Schedule

Teachers

Name	Office Hours	Email (please ask most questions in person)
Gillett, John (Lecturer)	Tu 11:55-12:45, Th 9:30-10:20 in 1221 Medical Sciences Center	jgillett@wisc.edu
Pei, Ming (TA)	Mo 1:10-2:00, We 11:40-12:30, Th 5:10-6:00 in 1217C Medical Sciences Center	mpei3@wisc.edu

Class Times

TuTh 8:00-9:15 in Microbial Sciences 1420

Course Description
The development of tools necessary for collecting, managing, and analyzing large data sets. Examples of techniques and programs utilized include Linux, R, distributed computing, text editor(s), git/github, and other related tools. Work in the class will be done in teams to research, develop, write, and make presentations related to a variety of data analysis projects.

Learning Outcomes
Use Linux, R, and distributed computing to analyze data sets too large for a laptop:

1. Collect and manage data and write programs and documentation via tools suited to large computations:
• Use the Linux operating system and write shell scripts.
• Use the emacs editor to write and manage local and remote files.
• Use the git/github version control system to track changes and manage collaboration.
2. Run analyses too large for a laptop:
   • Use Linux, R, and the Slurm job scheduler to run tens of parallel jobs on the Statistics High Performance Computing (HPC) Cluster.
   • Use Linux, R, and distributed high-throughput computing via HTCondor to run thousands of parallel jobs at UW's Center for High-Throughput Computing (CHTC).
3. Work in teams to research, develop, write, and make two presentations:
   • Discuss a data analysis proposal consisting of data, a question, and a suggested analysis.
   • Discuss your completed data analysis.

Requisites
One of STAT 303 or STAT 240; and one of CS 200, CS 220, or CS 300

Credit Information
This course is 3-credits. The class meets for two 75-minute in-person lectures each week and carries the expectation that students will work on course learning activities (readings, homeworks, projects, studying, etc.) for about 3 hours out of the classroom for every lecture period.

Instructional Mode

Classroom instruction

Regular and Substantive Student-Instructor Interaction
The regular and substantive student-instructor interaction requirement is met through in-person lectures and regular weekly office hours.

Online materials
Course materials are posted in the schedule (linked above). Canvas is used to collect homework and as a gradebook.

Textbook

There is no required textbook. See the schedule for course materials.

Optional Reference Books

The Linux Command Line, 2nd Edition, by William E Shotts Jr. free online or for sale

Computing
A laptop from which you can use ssh (see the "login" link in the schedule) to remote login to our parallel computing environments is required in class.

In case of computer trouble, UW's InfoLabs offer loaner laptops and desktop computer labs.

Grades

130 points are available:

• 12 points for three group exercises on statistical software that prepare for homework:
  • Group1 (4 points) git/GitHub version control system
  • Group2 (4 points) shell scripting
  • Group3 (4 points) CHTC
• 48 points for four individual statistical software exercises graded for code correctness and quality:
  • HW1 (8 points) on the emacs editor requires editing an 80-line text file
  • HW2 (10 points), a small search for galaxies among 100 spectra on a single linux machine, requires 80 lines of R code
  • HW3 (10 points) on answering questions about flights from Madison requires 80 lines of shell, Slurm, and R scripts to run a parallel computation on the Statistics HPC
  • HW4 (20 points), a large search for galaxies among 2.4 million spectra with distributed computing at the CHTC, requires 130 lines of shell, HTCondor, and R scripts
  Homework solutions are submitted via file upload to Canvas. I set Canvas to allow late homework submission with a 1% per hour penalty. For example:
  • 1 second to 1 hour late gets a 1% penalty.
  • 12 hours late gets a 12% penalty.
  • 50 hours late gets a 50% penalty.
  (Note that this 1% per hour penalty will not excuse late project components, as late project work would delay presentations and peer feedback.)
• 20 points for five quizzes in class:
  • Q01 (4 points): Linux
  • Q02 (4 points): emacs
  • Q03 (4 points): git
  • Q04 (4 points): shell scripting
  • Q05 (4 points): distributed computing
• 50 points for a group data science project including presentations:
  • Form a group (0 points) of 4-5 students (or be randomly assigned to a group)
  • Proposal (4 points) including code to read data descriptions of the variables, statistical methods, and computational steps you will use
  • Meet with teacher and TA to discuss proposal (3 points)
  • Presentation (25 points) to class, evaluated via peer feedback
  • Watch presentations (8 points over two or three days) and give peer feedback
  • Report (10 points) of no more than 600 words with supporting graphics

We'll assign grades according to the percentage scale, A = [92,100], AB = [88,92), B = [82,88), BC = [78,82), C = [70,78), D = [60,70), F = [0,60) (92% of points => A); and according to the percentile scale, A = 60, AB = 40, B = 20, BC = 8, C = 4, D = 2, F = 0 (which yields 40% A grades, 20% AB, 20% B, 12% BC, 4% C, 2% D, 2% F); your grade will be the higher of these two grades.

If you anticipate religious or other conflicts with course requirements, or if you require accommodation due to disability, you must notify me during the first three weeks of class. You may not make up missed quizzes, homework, or exams, except in the rare case of a documented, serious problem beyond your control.

How to Succeed in This Course
The successful student will attend lectures, submit homeworks and groupworks, develop and present a good project, and use lecture and office hours to ask questions when things are unclear.

Academic Integrity
By virtue of enrollment, each student agrees to uphold the high academic standards of the University of WisconsinMadison; academic misconduct is behavior that negatively impacts the integrity of the institution. Cheating, fabrication, plagiarism, unauthorized collaboration, and helping others commit these previously listed acts are examples of misconduct which may result in disciplinary action. Examples of disciplinary action include, but is not limited to, failure on the assignment/course, written reprimand, disciplinary probation, suspension, or expulsion.

Accommodations for Students with Disabilities
The University of Wisconsin-Madison supports the right of all enrolled students to a full and equal educational opportunity. The Americans with Disabilities Act (ADA), Wisconsin State Statute (36.12), and UW-Madison policy (UW-855) require the university to provide reasonable accommodations to students with disabilities to access and participate in its academic programs and educational services. Faculty and students share responsibility in the accommodation process. Students are expected to inform me of their need for instructional accommodations during the beginning of the semester, or as soon as possible after being approved for accommodations. I will work either directly with you or in coordination with the McBurney Center to provide reasonable instructional and course-related accommodations. Disability information, including instructional accommodations as part of a student’s educational record, is confidential and protected under FERPA. (See: https://mcburney.wisc.edu/).

Course Evaluations
UW-Madison students have the opportunity to evaluate the courses they are enrolled in and their learning experiences through course evaluations. Most instructors use a digital course evaluation survey tool such as HelioCampus AC (formerly AEFIS) https://kb.wisc.edu/luwmad/81069. In most instances, students receive an official email two weeks prior to the end of the semester, notifying them that anonymous course evaluations are available. Student participation is an integral component of course development, and confidental feedback is important UWMadison strongly encourages student participation in course evaluations.

Diversity & Inclusion
Diversity is a source of strength, creativity, and innovation for UW-Madison. We value the contributions of each person and respect the profound ways their identity, culture, background, experience, status, abilities, and opinion enrich the university community. We commit ourselves to the pursuit of excellence in teaching, research, outreach, and diversity as inextricably linked goals. The University of Wisconsin-Madison fulfills its public mission by creating a welcoming and inclusive community for people from every background – people who as students, faculty, and staff serve Wisconsin and the world.

Mental Health & Well-Being
Students often experience stressors that can impact both their academic experience and personal well-being. These may include mental health concerns, substance misuse, sexual or relationship violence, family circumstances, campus climate, financial matters, among others. Students are encouraged to learn about and utilize UW-Madison’s mental health services and/or other resources as needed. Visit https://www.uhs.wisc.edu/ or call University Health Services at (608) 265-5600 to learn more.

Privacy of Student Records & the Use of Audio Recorded Lectures Statement
Lecture materials and recordings for this course are protected intellectual property at UW-Madison. Students in this course may use the materials and recordings for their personal use related to participation in this class. Students may also take notes solely for their personal use. If a lecture is not already recorded, you are not authorized to record my lectures without my permission unless you are considered by the university to be a qualified student with a disability requiring accommodation. [Regent Policy Document 4-1] Students may not copy or have lecture materials and recordings outside of class, including posting on internet sites or selling to commercial entities. Students are also prohibited from providing or selling their personal notes to anyone else or being paid for taking notes by any person or commercial firm without the instructor’s express written permission. Unauthorized use of these copyrighted lecture materials and recordings constitutes copyright infringement and may be addressed under the university’s policies, UWS Chapters 14 and 17, governing student academic and non-academic misconduct.

Students' Rules, Rights, & Responsibilities
See: https://guide.wisc.edu/undergraduate/#rulesrightsandresponsibilitiestext

Teaching & Learning Data Transparency Statement
The privacy and security of faculty, staff and students’ personal information is a top priority for UW-Madison. The university carefully evaluates and vets all campus-supported digital tools used to support teaching and learning, to help support success through learning analytics (https://it.wisc.edu/services/lace/), and to enable proctoring capabilities. View the university’s full teaching and learning data transparency statement here: https://teachlearn.wisc.edu/teaching-and-learning-data-transparency-statement/.

Standards of Ethical Conduct in Data Analysis and Data Privacy
The members of the faculty of the Department of Statistics at UW-Madison uphold the highest ethical standards of teaching, data, and research. They expect their students to uphold the same standards of ethical conduct. Standards of ethical conduct in data analysis and data privacy are detailed on the ASA website (https://www. amstat.org/your-career/ethical-guidelines-for-statistical-practice/), and include:

• Use methodology and data that are relevant and appropriate; without favoritism or prejudice; and in a manner intended to produce valid, interpretable, and reproducible results.
• Be candid about any known or suspected limitations, defects, or biases in the data that may affect the integrity or reliability of the analysis. Obviously, never modify or falsify data.
• Protect the privacy and confidentiality of research subjects and data concerning them, whether obtained from the subjects directly, other persons, or existing records.

Statement on the Use of ChatGPT and other AI Language Models
While the Statistics Department recognizes the potential benefits of AI language models, their use in academic work can be problematic. In this course, two rules regarding the use of ChatGPT and other AI language models will be enforced: (1) Passing off AI-generated responses as original student work constitutes plagiarism and is strictly prohibited. Any students found to be engaging in this practice will be cited for academic misconduct. (2) Unless explicitly authorized by the instructor to do so, any form of attribution or citation to AI-generated responses as sources is prohibited.

Overlapping Course Time Statement
The Department of Statistics strongly discourages students from enrolling in any courses whose regular class meeting dates and times overlap with each other. This policy is in alignment with the College of Letters and Sciences Course Attendance Policy. It is also consistent with the Class Attendance Policy for Students at UW-Madison (https://kb.wisc.edu/ls/24628), whose first sentence reads, "It is expected that every student will be present at all classes." Statistics instructors may opt not to make any alternative arrangements in the event any conflict arises due to a student taking a course with class meetings that overlap with a Statistics course, including a conflict between two Statistics courses. Note that final exams occasionally are scheduled simultaneously for courses which meet at different times; in this situation, please contact your instructor well before the exam date about potential accommodations.

Netiquette on Piazza and Online Communication
See https://kb.wisc.edu/50548 for a general netiquette. Specifically:

• Any comment or answers must be on topic, concise, polite, and respectful of others.
• Assume the best intentions of others in the class and be forgiving when you think that the tone of someone’s post is offensive. It is easy to misread the tone of someone’s written communication. If in doubt, ask an open, honest question about what the person meant so that you can clarify before making assumptions that damage your perception of your colleague.
• Students must not post answers to homework problems. Questions should be sent via Piazza or Canvas discussions. Students should not send questions to the TA or instructor via email, unless for personal or sensitive issues. Students should not expect an immediate answer to a question posted late at night before an assignment due date.

Complaints
If you have a complaint about a TA or course instructor, you should feel free to discuss the matter directly with the TA or instructor. If the complaint is about the TA and you do not feel comfortable discussing it with him or her, you should discuss it with the course instructor. Complaints about mistakes in grading should be resolved with the instructor in the great majority of cases. If the complaint is about the instructor (other than ordinary grading questions) and you do not feel comfortable discussing it with him or her, contact the Director of Undergraduate Studies, Professor Jessi Cisewski-Kehe (jjkehe@wisc.edu). If your complaint concerns sexual harassment, please see campus resources listed at: https://compliance. wisc.edu/titleix/. In particular, there are a number of options to speak to someone confidentially. If you have concerns about climate or bias in this class, or if you wish to report an incident of bias or hate that has occurred in class, you may contact the Chair of the Statistics Department Climate and Diversity Committee, Professor Karl Rohe (karl.rohe@wisc.edu). You may also use the University’s bias incident reporting system, which you can reach at: https://osas.wisc.edu/report-an-issue/bias-or-hate-reporting/.