IS 597

Spring 2023 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 spring 2023
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
73038	Lecture-Discussion	CS	1:00PM -3:50PM	M	Grad Sch of Lib & Info Science	LeBlanc, Z	Part of Term: 1 Date Range: 01/17/23-05/03/23 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Culture At Scale: A Seminar Section Info: Other interested students should email the instructor for approval: zleblanc@illinois.edu. How does reading a novel or a dozen, compare to studying hundreds if not thousands? What about paintings or songs? This seminar is devoted to understanding how we can study and produce culture at scale. Bridging theoretical and technical, we will uncover how computation can influence our understandings of culture and how in turn focusing on culture can impact how and when we use computation. This is a HYBRID course that meets with IS 597 CSO. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to PHD: Informatics - UIUC or PHD:Information Sciences -UIUC.
72768	Online	CSO	1:00PM -3:50PM	M	n.a.	LeBlanc, Z	Part of Term: 1 Date Range: 01/17/23-05/03/23 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Culture At Scale: A Seminar Section Info: Other interested students should email the instructor for approval: zleblanc@illinois.edu. How does reading a novel or a dozen, compare to studying hundreds if not thousands? What about paintings or songs? This seminar is devoted to understanding how we can study and produce culture at scale. Bridging theoretical and technical, we will uncover how computation can influence our understandings of culture and how in turn focusing on culture can impact how and when we use computation. This is a HYBRID course that meets with IS 597 CS. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to PHD:Information Sciences -UIUC or PHD: Informatics - UIUC.
72428	Lecture-Discussion	DS	9:00AM -11:50AM	F	Grad Sch of Lib & Info Science	Weible, J	Part of Term: 1 Date Range: 01/17/23-05/03/23 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. Restricted to students in the Illinois Informatics Institute or Information Sciences department.
72425	Lecture-Discussion	PR	9:00AM -11:50AM	W	Grad Sch of Lib & Info Science	Weible, J	Part of Term: 1 Date Range: 01/17/23-05/03/23 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.
72551	Online	RIO	2:00PM -3:55PM	T	n.a.	Hsiao, T Schneider, J	Part of Term: 1 Date Range: 01/17/23-05/03/23 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Research Impact Section Info: A hands-on introduction to the theories, concepts, methods, and applications of bibliometrics in research impact evaluation. Students propose and execute a bibliometric or research impact assessment of their choice, demonstrating expertise in data collection, wrangling, visualization, and interpretation, and understanding of ethics and limitations. Outcomes of bibliometric analysis influence decision- and policy-making at various levels. Course topics include research impact, research profiles, scientific collaboration, research opportunity identification and portfolio analysis, university ranking systems, and altmetrics. Recommended experience: Data analysis, data cleaning, or programming experience. This could be from hands-on experience or learned in a course. Example courses with relevant material include: IS 407 Introduction to Data Science, IS 417 Data Science in the Humanities, IS 430 Foundations of Information Processing, IS 457 Data Storytelling, IS 527 Network Analysis, IS 537 Theory and Practice of Data Cleaning, IS 575 Metadata in Theory and Practice, IS 577 Data Mining, IS 595 LD Linked Data Processing, IS 597 PY Python Standard Library, IS 597 DM Open Data Mashups, IS 597 PD - Practical Health Data Analytics Restriction(s): Restricted to Graduate - Urbana-Champaign.