Degree Requirements – M.S. Information

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 intends 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.

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 

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

  • Register for INFO 698: Senior Capstone
  • 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 (see below for options within emphasis areas)
  • 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.

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.

{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.

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.

You are encouraged to select one or two emphasis areas or develop your own in collaboration with your faculty advisor. The areas of emphasis listed below represents some anticipated areas of interest and specialization based on student interest and faculty expertise, but it is not a comprehensive list of courses.

Data Science

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 guide students through advanced applications of computational methods for social science research.  Students will be encouraged to consider social problems from across sectors, including health science, environmental policy, education, and business. Particular attention will be given to the collection and analysis of data to study social networks, online communities, electronic commerce, and digital marketing.  Students will consider the many research designs used in contemporary social research, including “Big” data, online surveys, and virtual experimental labs, and will think critically about claims of causality, mechanisms, and generalization.

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.

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 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.

Students will learn from experts from projects that have developed widely adopted foundational Cyber infrastructure resources, followed by hands-on laboratory exercises focused around those resources. Students will use these resources and gain practical experience from laboratory exercises for a final project using a data set and meeting requirements provided by domain scientists. Students will be provided access to computer resources at: UA campus clusters, iPlant Collaborative and at NSF XSEDE. Students will also learn to write a proposal for obtaining future allocation to large-scale national resources through XSEDE.  Graduate-level requirements include reading a paper related to cyberinfrastructure, present it to the class, and lead a discussion on the paper.

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.

Introduction to organization systems that use controlled vocabularies. Principles, standards, design and maintenance of thesauri using computer software are studied. The use of controlled vocabularies in website design and digital libraries is also explored.

This course covers theory, methods, and techniques widely used to design and develop a relational database system and students will develop a broad understanding of modern database management systems. Applications of fundamental database principles in a stand-alone database environment using MS Access and Windows are emphasized. Applications in an Internet environment will be discussed using MySQL in the Linux platform. Graduate-level requirements include a group project consisting of seven sections: Database Design; Implementation (Tables); Forms; Data Retrieval (Queries/Reports); Project Presentation; Project Report; and, Peer Evaluation.

Information Systems

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.

We do all sorts of things with information technology: we play games, we listen to music, we watch movies, and we communicate with other people. But one of the main things that we use information technology for is to learn things. Toward this end, we visit Wikipedia, Ask.com, The New York Times, and other such sites. Or we just Google stuff that we want to know about. This course is about how information technology is affecting the ability of individuals and institutions to acquire and share knowledge.

This course covers theory, methods, and techniques widely used to design and develop a relational database system and students will develop a broad understanding of modern database management systems. Applications of fundamental database principles in a stand-alone database environment using MS Access and Windows are emphasized. Applications in an Internet environment will be discussed using MySQL in the Linux platform. Graduate-level requirements include a group project consisting of seven sections: Database Design; Implementation (Tables); Forms; Data Retrieval (Queries/Reports); Project Presentation; Project Report; and, Peer Evaluation.

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.

Organizing information in electronic formats requires standard machine-readable languages. This course covers recent standards including XML (eXtensible Markup Language) and related technologies (XPath and XSLT) which are used widely in current information organization systems. Building on a sounding understanding of XML technologies, the course also introduces students to newer standards that support the development of the Semantic Web. These standards include RDF (Resource Description Framework), RDFS (RDF Schema), and OWL (Web Ontology Language) and their application under the Linked Data paradigm. While the application of many specific XML schemas used in libraries and other information setting such as science and business will be used to provide the context for various topics, the main focus of the course is on understanding the concepts of XML and Semantic Web technologies and on applying practical skills in various settings, including but not limiting to libraries. The course is heavy with hands-on assignments and requires students complete a final group project.

This course provides a basic understanding of technology in the digital information environment along with an introduction to practical hands-on skills needed to manage digital information. The course combines reading, discussion, collaboration, project work, independent study, and guided hands-on practice. The course covers the basic installation, setup and maintenance of key systems found in the digital information environment today. Linux is used as a foundation for learning while drawing parallels to the Windows server operating system, Unix operating systems, and other operating systems.

This three-credit course is one of six required for completion of the Certificate in Digital Information Management (DigIn). This course will provide an in-depth look at the processes involved in building and managing digital collections and institutional repositories. The course will have a strong hands-on component in which students will apply advanced resource description methods to a collection, and then build a prototype repository along with a basic access system. Students will also analyze and discuss case examples of digital collections, focusing on technology management issues and organizational strategies for building different types of collections.

Human Information Interaction

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.

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.

This course provides an introduction to video game development. We will explore game design (not just computer games, but all games) and continue with an examination of game prototyping. Once we have working prototypes, we will continue with the development of a complete 2D computer game. The remaining course topics include: designing the game engine, rendering the graphics to the screen, and artificial intelligence. Students will be given periodic homework that reinforces what was learned in class. Homework will include developing a game prototype, game design documentation, some programming tasks. Students will work in small teams to develop a working game as a term project. Grades will be primarily based on the term project with some small amount of weight to homework. The examples provided in class will be programmed in Java and available for execution on any operating system. Programming homework assignments will be done in either Java or the language chosen by the instructor. The term project can be written in any programming language with instructor permission.

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.

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.

LIS/INFO 671 introduces the basic functions of: *digital curation, a term that refers to the full set of management processes needed to create, select, describe, preserve and facilitate access to all types of digital collections, and *digital preservation, a formal endeavor to ensure that digital information of continuing value remains accessible and usable. We will focus primarily on digital curation and preservation in archives, libraries and museums, but we will also explore and compare digital curation and preservation practices from other disciplines, such as e-commerce, government documents and various business document systems and collections, in order to understand both the differences and similarities in the organization, management and preservation of different digital collections. By concentrating on common principles of information organization and information life cycles, you will be able to translate your learning and skills to many kinds of digital collections across disciplines and institutional cultures. This course will also introduce the basic problems associated with digital preservation. It will give students a thorough orientation to the technological and organizational approaches, which have been developed to address long-term preservation concerns. Finally, the course will examine the current state of the art in digital preservation and assess what challenges remain in research and implementation efforts. This course is designed to help new information professionals identify roles to play in managing and preserving digital objects and collections, and at the same time to enhance their effectiveness in working across organizational and technical boundaries.

Healthcare Informatics

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.

This course will focus on the online retrieval and evaluation of medical literature and the issues surrounding provision of timely, relevant, peer-reviewed medical information. Emphasis will be on the development of the intellectual acuity required to provide physicians, nurses, pharmacists, allied health professionals, medical researchers and consumers with targeted responses to medical queries. Current search modalities such as Evidence-Based Medicine will be covered both in readings and in class discussions.

{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.

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.

Biodiversity/Ecological Informatics

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.

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.

Students will learn from experts from projects that have developed widely adopted foundational Cyber infrastructure resources, followed by hands-on laboratory exercises focused around those resources. Students will use these resources and gain practical experience from laboratory exercises for a final project using a data set and meeting requirements provided by domain scientists. Students will be provided access to computer resources at: UA campus clusters, iPlant Collaborative and at NSF XSEDE. Students will also learn to write a proposal for obtaining future allocation to large-scale national resources through XSEDE.  Graduate-level requirements include reading a paper related to cyberinfrastructure, present it to the class, and lead a discussion on the paper.

Analyze genomic sequences through understanding and using a variety of bioinformatics algorithms and software tools.  Interdisciplinary approach integrating informatics, statistics, and biology.  Graduate-level requirements include leading a discussion on a current paper or give a tutorial on a bioinformatics tool as part of the Major Concept Exercises category.

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.

Organizing information in electronic formats requires standard machine-readable languages. This course covers recent standards including XML (eXtensible Markup Language) and related technologies (XPath and XSLT) which are used widely in current information organization systems. Building on a sounding understanding of XML technologies, the course also introduces students to newer standards that support the development of the Semantic Web. These standards include RDF (Resource Description Framework), RDFS (RDF Schema), and OWL (Web Ontology Language) and their application under the Linked Data paradigm. While the application of many specific XML schemas used in libraries and other information setting such as science and business will be used to provide the context for various topics, the main focus of the course is on understanding the concepts of XML and Semantic Web technologies and on applying practical skills in various settings, including but not limiting to libraries. The course is heavy with hands-on assignments and requires students complete a final group project.

Computational Arts

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.

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.

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.

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 an introduction to video game development. We will explore game design (not just computer games, but all games) and continue with an examination of game prototyping. Once we have working prototypes, we will continue with the development of a complete 2D computer game. The remaining course topics include: designing the game engine, rendering the graphics to the screen, and artificial intelligence. Students will be given periodic homework that reinforces what was learned in class. Homework will include developing a game prototype, game design documentation, some programming tasks. Students will work in small teams to develop a working game as a term project. Grades will be primarily based on the term project with some small amount of weight to homework. The examples provided in class will be programmed in Java and available for execution on any operating system. Programming homework assignments will be done in either Java or the language chosen by the instructor. The term project can be written in any programming language with instructor permission.