IS 597

Spring 2025 All Classes

All Classes

Advanced Topics in Data Analytics & Data Science

SHOW FINAL EXAM SCHEDULE

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:

My Favorites

My Schedule

Section Status updates every 10 minutes.

IS 597 class schedule data for spring 2025
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
72428	Laboratory-Discussion	DS	9:00AM -11:50AM	F	131 Grad Sch of Lib & Info Science	Weible, J	Part of Term: 1 Date Range: 01/21/25-05/07/25 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. Not intended for MS:Library & Infor Sci -UIUC.
72773	Lecture-Discussion	HCD	1:00PM -3:50PM	M	106B1 Engineering Hall	Wang, D	Part of Term: 1 Date Range: 01/21/25-05/07/25 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.
75975	Lecture-Discussion	MLC	9:00AM -11:50AM	T	2036 Campus Instructional Facility	Tibebu, H	Part of Term: 1 Date Range: 01/21/25-05/07/25 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Machine Learning Cloud Section Info: Developing Machine Learning Pipelines Using Cloud-Based Platforms: This is a graduate-level data science course for students who need practical experience in using machine learning approaches to address real world problems. This course provides hands-on experience in identifying problems suitable for applying machine learning techniques, designing pipeline-based solutions using Python programming, and implementing these solutions on cloud-based platforms. PREREQUISITES: IS430 or equivalent course in Python programming. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MS:Library & Infor Sci -UIUC.
72425	Lecture-Discussion	PR	9:00AM -11:50AM	W	W203 Turner Hall	Weible, J	Part of Term: 1 Date Range: 01/21/25-05/07/25 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. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MS:Library & Infor Sci -UIUC.
75763	Lecture	TML	3:30PM -6:20PM	T	131 English Building	Wang, H	Part of Term: 1 Date Range: 01/21/25-05/07/25 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. Not intended for MS:Library & Infor Sci -UIUC.