IS 597

Fall 2024 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.

2 or 4 graduate hours. No professional credit. 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 fall 2024
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
73265	Lecture-Discussion	DS	9:00AM -11:50AM	F	Sidney Lu Mech Engr Bldg	Jiang, X Weible, J	Part of Term: 1 Date Range: 08/26/24-12/11/24 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. Graduate student questions may be sent to ischool-advising@illinois.edu Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to students in the Illinois Informatics Institute or Information Sciences department.
73578	Lecture-Discussion	HCD	1:00PM -3:50PM	M	Sidney Lu Mech Engr Bldg	Liu, Y Wang, D	Part of Term: 1 Date Range: 08/26/24-12/11/24 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. Restricted to students in the Illinois Informatics Institute or Information Sciences department.
76353	Lecture-Discussion	MLC	1:15PM -3:15PM	T	Grad Sch of Lib & Info Science	Shah, V Trainor, K	Part of Term: 1 Date Range: 08/26/24-12/11/24 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Machine Learning Cloud Section Info: MUST CHOOSE: 2 or 4 credits. 4 credits requires a project, 2 credits no project. This course provides deeper student engagement with the talks and topics presented at the “Responsible Data Science and AI” speaker series (https://jdiesnerlab.ischool.illinois.edu/responsible_ds_ai.html). We focus on explainability, reproducibility, biases, data curation and governance, and privacy. Students discuss recent research on these topics in depth, analyze papers in the wider context of theories, methods, and findings from their fields, guide or lead discussions, and reflect on the discussed papers in the context of their own research. Everybody is expected to read the assigned paper(s) for each week before class, come to class with at least 3 questions, and be able to discuss the paper(s), presentation, and their questions. This class is open to PhD students from across campus. Exceptions can be made for advanced MS students who have a strong focus on research per their advisor's or the instructor's approval. This course meets with CMN 529-1. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to Information Management major(s).
73264	Lecture-Discussion	PR	9:00AM -11:50AM	W	English Building	Cheng, H Weible, J	Part of Term: 1 Date Range: 08/26/24-12/11/24 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. Restricted to students in the Illinois Informatics Institute or Information Sciences department.