Important ideas and applications of information science and technology in the sciences, humanities and arts. Information, entropy, coding; grammar and parsing; syntax and semantics; networks and relational representations; decision theory, game theory; and other great ideas form the intellectual motifs of the Information Age and are explored through applications such as robotic soccer, chess-playing programs, web search, population genetics among others.
As we work together to battle the coronavirus, we will continue to offer safe and secure online sessions . Even though our physical office is closed, in accordance with the guidelines recommended by CDC, we are working remotely and continuing to provide student, staff, and faculty assistance. We can be reached Monday-Friday 9am-4pm Mountain Standard Time at 520-621-3565 or by email – please refer to the iSchool Directory. Please allow up to 24 hours response time. Faculty and Adjuncts will respond as their schedules permit.
Degree Requirements – Information Science & Technology
If your catalog year is Spring 2017 or earlier, please contact the Undergraduate Academic Advisor for your degree requirements.
- 1st Year English or equivalent
- MATH 122B or MATH 113 or MATH 116
- 2nd semester second language proficiency
- 6 units Tier 1 Individuals & Societies
- 6 units Tier 1 Traditions & Cultures
- 6 units Tier 1 Natural Sciences
- 3 units Tier 2 Humanities
- 3 units Tier 2 Individuals & Society
- 3 units Tier 2 Arts
- 3 units Diversity
Required, minimum of 18 units (or double-major)
- Complete 5 courses (15 units)
- CSC 110 can sub for ISTA 130
Understanding uncertainty and variation in modern data: data summarization and description, rules of counting and basic probability, data visualization, graphical data summaries, working with large data sets, prediction of stochastic outputs from quantitative inputs. Operations with statistical computer packages such as R.
An introduction to computational techniques and using a modern programming language to solve current problems drawn from science, technology, and the arts. Topics include control structures, elementary data structures, and effective program design and implementation techniques. Weekly laboratory.
**Programming-intensive Course, College Algebra recommended
At the core of Information Science lies the digital data that is the object of study. This course aims to introduce the tools, techniques, and issues involved with the handling of this data: where it comes from, how to store and retrieve it, how to extract knowledge from the data via analysis, and the social, ethical, and legal issues involved in its use. Throughout the course, students will be given hands-on experience with actual datasets from a variety of sources including social media and citizen science projects, as well as experience with common tools for analysis and visualization. Students will also examine topical case studies involving legal and ethical issues surrounding data.
This course explores the social, legal, and cultural fallout from the exponential explosion in communication, storage, and increasing uses of data and data production. In this class, we emphasize the opposing potentials of information technologies to make knowledge widely available and to distort and restrict our perceptions. In a world of rapid technological change, topics include (but are not limited to): eavesdropping and secret communications, privacy; Internet censorship and filtering, cyberwarfare, computer ethics and ethical behavior, copyright protection and peer-to-peer networks, broadcast and telecommunications regulation, including net neutrality, data leakage, and the power and control of search engines.
- 15 units total
- At least two Intensive Computer courses must have an ISTA/ESOC/LIS prefix
Choose five from:
Intensive Computing Options
An introduction to the mathematical theories of probability and information as tools for inference, decision-making, and efficient communication. Topics include discrete and continuous random variables, measures of information and uncertainty, discrete time/discrete state Markov chains, elements of Bayesian inference and decision-making, Bayesian and Maximum Likelihood parameter estimation, and elementary coding theory.
This course will introduce students to the fundamental concepts and tools used to convey the information contained within large, complex data sets through a variety of visualization techniques. Students will learn the fundamentals of data exploration data via visualizations, how to manipulate and reshape data to make it suitable for visualization, and how to prepare everything from simple single-variable visualizations to large multi-tiered and interactive visualizations. Visualization theory will be presented alongside the technical aspect of the course to develop a holistic understanding of the topic.
This course introduces students to the theory and practice of data mining for knowledge discovery. This includes methods developed in the fields of statistics, large-scale data analytics, machine learning, and artificial intelligence for automatic or semi-automatic analysis of large quantities of data to extract previously unknown and interesting patterns. Topics include understanding varieties of data, classification, association rule analysis, cluster analysis, and anomaly detection. We will use software packages for data mining, explaining the underlying algorithms and their use and limitations. The course will include laboratory exercises, with data mining case studies using data from biological sequences and networks, social networks, linguistics, ecology, geo-spatial applications, marketing and psychology.
This course will be inviting for a wide variety of students from across disciplines, and they will learn how to use industry standard tools and practices to make large data sets usable for scientists and other decision makers. From data collection and preparation, to the creation of big data stores, databases, or systems to make data flow, this course will focus on the practical work needed to prepare big data for analyses across contexts. Students will be introduced to a variety of technical tools for data management, storage, use, and manipulation.
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 are 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.
Students will learn from experts from projects that have developed widely adopted foundational Cyberinfrastrcutrue 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.
Data Warehousing and Analytics In the Cloud will utilize concepts, frameworks, and best practices for
designing a cloud-based data warehousing solution and explore how to use analytical tools to perform
analysis on your data. In the first half of the course, I will provide an overview of the field of Cloud
Computing, its main concepts, and students will get hands-on experience through projects which utilize
cloud computing platforms. In the second half of the course, we will examine the construction of a cloudbased
data warehouse system and explore how the Cloud opens up data analytics to huge volumes of
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 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.
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.
Special topics courses are offered to allow students to explore specialized topics not covered in the program curriculum. Multiple topics might be offered in any given year, and specialized topic descriptions will be advertised by the School for students interested in enrolling in the course. A specific course syllabus will be published prior to the offer of a special topic course.
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.
Machine learning describes algorithms which can modify their internal parameters (i.e., "learn") to recognize patterns and make decisions based on examples or through interaction with the environment. 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.
Programming Intensive Options
This course surveys the techniques central to the modern practice of extracting useful patterns and models from large bodies of data and the theory behind these techniques. Students will learn the purpose, power, and limitations of models, with concrete examples from business and science. Course subject matter may include classification and regression, supervised segmentation and decision trees, similarity/distance metrics and recommender systems, clustering and nearest neighbors, support vector machines, understanding and avoiding overfitting, natural language processing and sentiment analysis, machine learning, neural networks, and AI, and logistic regression.
This course will provide an introduction to informatics application programming using the python programming language and applying statistical concepts from a first semester statistics course. A key goal of this course is to prepare students for upper division ISTA courses by expanding on the skills gained in ISTA 116 and 130 but will be broadly applicable to any informatics discipline. Throughout the semester students will be faced with information application problems drawn from several different disciplines in order to expand their breadth of experience while simultaneously increasing their depth of knowledge of scientific and informatics programming methods. Students will practice problem decomposition and abstraction, gaining experience in identifying commonly occurring information processing issues and in applying well-known solutions. In addition, students will design their own algorithmic solutions to problems and will learn how to effectively compare different solutions, evaluating efficiency in order to choose the best solution for a given problem. Periodic code reviews will be held in order to expose students to a range of different solution methods, which will aid them in discovering weaknesses in their own work and will improve their ability to communicate with others on technical topics. The course will include an introduction to the python scientific computing libraries and other statistical packages. Additional course topics will include the use of version control systems, software profiling, general software engineering practices and basic shell scripting.
Natural language processing (NLP) is the study of how we can teach computers to use language by extracting knowledge from text, and then use that knowledge in some meaningful way. In this introductory course, we will examine the fundamental components on which natural language processing systems are built, including frequency distributions, part of speech tagging, syntactic parsing, semantics and analyzing meaning, search, introductory information and relation extraction, and structured knowledge resources. We will also examine pragmatic concerns in processing raw text from real-world sources.
The methods and tools of Artificial Intelligence used to provide systems with the ability to autonomously problem solve and reason with uncertain information. Topics include: problem solving (search spaces, uninformed and informed search, games, constraint satisfaction), principles of knowledge representation and reasoning (propositional and first-order logic, logical inference, planning), and representing and reasoning with uncertainty (Bayesian networks, probabilistic inference, decision theory).
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.
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 the application of GIS and related technologies for both the natural and social sciences. Conceptual issues in GIS database design and development, analysis, and display.
Continuation of MATH 122B or MATH 125. Techniques of symbolic and numerical integration, applications of the definite integral to geometry, physics, economics, and probability; differential equations from a numerical, graphical, and algebraic point of view; modeling using differential equations, approximations by Taylor series. A graphing calculator is required for this course. We recommend the TI-83 or TI-84 models. Calculators that perform symbolic manipulations, such as the TI-89, NSpire CAS, or HP50g, cannot be used. Examinations are proctored.
An algorithmic approach to solving systems of linear equations transitions into the study of vectors, vector spaces and dimension. Matrices are used to represent linear transformations and this leads to eigenvectors and eigenvalues. The precise use of definitions plays an important role. Examinations are proctored. This course is required in the math major and prepares students to take Math 323. It is a prerequisite to the majority of the higher level courses in mathematics.
Computational Arts & Medias
- 3 units total
Choose one class from:
This course explores the process of creating interactive computer art by teaching the essential principles of programming.
This course introduces fundamental theories, principles, and practices of 3D digital modeling, rendering, and rapid prototyping. Students are given a thorough overview of 3D modeling techniques including: production of geometric and organic surfaces and forms using both NURBs and polygon construction, texturing, and lighting. Minimal materials fee for rapid prototyping assignment.
This course will lay a foundation for understanding how stories shape communities, identities, memories, and perspectives on our lives. In addition, this course will provide opportunities for the theoretical analysis of self representation, composite narratives on behalf of others, cultural heritage, and memories as they are preserved and performed within stories and through narrative. Influences on digital digital storytelling such as the sociocultural context, the institutional contexts of production the audience, and the needs or goals of the digital storyteller will be examined. Students will be required to call on their own intellectual, emotional, and imaginative processes, as well as to develop their own skills in digital storytelling, interviewing, oral history collection, and the use of relevant digital storytelling tools.
We are living in a time when nearly everyone has the means to make movies, music and photos using just their own personal tools like smartphones, iPads, and similar mobile gadgets. This course will develop and refine skills and understanding of multimedia in contemporary culture. Offering a survey of innovative works in film and information arts, this course will allow students a hands-on opportunity to respond to concepts covered in class using self-produced media. This course will address how information functions in time-based forms of multimedia and video in this era of interactive information and displays. Drawing on historical precedents in the media and computational arts, this course focuses on both linear and non-linear approaches of using image, sound and text to create critical and creative works that function in a the context of social media and our contemporary digital society. How and why do certain images, music or films affect us so profoundly? We will address this question through a study of the components of media literacy that include: Production, Language, Representation, and Audience. These concepts will be examined through a cross-section of writers including: Marshall McLuhan, John Berger and Susan Sontag.
This course provides an introduction to game design and teaches students the fundamental concepts for creating games. Students will survey many different games, exploring the issues game designers face when designing games in different genres. Students will participate in a series of game design challenges and will be responsible for designing and prototyping simple games using a game building tool. Students will present their solutions to these challenges in front of the class for general discussion and constructive criticism.
This course examines the ways in which computing and information science support and facilitate the production and creation of art in current society. A particular focus of the course will be to discuss how artists have used advances in technology and computing capacity to explore new ways of making art, and to investigate the relationships between technical innovation and the artistic process. This class satisfies a Tier II: Arts General Education Requirement. Alternatively, this class can be applied towards the ISTA BA/BS and ISTA minor. Tier II Gen-eds can be double-dipped with a minor but not a major.
This course will provide the student with the information and experience necessary for the creation and manipulation of digital audio. Students will have the opportunity to experience the music-making process with the technology tools and techniques that are common in both home and professional studios. The class will make use of a variety of software packages designed for contemporary music production, explaining the universal techniques and concepts that run through all major software programs. Topics will include musical analysis, MIDI control, synthesis techniques, audio editing, and audio mixing. Lab assignments will emphasize hands-on experience working with musical hardware and software to provide the necessary skills to create music based on today's musical styles. The course provides the foundation for further study, creative applications, and personal expression.
This course provides an introduction to software and hardware packages that allow students to explore rapid prototyping, object design, and physical computing using Computer-Aided Design (CAD) software, 3D printing technology, laser cutting, and Arduino microcontrollers. The processing language will also be introduced, and used for visualization and interfacing. This interdisciplinary course combines elements of computer science, electrical engineering, mechanical design, robotics, and visualization.
This is a hands-on practical course where fluency is largely built through experience building projects, rather than written exams. This course will require extensive technology training and substantial reference to open resources on the web. This course includes a team-based design competition as a final project.
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.
This course continues the exploration of creative coding that began in ISTA 303. Students will develop experimental and creative works based, in part, on techniques from the fields of human-computer interaction, computer vision, virtual reality, machine learning, and other disciplines that have the potential to impact our culture through the introduction of new technologies. Aside from gaining technical proficiencies needed to engage with these topics (e.g., software engineering, physical computing techniques, familiarity with multimedia packages and libraries), students will have the opportunity to explore the use of novel interaction devices (e.g., Kinect, Wii, LeapMotion, Glasses, and Oculus Rift) as well as to experiment with a range of digital media environments (e.g., projection mapping, live coding, sonification, mobile devices, physical sensors,augmented reality, immersive systems). Moreover, students will become more familiar with the history and current state of the fields of new media art and creative coding. Students will read widely from journal articles and from media arts conference and festival proceedings, and will be expected to document their own work in a clear, professional manner, both through writing assignments and the creation of an online portfolio of creative projects. By the end of this course students will have the ability to participate meaningfully (through the implementation and documentation of creative projects) in contemporary discourse regarding art and technology.
Fundamentals of processing of natural language and computational linguistics.
- 3 units total
Choose one class from:
The focus of this course is on how social information is produced though language and identity work online, focusing on patterns of talk and interactional rules and practices across contexts (e.g., text-messaging, online communities, personal identity work, and transnational blogs). As part of this focused study of talk, this course will explore how online language use can create, maintain, reproduce, or disrupt roles and related norms (e.g., those of a friend, student, expert, or political agent), as well as identities and social categories (e.g., gender, sexuality, race, disability, or nationality). This course will also focus on the broader discourses on a 'global' level, examining human collaboration online for practices tied to elitism, the movement of social capital, racism, power, and the cultural production of inequalities.
This course will lay a foundation for theoretical analyses of how people socially create and negotiate information in the digital age. In addition, this course investigates a variety of approaches ranging from critical/cultural studies to positivist/behavioral research, considering the differing ways to think about social life and information in contemporary times. Broader paradigmatic assumptions (e.g., feminist theory, systems research) as well as specific theoretical topics (e.g., interactivity, mobility, telecommunity) will be examined. In addition, this class will survey the theoretical underpinnings of new media research across a variety of topic areas to include gaming, digital labor, communities, and global culture online.
In the early 21st Century, we see publishing in the throes of dramatic changes, from print to electronic most obviously but also in who authors books, the economics of publishing, and how books get to readers. These changes remind us that the dynamics of the movement of the written word to its audience are an integral part of the society in which books are written, produced, and circulate. This 3-credit course takes an historical perspective on publishing, which we will define as the processes by which books come into being in multiple copies and are distributed to reach their audiences. We will start with ancient societies all over the world, and we will investigate the circumstances across societies in which books distinguish themselves from administrative records and begin to serve the needs of the literate elite. We will examine the way the physical form of the book and the technologies for producing it arise from the circumstances of each society, and in turn, how that physical format conditions the character of books and their use. We will trace the rise of publishing practices and identify the factors necessary for the reproduction and distribution of books to form an actual trade in books in varying societies. As we work our way from the ancient world to the early modern world, we will compare publishing practices in different societies and explore commonalities and differences in the relationships that develop between the creation, reproduction and distribution of books. Of particular focus will be our comparison of the rise of publishing and book trades in Europe, Asia, and the Arab world before 1450. After the introduction of printing with metal moveable type in Europe, associated with Gutenberg in approximately 1450, we will have an opportunity to observe the changes that this new technology makes in publishing and the book trade, by comparing the mature manuscript book trade of the late middle ages to that of the hand-press book publishing of early modern Europe. In the run up to the mid-term we will see the effect of monetary capital on the book trades and the shaping of the function of the publisher (although not yet called that). We will also examine related publishing matters such as art and decorative print production as well as the emergence and social role of pamphlets.
This course will look at how commerce in information content (websites, books, databases, music, movies, software, etc.) functions. We will discuss things like switching costs, net neutrality, the long tail, differential pricing, and complementary goods. We will address the following sorts of questions: - Why do so many information producers give away content (such as "apps" for mobile phones) for free? How do companies (such as Google and Facebook) stay in business when no one has to pay to use their services? - What are contemporary practices with regard to purchasing access to information content? For instance, why do we tend to buy books, but only rent movies? Also, how do new modes of content provision (such as Pandora and Spotify) change the way that creators get paid for their work? - Why are there restrictions on how information content can be used? For instance, why can you play the DVD that you bought on your trip to Europe on the DVD player that you bought at home in the United States? But why should anybody other than an economist care about the answers to these sorts of questions? The world now runs on the production, dissemination, and consumption of information. All of us constantly access all sorts of information, through all sorts of devices, from all sorts of providers. We read and interact with websites, we query databases, and we communicate with each other via social media. These sorts of activities permeate both our personal and professional lives. In order to successfully navigate this digital world, information consumers, information producers, and information policy makers need to understand what sorts of information goods are likely to be available and how much they are likely to cost. We cannot learn enough about digital commerce simply by studying the various information technologies that are now available to create and disseminate information content. What matters most is how people choose to spend their time using these technologies, and what sorts of content can provide earning potential for its creators. What also matters are the unique properties of information content that make it very different from other sorts of goods. For instance, while only one person at a time can drive a particular car or eat a particular hamburger, millions of people can simultaneously read the same book, listen to the same song, and use the same software. These are issues that are part and parcel to living, working, purchasing, and being entertained in an eSociety; these are the issues addressed in this course.
This course provides a powerful introduction to some of the criminal activities taking place in relation to digital information, big data, and social media. Related to the exploration of criminal activity in an eSociety, this course focuses on some of the most common legal issues faced today, with regard to our own personal data (e.g., our health histories, our genetic make up, our cloud-based photos and messages, our past) and in relation to organizational or political data on social media and in society. In this course, students as future technologists, will be exposed to the 'dark side' of this current 'information society' (e.g., deception, cybercrime) as well topics such as big data privacy, digital disruptions, consumer data and related sales, gaming protections, youth safety online, big science data sharing issues and related trust, digital security, as well as how certain groups -- law firms, advocacy groups, marketing professionals, and political or lobbying groups -- are mining data for particular use. Students will be required to consider recent court cases and contentions around the use, management, and protection of data in society as well as the risk humans face in this digital information and mediated age.
This course introduces key concepts and skills needed for those working with information and communication technologies (ICT). Students will be exposed to hardware and software technologies, and they will explore a wide variety of topics including processing and memory systems, diagnostics and repair strategies, operating systems in both desktop and mobile devices. As part of this course, students will consider current technological disruptions, those issues emerging as technologies and social needs collide. Students we also learn about design issues and user needs tied to mobile or computer applications and web-based tools, sites, games, data platforms, or learning environments.
This course is a broad survey of the processes, theories, and practices around instructional technologies that can be applied to various learning situations. Students will study and apply research and theory on technology adoption, analysis, and support, along with instructional design, learning theories, and training needs analysis. The course will also guide students through the design of effective tech-supported training, technology selection dependent upon learning situations, evaluation of chosen learning technologies, and considerations in instructional technology piloting, adoption, and support. By the end of this course, students will make educated decisions about technology implementation across diverse learning environments.
This course focuses on the ethical issues that arise in the context of new and emerging information technologies-- e.g., threats to privacy of ubiquitous technological surveillance, limitations on access created by digital rights management. The course will use the framework of ethical theory to analyze these issues and to propose policy solutions. The goal of the course is to give students the necessary theoretical foundation to be involved in the evaluation and construction of information policies at the local, national, and international level. The course will focus on three core areas where digital dilemmas arise--information access, information privacy, and intellectual property. In order to achieve depth as well as breadth, the course will put one of these issues at the center and discuss the others in relation to it. So, for instance, the course may focus on Intellectual Property looking at the threats and benefits of IP to privacy and access. This syllabus provides an overview of the range of topics that may be discussed.
Security is about protecting assets, such as money and physical possessions. For instance, we use walls, locks, burglar alarms, and even armed guards to keep other people from stealing and/or destroying our stuff. These days, information is typically one of our most important assets. Thus, we have to worry about the possibility of other people stealing and/or destroying it. For instance, criminals threaten our data with scareware or ransomware in order to extort money from us.
In today's digital society, people have access to a wide variety of information sources and scientific data. In this course, students will learn about the role of science and scientific data in society, and they will consider means for making science information findable and understandable for a wide variety of audiences. This course will provide students an interdisciplinary experience for considering science data and how that information gets shared across contexts.
Special topics courses are offered to allow students to explore specialized topics not covered in the program curriculum. Multiple topics might be offered in any given year, and specialized topic descriptions will be advertised by the School for students interested in enrolling in the course.
Special topics courses are offered to allow students to explore specialized topics not covered in the program curriculum. Multiple topics might be offered in any given year, and specialized topic descriptions will be advertised by the School for students interested in enrolling in the course.
This course will explore broad research paradigms and theoretical approaches that inform contemporary social research, varying study designs, as well as the systematic methods utilized in differing types of data analyses. Though this course will introduce research processes across the academic spectrum, quantitative analysis of both small and large data sets will be emphasized. Therefore, students will learn about basic statistical analyses and will be introduced to the emerging worlds of data science and social media analytics. Students will also consider related topics such as data visualization or research presentations.
- Independent Study, Directed Research, Internship or ESOC 480: Digital Engagement (3 units)
- ISTA 498 (3 units)
Elective courses can be taken if needed to reach 120 total units or 42 upper-division units.