IS 597

Fall 2021 All Classes

All Classes

Advanced Topics in Data Analytics & Data Science

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Credit: 2 TO 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 to 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 2021
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
74194	Online	CLO	ARRANGED	n.a.	n.a.	Wickes, E	Date Range: 10/18/21-11/12/21 Degree Notes: ONL Info Science rate course. Credit: 2 hours Section Title: Command Line Tools Section Info: Course is asynchronous, more information will be provided by the instructor, Elizabeth Wickes prior to the start of class. 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.
73264	Lecture-Discussion	PR	9:00AM -11:50AM	F	Grad Sch of Lib & Info Science	Weible, J	Part of Term: 1 Date Range: 08/23/21-12/08/21 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Progr Analytics & Data Process Section Info: Prerequisite: IS 430-Foundations of Information Processing; or equivalent programming knowledge. Building on the fundamentals introduced in IS 430-Foundations of Info Processing, this course adds skills, data structures, tools, & patterns needed for developing & modifying software to solve more complex problems & to improve code maintainability & reliability. These skills are relevant to many types of programming, but many scenarios used will involve data analysis, conversion, validation & processing pipelines. The course helps prepare students for work on larger projects with multiple developers. Includes test-driven design, more OOP design concepts, refactoring, profiling, introductory parallel processing & more. Primarily uses the Python language. 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.
75251	Online	PRO	9:00AM -11:50AM	W	n.a.	Weible, J	Part of Term: 1 Date Range: 08/23/21-12/08/21 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Progr Analytics & Data Process Section Info: Prerequisite: IS 452-Foundations of Information Processing; or equivalent programming knowledge. Building on the fundamentals introduced in IS 430-Foundations of Info Processing, this course adds skills, data structures, tools, & patterns needed for developing & modifying software to solve more complex problems & to improve code maintainability & reliability. These skills are relevant to many types of programming, but many scenarios used will involve data analysis, conversion, validation & processing pipelines. The course helps prepare students for work on larger projects with multiple developers. Includes test-driven design, more OOP design concepts, refactoring, profiling, introductory parallel processing & more. Primarily uses the Python language. 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.
74195	Online	PYO	ARRANGED	n.a.	n.a.	Wickes, E	Date Range: 11/15/21-12/08/21 Degree Notes: ONL Info Science rate course. Credit: 2 hours Section Title: Python Standard Library Section Info: Course is asynchronous, more information will be provided by the instructor, Elizabeth Wickes prior to the start of class. 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 430 - Foundations in Information Processing before, during, or after completing this section. Students are not allowed to have taken IS 597 PR - Progr Analytics & Data Process 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.
76353	Online	RDA	9:00AM -10:55AM	F	n.a.	Diesner, J	Part of Term: 1 Date Range: 08/23/21-12/08/21 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 and as per their advisor’s and instructor approval. Restriction(s): Restricted to Graduate - Urbana-Champaign.