Below is information about Fall 2021 courses offered for the IS major.
iSchool Course Catalog and the Campus Course Schedule provide additional information on our courses. Please make sure to read the section text in the Course Explorer Schedule under each section to find details on restrictions.
General Restriction Information:
- Many IS and INFO courses have restrictions until a certain date to allow for declared Information Science Majors to have priority registration into these courses. The majority of the courses will open (restrictions lifted) one week before classes begin in the Fall.
- Informatics (INFO) Minors will have access to some of the IS courses starting July 13 by noon. Please refer to your INFO Minor advisor for additional information.
- Undergraduate Sections: Students must register for the Undergraduate section for courses when relevant.
Questions may be sent to ischool-is@illinois.edu
IS Course Websites
| Course |
Name |
Section |
LMS |
| IS 100 |
Exploring the iSchool |
A |
Canvas |
| IS 100 |
Exploring the iSchool |
B |
Canvas |
| IS 100 |
Exploring the iSchool |
ICT |
Canvas |
| IS 100 |
Exploring the iSchool |
ONL |
Canvas |
| IS 101 |
Intro to Information Sciences |
AD1 |
Canvas |
| IS 101 |
Intro to Information Sciences |
AD2 |
Canvas |
| IS 101 |
Intro to Information Sciences |
AD3 |
Canvas |
| IS 101 |
Intro to Information Sciences |
AL |
Canvas |
| IS 101 |
Intro to Information Sciences |
AD4 |
Canvas |
| IS 199 |
iSchool Explore, Engage, Devel |
EE1 |
Canvas |
| IS 199 |
iSchool Explore, Engage, Devel |
EE2 |
Canvas |
| IS 199 |
Social Hist of Games & Gaming |
SHG |
Canvas |
| IS 202 |
Social Aspects Info Tech |
AD1 |
Canvas |
| IS 202 |
Social Aspects Info Tech |
AL1 |
Canvas |
| IS 202 |
Social Aspects Info Tech |
AD2 |
Canvas |
| IS 202 |
Social Aspects Info Tech |
AD3 |
Canvas |
| IS 202 |
Social Aspects Info Tech |
AD4 |
Canvas |
| IS 202 |
Social Aspects Info Tech |
AD5 |
Canvas |
| IS 202 |
Social Aspects Info Tech |
AD6 |
Canvas |
| IS 203 |
Analytical Fndts Info Problems |
C |
Moodle |
| IS 203 |
Analytical Fndts Info Problems |
AO |
Canvas |
| IS 203 |
Analytical Fndts Info Problems |
BO |
Canvas |
| IS 204 |
Research Design Info Sciences |
CO |
Canvas |
| IS 204 |
Research Design Info Sciences |
AO |
Canvas |
| IS 204 |
Research Design Info Sciences |
BO |
Canvas |
| IS 205 |
Program for Info Problems |
AO |
Canvas |
| IS 206 |
Intro Database Concepts & Apps |
AB1 |
Moodle |
| IS 206 |
Intro Database Concepts & Apps |
AB2 |
Moodle |
| IS 206 |
Intro Database Concepts & Apps |
AL |
Moodle |
| IS 226 |
Introduction to HCI |
AO |
Moodle |
| IS 229 |
Web Design Fundamentals |
AO |
Moodle |
| IS 236 |
User Research & Evaluation |
AO |
Moodle |
| IS 305 |
Program for Info Problems II |
A |
Canvas |
| IS 308 |
Race, Gender, and Info Tech |
A |
Canvas |
| IS 309 |
Computers and Culture |
AO |
Moodle |
| IS 311 |
History Fndts of Info Society |
A |
Canvas |
| IS 390 |
Consulting Info Professionals |
CIP |
Moodle |
| IS 390 |
Race & Digital Studies |
RDS |
Moodle |
| IS 390 |
Privacy and Info Technology |
PIT |
Canvas |
| IS 400 |
Colloquium |
AO/AO1 |
Canvas |
| IS 400 |
Colloquium |
AC/JS |
Canvas |
| IS 401 |
Intro to Network Systems |
ACG/ACU/AO |
Canvas |
| IS 403 |
Children's Materials |
BOG/BOU |
Moodle |
| IS 403 |
Children's Materials |
AOG/AOU |
Moodle |
| IS 407 |
Introduction to Data Science |
AOG/AOU |
Canvas |
| IS 410 |
Storytelling |
AO2/AOG/AOU |
Canvas |
| IS 417 |
Data Science in the Humanities |
ACG/ACU |
Moodle |
| IS 423 |
Early Literacy |
AOG |
Moodle |
| IS 423 |
Early Literacy |
BOG |
Canvas |
| IS 426 |
Museum Informatics |
ACG/ACU |
Moodle |
| IS 430 |
Foundations Info Processing |
AO2/AO4 |
Canvas |
| IS 430 |
Foundations Info Processing |
BO2/BO4 |
Canvas |
| IS 445 |
Data Visualization |
AOG/AOU |
Moodle |
| IS 445 |
Data Visualization |
ACG/ACU |
Moodle |
| IS 446 |
Systems Analysis and Design |
AOG/AOU |
Canvas |
| IS 453 |
Info Books & Resources Youth |
AO2/AO4/AOU |
Canvas |
| IS 455 |
Database Design & Prototyping |
AOG/AOU |
Moodle |
| IS 455 |
Database Design & Prototyping |
AO |
Canvas |
| IS 457 |
Data Storytelling |
ACG/ACU |
Moodle |
| IS 457 |
Data Storytelling |
AOG/AOU |
Moodle |
| IS 467 |
Ethics & Policy for Data Scien |
ACG/ACU |
Moodle |
| IS 471 |
Instruc Strat Tech Info Prof |
AO2/AO4 |
Moodle |
| IS 471 |
Instruc Strat Tech Info Prof |
AC2/AC4 |
Canvas |
| IS 490 |
Social Media and Global Change |
SMG/SMU |
Moodle |
| IS 490 |
Professional Skills in Info Sc |
PSG/PSU |
Canvas |
| IS 496 |
Design for Social Interact Exp |
DE3/DE4 |
Moodle |
| IS 497 |
Database Admin & Scaling |
DA |
Moodle |
These classes are specifically required within the IS degree.
- Lecture: Online | Discussion: On Campus
- Lecture: Online | Discussion: On Campus
- Lecture: Online | Lab: On Campus (Online Lab Option)
IS & INFO ELECTIVE COURSES
- Lecture: Online | Lab: On Campus (Online Lab Option)
Data Science Discovery is the intersection of statistics, computation, and real-world relevance. As a project-driven course, students perform hands-on-analysis of real-world datasets to analyze and discover the impact of the data. Throughout each experience, students reflect on the social issues surrounding data analysis such as privacy and design. (Register under STAT 107)
A survey of the history of gaming from the ancient world through the twentieth century, and its impact on science, society, and culture. For more information about this major, please visit: http://go.ischool.illinois.edu/BSIS. Questions may be sent to ischool-is@illinois.edu This course will count as GenEds for iSchool students (other students should confirm with their college): Humanities & the Arts: Historical Perspectives.
Explores the data science pipeline from hypothesis formulation, to data collection and management, to analysis and reporting. Topics include data collection, preprocessing and checking for missing data, data summary and visualization, random sampling and probability models, estimating parameters, uncertainty quantification, hypothesis testing, multiple linear and logistic regression modeling, classification, and machine learning approaches for high dimensional data analysis. Students will learn how to implement the methods using Python programming and Git version control. (Register under STAT 207) Prerequisite: STAT 107 or consent of instructor. Quantitative Reasoning II
This course introduces students to fundamental theories and techniques in Human-Computer Interaction (HCI). This course presents basic tools and methods for creating, designing, prototyping, and evaluating user interfaces to computing applications and web sites. Students will explore course content by conducting individual and group hands-on projects. Assignments involving prototyping can be implemented by self-selected solutions, e.g. Axure, JavaScript. Students from all backgrounds are welcomed.
This course will teach students about building inclusive interactive systems. They will learn to gather and understand user requirements and needs for a wide range of user populations, especially those that are under-served (e.g., children, older adults, people with disabilities), apply inclusive design frameworks and principles, and design, develop, evaluate and improve interactive prototypes in an iterative manner.
This course will teach students about user research and evaluation. They will learn to apply various user research methods, gather and understand user requirements and needs for a wide range of user populations, especially those that are under-served (e.g., children, older adults, people with disabilities), conduct user evaluations of prototypes and interactive systems, and communicate effectively about the research insights and make actionable design suggestions.
- Location: On Campus | Online (Hybrid)
Continuing coverage of common data processing and computing methods in the information sciences. Building on programming skills from IS 205, additional programming patterns will be explored, and additional tools like the command line and version control will be explored in the context of information problems.
Course will be in Python. Some Python review will be provided, but students without prior experience in Python should contact the school or instructor for review material. Prerequisite: IS 205, or CS 101, or CS 105, or CS 125, or ECE 120, or equivalent. Basic programming (Python) proficiency required. General Education: Quantitative Reasoning I
Consulting Info Professionals: This course is designed to provide practical and hands-on training by simulating consulting projects. Students will develop proficiencies in problem-solving, team management, storytelling, and professional communications. As they learn the theories and practices of consulting engagements, students will have opportunities to discover how their knowledge in information sciences can be applied to various types of consulting services. The transferrable skills acquired in this class are applicable to other workplace settings. Location: On Campus
Privacy & Info Technology: This course is designed to be an introduction to data privacy to a wide audience and help information science students learn how data privacy has evolved as a compelling concern to public and private organizations as well as individuals. This course will provide an overview of privacy theories and the challenges that information technology innovations poses to privacy. Course content will focus on enhancing data professionals’ knowledge of privacy vulnerabilities in the digital age and how they can integrate strategies and techniques to minimize privacy threats. The curriculum includes privacy-by-design principles; legal and regulatory protections, implementing data- and process-oriented strategies to support privacy policies; and managing threats from emerging technologies such as social media, AI, and, surveillance technologies.
- Location: On Campus | Online (Hybrid)
Venue for presentation and discussion of research and professional activities by faculty, students, staff, and guest speakers.
This course provides a deep hands-on sociotechnical dive into technology including electronics, software, and networks culminating in a holistic understanding of networked information systems. The course also explores the methodological landscape of networked information systems including theoretical assumptions, research methods, and research techniques. Throughout, students will be introduced to, and make active use of, skillsets, frameworks, and standards employed by a wide range of information professionals in selecting, co-designing, appropriating, and innovating-in-use networked information systems.
Evaluation, selection and use of books and other resources for children (ages 0-14) in public libraries and school media centers; explores standard selection criteria for print and nonprint materials in all formats and develops the ability to evaluate and promote materials according to their various uses (personal and curricular) and according to children's various needs (intellectual, emotional, social and physical).
This course introduces students to data science approaches that have emerged from recent advances in programming and computing technology. They will learn to collect and use data from a variety of sources, including the web, in a modern statistical inference and visualization paradigm. The course will be based in the programming language R, but will also use HTML, regular expressions, basic unix tools, XML, and SQL. Supervised and unsupervised statistical learning techniques made possible by recent advances in computing power will also be covered.
Fundamental principles of the art of storytelling including techniques of adaptation and presentation; content and sources of materials; methods of learning; practice in storytelling; planning the story hour for school and public libraries and other public information settings; and audio, video, and digital media.
Human culture provides an ideal testbed for students exploring data science, because the interpretive challenges that lurk beneath the surface in other domains become starkly visible here. For instance, cultural materials usually come to analysts as unstructured texts, images, or sound files, forcing explicit decisions about data modeling and feature extraction. Cultural questions also highlight the importance of interpreting statistical models in relation to a social context. Last but not least: songs, poems, and stories confront us with vivid problems that are inherently fun to explore. This course will start by reviewing descriptive and inferential statistics, and build up to applications of supervised and unsupervised machine learning. We will apply those methods to a range of cultural materials using them to model the pace of stylistic change in popular music, for instance, and the representation of gender in fiction.
The course examines various ways that information technologies are and might be used in museums and other cultural heritage settings. Museum websites, visitor apps, interactive exhibits, and uses of digitized and federated collections are explored. Students gain an introduction to Design Thinking by working on a final project that involves the development of a novel computational resource. Students are encouraged to approach class topics from their individual backgrounds in the humanities, sciences, or social sciences.
Data visualization is crucial to conveying information drawn from models, observations or investigations. This course will provide an overview of historical and modern techniques for visualizing data, drawing on quantitative, statistical, and network-focused datasets. Topics will include construction of communicative visualizations, the modern software ecosystem of visualization, and techniques for aggregation and interpretation of data through visualization. Particular attention will be paid to the Python ecosystem and multi-dimensional quantitative datasets.
Covers how to evaluate, select and manage information systems that will be used in the daily operation of libraries and information centers. Includes the systems used by technical staff and the information consumers. Course will focus on information as a product. Attention is given to the operation of an organization as a whole and the impact of change on the integration of resources, work flow and usability. Formal methods for modeling systems, and industry practice techniques of analysis are used to address these problems and opportunities.
The course provides students with both theoretical and practical training in good database design. By the end of the course students will create a conceptual data model using entity-relationship diagrams, understand the importance of referential integrity and how to enforce data integrity constraints when creating a database. Students will be proficient in writing basic queries in the structured query language (SQL) and have a general understanding of relational database theory including normalization.
- Location: On Campus & Online
An introduction to understanding data as a source for storytelling and to telling stories based on data. This process will include understanding and analyzing data sets to find informative aspects, changes, or contrasts that will provide the basic information for developing stories. Course participants will learn storytelling concepts, narrative theories, and performance techniques and develop stories in a collaborative workshop style. Students will work with data visualization toolkits, which will involve variable levels of coding and skill. By using storytelling techniques with data, students can develop, and tell well-evidenced stories, organizations can make better data-driven decisions.
The course will address common ethical challenges related to data including privacy, bias, and data access. These challenges will be explored through real-world cases of corporate settings, non-profits, governments, academic research, and healthcare. The course emphasizes the complexity of ethical decision-making and that trade-offs between priorities are often necessary. The course also considers how the burdens of addressing ethical concerns should be distributed among stakeholders. Students will be introduced to a range of relevant policy responses at the organizational, institutional, governmental,and supranational levels.
- Professional Skills in Info Sc, Section PSU: In this course, students will learn about and develop professional skills in the information sciences, a broad and interdisciplinary field at the intersection of people, information, and technology. Students will engage in professional skill building for a broad range of employment in the information professions with a specific focus on internship and career readiness. Restricted to Information Sciences majors until x/xx/21 by noon. For more information about this major, please visit: http://go.ischool.illinois.edu/BSIS. We will not provide overrides, please wait until the course restrictions are removed. Questions may be sent to ischool-is@illinois.edu. Location: On Campus
- Social Media & Global Change, Seciton MSU: This course covers the impact of global and national computer networks on politics, culture, and social relations during a time of upheaval and revolutionary change. Topics may include the new social media, the politics and culture of the internet, hacktivism, cyber warfare, and mobile telephony and their role in the formation, dissemination, manipulation, and suppression of public opinion in Russia/Eurasia, the China/Pacific region, Central/South America, as well as Africa, Iran, and the Middle East.
Designing for Social Interactive Experience: From Facebook to TikTok, technology is a double-edged sword. On the one hand, technical solutions are designed to address real-world problems and to promote positive social change; on the other hand, technologies might have unwanted impacts on the society. This course covers theories, methods and state-of-the-art works in designing interactive technologies (e.g., conversational agents) for enhancing desirable social experiences. The course is taught with lecture, discussion, and *in-class studio participation (*when taught on-campus). Student will explore how to design conversational agents to mediate social interactions between different stakeholders (e.g., public service providers and community members) in a variety of contexts. The course prepares students to work as practitioners, academicians, or system designers in their future endeavors. Students in the 3-credit version of the course could choose to skip some of the assignments, e.g., programming tasks in the final project or paper reviews that are required in the 4-credit version of the course. Students can discuss with the instructor about which assignments to skip depending on their interests and background.
Database Administration & Scaling for IS. Prerequisite: IS 455 - Database Design & Prototyping. Involves database administration (DBA) broadly relevant to computational information science work. Explores several types of scalable database engines, using popular NOSQL and SQL products. Develops practical skills for managing reliable DBMS for production systems or to create analytically-focused NOSQL derivatives. Small student teams will experiment and present findings to the class, with student-directed inquiry encouraged. Readings, discussions, and situational problems drawn from real scenarios will involve managerial, ethical, and other aspects of the human side of professional DBA work. Some intro to virtual machines and cloud systems administration is included. Requires Instructor approval, Email John Weible at jweible@illinois.edu.
Introduction to the craft of publishing historical materials, with a special focus on how to publish the past in the digital age. Assignments will include historical and methodological readings, as well as hands-on instruction in digital publishing techniques. Skills taught include historical research, content development, project management, and copyright analysis. General Education: Humanities – Hist & Phil
INFO 199 Seminar: Intro to Video Game Industry (3 hr.)
Lecture: Online | Discussion: On Campus
The course is designed to introduce individuals to the Video Game Industry, its history, current status, processes and future. It includes a survey of the positions and information about how to prepare to enter the Industry. Students need to register for lecture and one of the discussion sections.
The ability to communicate effectively in multiple types of media is a crucial part of literacy in our society. In this course, students will explore the intersections of various media: print, film, images, sound, etc. Students will consider the ways in which writing--as an object and as a practice--is shaped by multimodal interactions. Also integrates practical activities with broader theoretical issues in order to provide effective strategies for designing multimedia presentations, projects, and texts that integrate photography, video, and sound.
Location: On Campus
Digital media is an immensely pervasive and powerful form of communication that despite its rapid growth has yet to reach most of the world's population. This lecture-based survey course for undergraduates traces the history and formation of personal computing and the Internet, the development of virtual communities and virtual worlds, evolving forms of digital representation and communication, digital visual cultures, features of new media industries, and the rise of participatory media. Evaluation and assessment is based on written exams, quizzes, class discussion in section, and practice-based assignments using new media technologies such as wikis, blogs, games, and digital video. Emphasis is on mastering key concepts of digital media through theory and history, and on critical discussion of distinctive features of digital media objects. Lectures and discussion sections are held in computer-equipped classrooms.
Location: On Campus
This interdisciplinary course uses the lens of gender critique and pairs it with social activism to provide students analytical tools to engage with, reshape, and create digital cultures. Examines recent research and public policies related to the gendered, raced, and classes dimensions of digital cultures and inequality; the broad range of labor issues embedded in the growing income disparity related to digital cultures; the various ways that digital inequality has been defined by public policy, sociologists, and activists, and real examples of collective activism and social change related to emerging technologies.
- Makerspace Intro: Open Studio - This course introduces learners to a variety of rapid prototyping and fabrication techniques in collaboration with the CU Community Fab Lab. Weekly class lecture will introduce students to trends and ideas in Makerspaces, Peer-to-Peer learning, design processes, creativity, computational thinking, and practicing makers. Each week students will be provided a general project prompt and set to work with a tool area in response to a simple design exploration challenge. Over the course of the semester they will have an opportunity to become familiar with the basics of several advanced small-scale manufacturing tools, such as 3D printers, laser engravers, digital embroidery machines, graphic drawing tablets and small board electronics. While there is no studio fee students will be expected to find, purchase, borrow or otherwise provide their own materials for several projects. The class will have both group and independent work and make use of Moodle for assignment hand-in and peer-feedback. Please note that this course will emphasize self-guided learning and time management, students will need to rely on online tutorials and information resources to explore methods and complete much of the work in a rapid-response fashion; students will need to come into FabLab open hours outside of normal lab times to complete projects. Projects will be small and contained, in order to allow for exposure to several tools and mediums. Students who have taken a prior Makerspace course at the FabLab are eligible to participate in this class, but it is also not a requirement. This class is for undergraduate students only, at the sophomore level or higher. Graduate students should enroll in INFO 490 section ALG (CRN 68913). Lecture: Online | Lab: On Campus
- Makerspace: Escape Rooms - This course will explore the intersection of storytelling, interaction design, and user experience through the design of escape rooms. In the past couple years escape rooms have been on the rise, changing from simple locked boxes in an open room to complex adventures spanning multiple rooms involving electronics, sound design, storytelling, and even live actors. This class will be primarily focusing on the manufacturing and electronics work that goes into making an immersive escape room experience. Over the span of the course, students will become familiar with the basics of several advanced small-scale manufacturing tools, such as laser engravers, electronic cutters, and 3D printers/scanners. The primary focus, however, will be a more in depth exploration of small board electronics – such as Arduino and IoT programming – and hardware – such as sensors, servos, LEDs, and other components. This section is for undergraduate students at the sophomore level or higher only. Graduate students should register for CRN 62221. Course materials and assignments will be hosted on Moodle at learn.illinois.edu. Location: On Campus
- Machines, Data and the Python - INFO 490 MH2: Machines, Data and the Python (https://uicourses.web.illinois.edu/info490mh2) continues where the INFO 490 MH tour (https://uicourses.web.illinois.edu/info490mh) left off. You will learn advanced techniques in data science and be introduced to machine learning algorithms. You will also continue to improve your Python knowledge as well as your software development skills including how to architect large scale data processing pipelines. Although this an an applied course (you will learn by doing), you'll also learn how and why something works. In many cases, you will first write a reduced implementation before using an established library. Mastering the ability to write software to gain insights from data will help drive your research and career. The last four weeks of the class will be spent on a data driven project that will give you a chance to work on your own interests and showcase your knowledge and skills. The class will be taught on-line and be scheduled asynchronously (you decide where it best fits in your week). Prerequisites • Junior/Senior/Graduate Standing • Taken INFO490 MH Intro to Prog for Data Sci OR have at least 2 years of programming experience using Python • Already comfortable with Numpy, Pandas, Matplotlib, NLTK • Voraciously willing to do the necessary work to fill in any knowledge gaps • Enjoy contributing and learning in an on-line environment • the ability to create a boolean expression for these prerequisites Contact the instructor (Mike Haberman, haberman@illinois.edu) if you want to be considered for one of the slots in this course. There are limited seats available and the instructor will use the following criteria to prioritize who gets to enroll: 1) Last semester seniors/grad students who either took INFO 490 MH or have the prereqs 2) Last year seniors/grads who either took INFO 490 MH or have the prereqs 3) Juniors who took INFO 490MH 4) Sophomores who took INFO 490MH 5)Juniors with the prereqs 6)Sophomores with the prereqs
- Intro to Prog for Data Science - NOTE: Students must be enrolled in this course by 12 pm on Thursday, Sept. 5, 2019. Enrollment in this course will be shut down at that point and no new students will be allowed to enroll. Introduction to Programming for Data Science is for students who want to learn about solving problems common in data sciences but have little or no programming experience. The class is asynchronous (students can access material on-line but within specified timeframes) and taught online. Data Science lies at the intersection of statistics and computer science and focuses on extracting information from data. This class will immerse students on topics of software construction, design, programming paradigms and the semantic and syntax of the Python language and then focus on some of the necessary workflows to move raw data into information. The class will explore common Python modules (libraries) used in data science, natural language processing, statistics, mathematics, data management (acquiring, cleaning, reshaping, organizing, persisting) and visualizations. Sample course material can be viewed at: https://info490fa19.web.illinois.edu/ This is ONLINE and ASYNCHRONOUS (there is no regular meeting day/time). Students who have completed INFO 490 RB Foundations of Data Science or INFO 490 RB2 Advanced Data Science should not register for this course as it will be considered duplicate credit (which does not count towards graduation).
- Computer Music - Introduction to the multiple ways computers are used in music, with an emphasis on digital sounds synthesis and composition. Elements of acoustics, psychoacoustics, and programming are introduced in order to allow students to use and modify the existing software DISSCO/Sound Maker developed at UIUC. Students are required to bring a laptop to class. 3 undergraduate hours. Location: On Campus