IS 597

Fall 2022 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.

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:

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IS 597 class schedule data for fall 2022
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
73265	Lecture-Discussion	DS	9:00AM -11:50AM	M	131 Grad Sch of Lib & Info Science	Weible, J	Part of Term: 1 Date Range: 08/22/22-12/07/22 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.
73264	Lecture-Discussion	PR	9:00AM -11:50AM	W	1022 Lincoln Hall	Weible, J	Part of Term: 1 Date Range: 08/22/22-12/07/22 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Progr. & Quality in Analytics Section Info: Programming & Quality in Data Analytics - Prerequisites: At least two prior semester-length programming courses or 1 year of experience with any general-purpose language(s) AND including some prior familiarity with Python fundamentals. This is an intermediate-level Python programming course involving a broad range of data structures, skills, 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 design, or processing pipelines while using data sets drawn from scientific, historical, business, and other contexts. Introduces test-driven design, OOP design, performance analysis, and concurrent processing. The primary learning objectives are to increase general programming capabilities and to develop deeper critical understanding of work in data analytics and typical flaws." 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.
76353	Online	RDA	9:00AM -10:55AM	F	n.a.	Diesner, J	Part of Term: 1 Date Range: 08/22/22-12/07/22 Degree Notes: ONL Info Science rate course. Section Title: Responsible Data Science & AI 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.