IS 597

Spring 2026 All Classes

All Classes

Advanced Topics in Data Analytics & Data Science

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Credit: 2 OR 4 hours.

Variety of newly developed and advanced topics courses within the fields of Data Analytics & Data Science, intended to augment the existing Information Sciences curricula.

Approved for Letter and S/U grading. May be repeated in the same or separate semesters to a maximum of 16 hours, if topics vary.

This course satisfies the General Education Criteria in Fall 2022 for:

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IS 597 class schedule data for spring 2026
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
73038	Lecture-Discussion	CS	1:00PM -3:50PM	M	46 Sch of Info Sciences Bldg	LeBlanc, Z	Part of Term: 1 Date Range: 01/20/26-05/06/26 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Culture At Scale: A Seminar Section Info: Other interested students should email the instructor for approval: zleblanc@illinois.edu. How does reading a novel or a dozen, compare to studying hundreds if not thousands? What about paintings or songs? This seminar is devoted to understanding how we can study and produce culture at scale. Bridging theoretical and technical, we will uncover how computation can influence our understandings of culture and how in turn focusing on culture can impact how and when we use computation. This is a HYBRID course that meets with IS 597 CSO. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to students in the Illinois Informatics Institute or Information Sciences department.
72768	Online	CSO	1:00PM -3:50PM	M	n.a.	LeBlanc, Z	Part of Term: 1 Date Range: 01/20/26-05/06/26 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Culture At Scale: A Seminar Section Info: Other interested students should email the instructor for approval: zleblanc@illinois.edu. How does reading a novel or a dozen, compare to studying hundreds if not thousands? What about paintings or songs? This seminar is devoted to understanding how we can study and produce culture at scale. Bridging theoretical and technical, we will uncover how computation can influence our understandings of culture and how in turn focusing on culture can impact how and when we use computation. This is a HYBRID course that meets with IS 597 CS. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to students in the Illinois Informatics Institute or Information Sciences department.
72428	Lecture-Discussion	DS	10:00AM -12:50PM	F	53 Sch of Info Sciences Bldg	Cohen, B Weible, J	Part of Term: 1 Date Range: 01/20/26-05/06/26 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Data Structures & Algorithms Section Info: Data Structures & Algorithms via Puzzles & Games. This is an advanced programming and analysis course, requiring effective team coding skills from the start. PREREQUISITES: At least three intermediate-level programming courses including IS597PR, OR contact instructor at jweible@illinois.edu for approval of alternatives. Learn, experiment, code with, and compare performance of common data structures and algorithms in a fun, collaborative, and challenging context. In class, students will solve or play and discuss several types of logic puzzles and strategy games. In small teams they will explore the deductive, strategic, and tactical decisions involved, select appropriate data structures & algorithms to develop efficient program solutions to automate playing, solving, generating, or analyzing puzzles & games. Techniques used include analysis of efficiency (Big-O, Big-theta), recursion, minimax, Monte Carlo Tree Search, client/server network communications, deterministic vs non-deterministic algorithms. Structures used include arrays, hash tables, stacks, various trees, network graphs, and custom structures. For some projects, students will have competitions pitting their solutions against other teams’. Primarily based in the Python language. Intended for IS students who have not formally studied algorithm efficiency & data structures or as a reinforcement of those concepts through applied practice. Restriction(s): Restricted to Graduate - Urbana-Champaign.
72773	Lecture-Discussion	HCD	1:00PM -3:50PM	M	303 English Building	Wang, D Yao, R	Part of Term: 1 Date Range: 01/20/26-05/06/26 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Human Centered Data Science Section Info: Advances in data science have transformed decision making throughout our professional and personal lives. This course combines hands-on experiences using state-of-the-art technologies with theoretical foundations and historical perspectives that will enable students to anticipate the broader social impact of these technologies. Moreover, by centering people throughout the design, development, testing, and deployment of these tools, students will be able to establish when data-driven approaches should be used in a professional setting. Specific topics vary from semester-to-semester so the course may be repeated in the same or separate terms, to a maximum of 16 hours, if topics vary. 4 graduate hours. No professional credit. NOTE: This course assumes competency in data processing and analytics. Students are advised not to enroll in this class if they do not feel capable of processing data and learning/mastering new data analytics and machine learning tools on their own. Pre- and Co-requisites This course does not teach basic data science/analytic and machine learning topics. Students must have demonstrated ability in data science methods and should have taken one of the following courses or a close equivalent: IS 305 Programming for Information Problems, IS 327 Concepts of Machine Learning; CS 412-Introduction to Data Science, CS 446-Machine Learning. Restriction(s): Restricted to Graduate - Urbana-Champaign.
75681	Lecture-Discussion	LLM	9:30AM -12:20PM	R	243 Armory	Hong, G Kilicoglu, H	Part of Term: 1 Date Range: 01/20/26-05/06/26 Degree Notes: ONL Info Science rate course. Special Approval: Instructor Approval Required Credit: 4 hours Section Title: Large Language Models Section Info: Doctoral students and advanced master's students can enroll with the permission of the instructor, email halil@illinois.edu for approval. This seminar-style course is designed to provide a comprehensive exploration of large language models (LLMs) from an information science perspective. It covers the fundamental concepts and algorithms that power LLMs, including deep learning architectures, training strategies, and emerging capabilities, emphasizing core principles. The course also examines the societal impacts of LLMs, considering challenges and risks related to ethics, bias, misinformation, and the potential for transformation in fields such as on healthcare, education, and creative industries. Background in NLP and machine learning basics is helpful (IS 567, IS 577, or similar). Restriction(s): Restricted to Graduate - Urbana-Champaign.
72425	Lecture-Discussion	PR	9:00AM -11:50AM	W	12A Sch of Info Sciences Bldg	Cheng, H Weible, J	Part of Term: 1 Date Range: 01/20/26-05/06/26 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Progr. & Quality in Analytics Section Info: Programming & Quality in Data Analytics - Prerequisites: Any two previous programming courses or 1 year of experience with any general-purpose language(s). Prior familiarity with Python is very helpful but not required. This is an intermediate-level Python programming course using a broad range of data structures, packages, concepts, best practices, and tools needed for developing, debugging, and modifying software to solve moderately complex problems and to evaluate and improve code maintainability and reliability. These skills are relevant to all contexts of programming, but most scenarios and assignments will include numerical data analysis, Monte Carlo simulation & experimental design, or processing pipelines while using data sets from sciences, finance, business, or government. Introduces test-driven design, OOP features, performance analysis, and concurrent processing. The primary learning objectives are to improve general programming abilities and to develop deeper critical understanding of work in data analytics and common flaws therein. Restriction(s): Restricted to Graduate - Urbana-Champaign.
75763	Lecture	TML	3:30PM -6:20PM	T	214 Davenport Hall	Wang, H	Part of Term: 1 Date Range: 01/20/26-05/06/26 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Trustworthy Machine Learning Section Info: The course will cover several technical aspects as well as the intuitive understanding of topics under the umbrella of trustworthy machine learning, such as robustness, adversarial robustness, fairness, privacy, and interpretability. Restriction(s): Restricted to Graduate - Urbana-Champaign.