for business, data management with SQL and NoSQL based technologies, data exploration and preprocessing for analytics, data dashboards for business, distributed data storage and computing, and big data based machine learning systems. This course will use state-of-the-art data management, data exploration and computing, and big data machine learning software tools (such as SQL Server, MongoDB, PySpark and TensorFlow) to provide hands-on experience. Students will learn how to apply big data techniques to sift through large amounts of data and provide actionable business insights.
The amount of data in our world has been exploding, resulting in what is popularly known as Big Data. At least three major forces are driving the interest and growth in Big Data (1) a rapid increase in the amount of data being generated on the internet, (2) the evolving strategy of firms to collect data from sources both internal and external along the entire product and process lifecycle, and (3) the phenomenal growth of social media, mobile applications, and sensor based technologies as well as the Internet of Things. All of these forces are generating a flood of data which is increasing in volume, variety and velocity.
The objective of this course is to introduce students to Data Science techniques to collect, process, visualize and analyze all kinds of "Big Data". It will provide training to those interested in becoming Data Scientists. The course will delve into Web analytics and students will be exposed to tools such as Google analytics and participate in a Google Online Challenge to compete for awards. Topics related to network analysis techniques will be covered in detail where students will learn how to construct, mathematically analyze and visualize different types of networks. Additionally, students will also learn about using MongoDb, Hadoop, and executing map-reduce jobs to process and analyze large datasets collected from social media sites such as Twitter, Youtube, and Facebook.
The objective of this course is to give students a broad overview of managerial, strategic and technical issues associated with Business Intelligence and Data Warehouse design, implementation, and utilization. Topics covered will include the principles of dimensional data modeling, techniques for extraction of data from source systems, data transformation methods, data staging and quality, data warehouse architecture and infrastructure, and the various methods for information delivery. Critical issues in planning, physical design process, deployment and ongoing maintenance will also be examined. Students will learn how data warehouses are used to help managers successfully gather, analyze, understand and act on information stored in data warehouses. The components and design issues related to data warehouses and business intelligence techniques for extracting meaningful information from data warehouses will be emphasized. The course will use state-of-the-art data warehouse and OLAP software tools to provide hands-on experience in designing and using Data Warehouses and Data Marts. Students will also learn how to gather strategic decision making requirements from businesses, develop key performance indicators (KPIs) and corporate performance management metrics using the Balanced Scorecard, and design and implement business dashboards.
Focuses on contemporary organizational theories as they apply to complex healthcare systems. Emphasis is placed on application of theory to organizational analysis and decision making.
This course examines the use of technology for expanding capacity to deliver health care services and education. Students will explore major conceptual and methodological issues associated with designing, implementing, and evaluating the effectiveness of technology-enhanced interventions.
Techniques of advanced computational statistics. Numerical optimization and integration pertinent for statistical calculations; simulation and Monte Carlo methods including Markov chain Monte Carlo (McMC); bootstrapping; smoothing/density estimation; and other modern topics.