IS 597

Spring 2021 Part of Term 1

Part of Term 1
Jan 25-May 5

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 spring 2021
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
71754	Online	DSO	9:00AM -11:50AM	F	n.a.	Weible, J	Part of Term: 1 Date Range: 01/25/21-05/05/21 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Data Structures & Algorithms Section Info: Prerequisite: “At least three previous programming courses including 590PR/597PR, or instructor approval, email your request to jweible@illinois.edu. ” 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, matrices, 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, though students may also use others. 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 department.
71815	Online	PDO	11:00AM -12:20PM	TR	n.a.	Brooks, I	Part of Term: 1 Date Range: 01/25/21-05/05/21 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Practical Health DataAnalytics Section Info: This course provides hands-on experience with practical data analysis. Datasets will be drawn from the health sector and will include structured, unstructured, social media, and geospatial data. Students will work in teams to refine the project question, identify the appropriate analytical methods, obtain any necessary supplemental data from online sources, perform the analysis, visualize the results, and present the project to stakeholders. Teams will be assigned based on analytical skill-level from basic statistics to advanced machine learning. Students should have taken an introduction to statistics class, but no prior experience in the health domain is required. 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 department.
71816	Online	PRO	9:00AM -11:50AM	M	n.a.	Weible, J	Part of Term: 1 Date Range: 01/25/21-05/05/21 Degree Notes: ONL Info Science rate course. Credit: 4 hours Section Title: Progr Analytics & Data Process Section Info: Prerequisite: IS 452 (Foundations Info Processing); or equivalent programming knowledge, w/consent of instructor. Building on the fundamentals introduced in IS 452, 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 students in other departments may register with approval; please email your request to jweible@illinois.edu Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to students in the Information Sciences department.