IS 597

Fall 2025 Part of Term 1

Part of Term 1
Aug 25-Dec 10

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 fall 2025
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
73578	Lecture-Discussion	HCD	1:00PM -3:50PM	M	1047 Sidney Lu Mech Engr Bldg	Gong, Y Wang, D	Part of Term: 1 Date Range: 08/25/25-12/10/25 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.
76353	Lecture-Discussion	MLC	1:00PM -3:20PM	F	108 Bevier Hall	Kattamuri, S Tibebu, H	Part of Term: 1 Date Range: 08/25/25-12/10/25 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Machine Learning Cloud Section Info: Organizations are increasingly moving to cloud-based infrastructures for scalability, efficiency, and collaboration. This advanced course offers a technical and practical hands-on exploration of machine learning techniques using the AWS Academy platform. Students will work with tools including Amazon SageMaker, Jupyter Notebooks, IAM security systems, S3 buckets, and other key AWS services to build end-to-end machine learning pipelines, train and test models, and prepare them for deployment. The course emphasizes weekly assignments and real-world applications, preparing students to master cloud-based ML workflows. This course's prerequisites include: IS 305: Programming for Information Problems, IS 327: Concepts of Machine Learning, CS 412: Introduction to Data Science, or CS 446: Machine Learning. Restriction(s): Restricted to Graduate - Urbana-Champaign.
73264	Lecture-Discussion	PR	9:00AM -11:50AM	W	156 Henry Administration Bldg	Cheng, H Weible, J	Part of Term: 1 Date Range: 08/25/25-12/10/25 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 student questions may be sent to msim-advising@illinois.edu. Restriction(s): Restricted to Graduate - Urbana-Champaign.