IS 597

Fall 2026 Part of Term 1

Part of Term 1
Aug 24-Dec 9

Advanced Topics in Data Analytics & Data Science

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 fall 2026
	Status		CRN	Type	Section	Time	Day	Location	Instructor	Section Details
			73578	Online	HCD	12:00PM -2:50PM	M	n.a.		Availability: Pending Part of Term: 1 Date Range: 08/24/26-12/09/26 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.
			73264	Lecture-Discussion	PR	8:00AM -10:50AM	W	325 David Kinley Hall	Weible, J	Availability: Open (Restricted) Part of Term: 1 Date Range: 08/24/26-12/09/26 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. Graduate students can send questions to their advisor. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to students in the Illinois Informatics Institute or Information Sciences department.