IS 597

Spring 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 spring 2022
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
73154	Online	CLO	ARRANGED	n.a.	n.a.	Wickes, E	Date Range: 03/21/22-04/15/22 Degree Notes: ONL Info Science rate course. Credit: 2 hours Section Title: Command Line Tools Section Info: This class will provide an overview of the history and commonly offered command line interfaces and essential shell scripting tools. These approaches will be extended to cover common version control tools, including git and GitHub, their value, and how to appropriately organize a project within them. Pre- and Co-requisites: Some programming experience is required, either demonstrated via previous experience or having completed IS 430-Foundations of Information Processing (formerly known as IS 452-Foundations of Information Processing). Concurrent enrollment within IS 430-Foundations of Information Processing (formerly known as IS 452-Foundations of Information Processing) may be acceptable with instructor approval. Graduate student questions may be sent to ischool-advising@illinois.edu Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to students in the Information Sciences or Illinois Informatics Institute department.
72428	Lecture-Discussion	DS	9:00AM -11:50AM	F	131 Grad Sch of Lib & Info Science	Weible, J	Part of Term: 1 Date Range: 01/18/22-05/04/22 Degree Notes: ONL Info Science rate course. Special Approval: Instructor Approval Required 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 Information Sciences or Illinois Informatics Institute department.
72425	Lecture-Discussion	PR	9:00AM -11:50AM	W	126 Grad Sch of Lib & Info Science	Weible, J	Part of Term: 1 Date Range: 01/18/22-05/04/22 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." Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to students in the Illinois Informatics Institute or Information Sciences department.
72426	Online	PRO	9:00AM -11:50AM	W	n.a.	Weible, J	Part of Term: 1 Date Range: 01/18/22-05/04/22 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." Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to students in the Illinois Informatics Institute or Information Sciences department.
73155	Online	PYO	ARRANGED	n.a.	n.a.	Wickes, E	Date Range: 04/18/22-05/14/22 Degree Notes: ONL Info Science rate course. Credit: 2 hours Section Title: Intro to Python Standard Lib Section Info: Pre- and Co-requisites: This class is designed for students who have previous computer science/engineering degrees or related coursework, and have never worked within Python. Students are not allowed to enroll in IS 452 before, during, or after completing this section. Students are not allowed to have taken IS 597 PR - Progr Analytics & Data Process (formerly IS 590 PR) before this class, but may be co-enrolled or take that course later. This is a short course introduction to the Python programming language for students with prior intensive programming experience and/or prior coursework on a programming language. This class will introduce students to the fundamental patterns within data-oriented Python programs, core differences between Python and other general purpose programming languages. This is an intensive course that presumes students are comfortable with loops, logic structures, accumulators, and working within programming environments. This course focuses on the Python standard library, and will not cover external tools like Pandas, numpy, sklearn, matplotlib, etc. Graduate student questions may be sent to ischool-advising@illinois.edu Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to students in the Information Sciences or Illinois Informatics Institute department.
72773	Online	RDA	9:00AM -10:55AM	F	n.a.	Diesner, J	Part of Term: 1 Date Range: 01/18/22-05/04/22 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 equity, fairness, biases, ethics, and privacy. Students taking this class attend both the talks and the class sessions. In class, we cover additional material on the topics of the series. Students will also 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 or interests. We will not discuss the history and foundations of the topics that we cover. Students are expected to have that background or acquire it per topic before each session. This course goes right into current debates and socio-technical details. 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 and obtained approval from the instructor. Restriction(s): Restricted to Graduate - Urbana-Champaign.