Syllabus

Click here to download the syllabus.

Time and location

	Day	Time	Location
Lectures	Tu & Th	3:30 - 4:45 PM	Cuday Hall 143
Lab	None	None	None

My in-person office hours are TuTh 4:50 - 5:50 PM, and Wed 12 - 1 PM in Cudahy Hall room 353.
You are welcome to schedule an online meeting via Microsoft Teams if you need/prefer.

MATH 4720 (Intro to Statistics), MATH 3100 (Linear Algebra) and MATH 4780 (Regression Analysis)
Having taken MATH 4700 (Probability) and MATH 4710 (Statistical Inference) or more advanced ones is strongly recommended.
This course is supposed to be taken in the last semester for the applied statistics (APST) master students. Talk to me if you are not sure whether or not this is the right course for you.

I will attempt to reply your email quickly, at least within 24 hours.
Expect a reply on Monday if you send a question during weekends. If you do not receive a response from me within two days, re-send your question/comment in case there was a “mix-up” with email communication (Hope this won’t happen!).
Please start your subject line with [mssc6250] followed by a clear description of your question. See an example below.

Email etiquette is important. Please read this article to learn more about email etiquette.
I am more than happy to answer your questions about this course or data science/statistics in general. However, with tons of email messgaes everyday, I may choose NOT to respond to students’ e-mail if
1. The student could answer his/her own inquiry by reading the syllabus or information on the course website or D2L.
2. The student is asking for an extra credit opportunity. The answer is “no”.
3. The student is requesting an extension on homework. The answer is “no”.
4. The student is asking for a grade to be raised for no legitimate reason. The answer is “no”.
5. The student is sending an email with no etiquette.

(ISL) An Introduction to Statistical Learning, by James et al. Publisher: Springer. (Undergraduate to master level, R and Python code)

(PML) Probabilistic Machine Learning: An Introduction, by Kevin Murphy. Publisher: MIT Press. (Master to PhD level, lots of mathematics foundations, Python code)
(PMLA) Probabilistic Machine Learning: Advanced Topics, by Kevin Murphy. Publisher: MIT Press. (PhD level, more probabilistic-based or Bayesian)
(ESL) The Elements of Statistical Learning, 2nd edition, by Hastie et. al. Publisher: Springer. (PhD level, more frequentist-based)

Your final grade is earned out of 1000 total points distributed as follows:
- Homework: 500 pts
- In-class Activity: 200 pts
- Final project presentation and/or written report: 300 pts
You will NOT be allowed any extra credit projects/homework/exam to compensate for a poor average. Everyone must be given the same opportunity to do well in this class. Individual exam will NOT be curved.
The final grade is based on your percentage of points earned out of 1000 points and the grade-percentage conversion Table. \([x, y)\) means greater than or equal to \(x\) and less than \(y\). For example, 94.1 is in \([93, 100]\) and the grade is A and 92.8 is in \([90, 94)\) and the grade is A-.

You may use any programming language to complete your homework and/or your project.

Homework will be assigned through the course website in weekly modules.
To submit your homework, please go to D2L > Assessments > Dropbox and upload your homework in PDF format.
No late or make-up homework for any reason.

There will be 3 to 4 in-class activities.
Students will learn from each other by presenting and discussing the assigned topics.
More details about the in-class activities will be released later.

The final project includes two parts: written report and oral presentation.
You need to participate (in-person) in the final presentation in order to pass the course.
The final project presentation is on Thursday, 5/9, 8 - 10 AM.
More details about the written report and oral presentation will be released later.

Unless explicitly stated otherwise, you may make use of any online resources, but you must explicitly cite where you obtained any code you directly use or use as inspiration in your solutions.
Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism, regardless of source.

As a student in this course, you have agreed to comply with Marquette undergraduate policies and regulations.

If you need to request accommodations, or modify existing accommodations that address disability-related needs, please contact Disability Service.

Click here for the full Marquette academic calendar.