Course Syllabus

Team

Instructor: Yaoyao Liu
Teaching Assistant: Wei Cao
Email: IS517@ischool.illinois.edu
Office Hours: By appointment

Acknowledgements

The instructor thanks Professors Yang Wang, Victoria Stodden, Vetle Torvik, Abbass Al Sharif, Nigel Bosch, Jiaqi Ma, and Ismini Lourentzou for generously sharing their materials. The materials for this course, including the syllabus, were adapted from these shared materials with permission.

Course Description

A dramatic increase in computing power has enabled new areas of data science to develop in statistical modeling and analysis. These areas cover predictive and descriptive learning and bridge between ideas and theory in statistics, computer science, and artificial intelligence. We will cover methods, including predictive learning: estimating models from data to predict future outcomes. Regression topics include linear regression with recent advances using large numbers of variables, smoothing techniques, additive models, and local regression. Classification topics include linear regression, regularization, logistic regression, discriminant analysis, splines, support vector machines, generalized additive models, naive Bayes, mixture models, and nearest neighbor methods, as time permits. We situate the course components in the “data science lifecycle” as part of the larger set of practices in the discovery and communication of scientific findings.

This course will move rapidly. The course will include computer exercises using Python and other relevant computing languages.

Prerequisites

• LIS542 Data, Stat, Info, or equivalent, such as STAT100, CS361, or ECON202
• LIS490IDS/CS398ID/STAT490 or CS101 or equivalent; or consent of the instructor
• Linear Algebra recommended at the level of MATH125
• Calculus is recommended at the level of MATH220

Course Objectives

The overall goal of this course is to develop a functional data science perspective of the world. More specifically, the objectives are:

• To gain a broad exposure to data science methods through lectures and discussions.
• To develop a working proficiency in data science techniques through hands-on exercises.
• To nurture the ability to detect opportunities to apply these concepts, principles, and techniques in new scenarios by independent exploration of resources beyond the course materials and through a course project.

Course Materials

We use the following textbooks. They are available on the University of Illinois Library website.

• An Introduction to Statistical Learning, by James, Witten, Hastie, and Tibshirani.
• Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, by Sebastian Raschka and Vahid Mirjalili.

Assignments and Methods of Assessment

Methods of Assessment

Component	Weight
Home assignments	40%
Class participation	10%
Project proposal	5%
Project proposal presentation	5%
Final project report	30%
Final project presentation	10%

Assignment Rules

Criteria for grading homework assignments include, but are not limited to, creativity and the amount of original work demonstrated in the assignment. Students are permitted to use and adapt the work of others, provided that the following guidelines are followed:

• Use of other people’s material must not infringe the copyright of the original author, nor violate the terms of any licensing agreement. Know and respect the principles of fair use with respect to copyrighted material.
• Students must scrupulously attribute the original source and author of whatever material has been adapted for the assignment. Summarize the changes or adaptations that have been made. Make plain how much of the assignment represents original work.

Homework assignments will be mainly based on PrairieLearn (https://prairielearn.org). The exact submission deadlines for the homework assignments are shown on the PrairieLearn site. A rough reference of the due dates can be found in the Week-by-Week Topic and Assignment Schedule section. Please see the Late Policy section if you cannot meet the assignment submission deadlines.

The in-class quizzes and practices will also be taken on PrairieLearn. There will be nine in-class quizzes randomly scheduled throughout the semester. Your overall quiz grade will be calculated based on your best 5 out of the 9. The content of the quizzes will closely resemble the homework assignments, despite being much shorter.

The class project will start with a proposal document submission describing the dataset(s), the research question, and the proposed method of analysis. The proposal will likely describe the application of a regression or classification technique from class, including expected outcomes. The class project will carry out the research in the proposal. Students will give a presentation near the end of the course describing the research question, dataset, and comparing the expected and actual results. There will also be a project report of 500–1,000 words.

Note: Students are permitted to use and adapt the work of others, including AI tools such as ChatGPT, for homework assignments, provided that the same attribution, copyright, and originality guidelines above are followed.

Late Policy

Assignments will be accepted late for 80% of points until the end of Week 15 in this semester, but in-class quizzes, exams, and the project will not be accepted late without prior approval.

Class Project

This is a group project. Ideally, every group should have one or two students. The project proposal will describe the proposed dataset(s), the original research question(s), and the proposed method of solution. This will likely be the novel application of a regression or classification technique from class. It is at most one page in length.

The final project will carry out the research in the proposal. There will be a project proposal presentation describing the research question(s), dataset(s), method, and possibly the expected results. There will be a final project presentation describing the research question(s), dataset(s), method, and comparing the expected and actual results.

Incomplete Grades

An exceptional request for an incomplete grade is most often granted to students encountering a medical emergency or other extraordinary circumstances beyond their control. Students must request an incomplete grade from the instructor. The instructor and student will agree on a due date for completion of coursework. The student must submit an Incomplete Form signed by the student, the instructor, and the student’s academic advisor to the front office: https://uofi.app.box.com/v/ISIncompleteForm.

Please see the Student Code for full details: http://studentcode.illinois.edu/article3/part1/3-104/.

Grading Scale

A+: 97–100
A: 94–96
A−: 90–93

B+: 87–89
B: 83–86
B−: 80–82

C+: 77–79
C: 73–76
C−: 70–72

D+: 67–69
D: 63–66
D−: 60–62

F: Below 60

Attendance / Participation Policy

The iSchool expects students to attend all classes except in cases of emergency. Student Code on Attendance: http://studentcode.illinois.edu/article1/part5/1-501/.

Class discussion/participation grades must be based on the quality of what was said and how it added to the discussion, rather than the quantity of the participation by a student. Class discussion/participation should evaluate actual participation and not mere attendance.

If you have an emergency, communicate with the instructor as early as possible to prevent negatively impacting your grade. 

Enrollment in this course includes an expectation of regular attendance. Students may miss one class session with no penalty; thereafter, each unexcused absence will result in the grade being lowered by one step. Repeated tardiness or leaving sessions early may be considered an unexcused absence unless alternate arrangements have been made with the instructor.

Students are expected to be respectful of others’ perspectives, lived experiences, ideas, and opinions during class discussion. Failure to observe this requirement can result in a failing course participation grade and may result in a failing grade for the course.

Academic Integrity

The iSchool has the responsibility for maintaining academic integrity so as to protect the quality of education and research in our school and to protect those who depend on our integrity. Consequences of academic integrity infractions may be serious, ranging from a written warning to a failing grade for the course or dismissal from the University. See the student code for academic integrity requirements: http://studentcode.illinois.edu/article1/part4/1-401/.

Please review and reflect on the academic integrity policy of the University of Illinois, http://studentcode.illinois.edu/article1_part4_1-401.html, to which we subscribe. By turning in materials for review, you certify that all work presented is your own and has been done by you independently, or as a member of a designated group for group assignments.

If, in the course of your writing, you use the words or ideas of another writer, proper acknowledgment must be given. Not to do so is to commit plagiarism, a form of academic dishonesty. Students who violate university standards of academic integrity are subject to disciplinary action, including a reduced grade, failure in the course, and suspension or dismissal from the University.

Statement of Inclusion

http://www.inclusiveillinois.illinois.edu/mission.html

As the state’s premier public university, the University of Illinois at Urbana-Champaign’s core mission is to serve the interests of the diverse people of the state of Illinois and beyond. The institution thus values inclusion and a pluralistic learning and research environment, one in which we respect the varied perspectives and lived experiences of a diverse community and global workforce. We support diversity of worldviews, histories, and cultural knowledge across a range of social groups, including race, ethnicity, gender identity, sexual orientation, abilities, economic class, religion, and their intersections.

Religious Observances

In keeping with our Statement of Inclusion and Illinois law, the University is required to reasonably accommodate its students’ religious beliefs, observances, and practices in regard to admissions, class attendance, and the scheduling of examinations and work requirements.

If you anticipate the need for an accommodation, please communicate with your instructor in the first two weeks of class. If you are an undergraduate student and your instructor requires an absence letter, you must fill out the Religious Observance Accommodation Request form: Religious Observance Accommodation Request Form. Other accommodations may also be available.

Accessibility Statement

To ensure accessibility-related needs are properly addressed from the beginning of the semester, I request that students with disabilities who require assistance to participate in this class contact me as soon as possible to discuss their needs and any concerns they may have. The University of Illinois may be able to provide additional resources to assist you in your studies through the Office of Disability Resources and Educational Services (DRES). This office can assist you with disability-related academic adjustments and/or auxiliary aids.

Please contact DRES as soon as possible by visiting the office in person at 1207 S. Oak St., Champaign; visiting http://disability.illinois.edu; calling (217) 333-4603 (V/TTY); or emailing disability@illinois.edu. NOTE: I do not require a letter from DRES in order to discuss your requested accommodations.

Land Acknowledgment Statement

Adopted by the University of Illinois in 2018
More information: https://chancellor.illinois.edu/land_acknowledgement.html

As a land-grant institution, the University of Illinois at Urbana-Champaign has a responsibility to acknowledge the historical context in which it exists. We are currently on the lands of the Peoria, Kaskaskia, Peankashaw, Wea, Miami, Mascoutin, Odawa, Sauk, Mesquaki, Kickapoo, Potawatomi, Ojibwe, and Chickasaw Nations. It is necessary for us to acknowledge these Native Nations and to work with them as we move forward as an institution. Over the next 150 years, we will be a vibrant community inclusive of all our differences, with Native peoples at the core of our efforts.

Useful Resources

• Library Resources: https://www.library.illinois.edu/infosci/
• Writing and Bibliographic Style Resources: http://www.cws.illinois.edu/workshop/
• iSchool Writing Resources Moodle site: https://courses.ischool.illinois.edu/course/view.php?id=3389
• Writing coaches: https://publish.illinois.edu/ischoolwritingresources/

Schedule (subject to revision)

Schedule

Week	Topic	Readings
1	Syllabus + Data Science Intro
2	Classification	ISL Ch. 4, PML Ch. 3
3	Linear Regression	ISL Ch. 3, PML Ch. 10
4	Resampling	ISL Ch. 5, PML Chs. 4 and 6
5	Linear Model Selection & Regularization	ISL Ch. 6, PML Chs. 4 and 5
6	Splines / Generalized Additive Models	ISL Ch. 7, PML Ch. 3
7	Tree-Based Methods	ISL Ch. 8, PML Ch. 3
8	Project Proposal + Slides Due / Project Proposal Presentations
10	Support Vector Machines	ISL Ch. 9, PML Ch. 3
11	Unsupervised Learning	ISL Ch. 10, PML Ch. 5
12	Deep Learning	PML Chs. 12 and 13
13	Final Presentation Slides Due / Final Project Presentations
14	Final Project Presentations
15	Final Project Report Due