Syllabus

Click here to download the syllabus.

Time and location

	Day	Time	Location
Lectures	Tu & Th	2:00 - 3:15 PM	Olin Engineering 198
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.

By the end of the semester, you will be able to…

Represent and manipulate data in effective ways
Manipulate data using packages/tools and by ad hoc data handling
Use mathematical, computational and statistical tools to detect patterns and model performance
Use computational principles and tools to tackle issues addressable by data science
Use a solid foundation in data science to independently learn new methodologies and technologies in the field of data science

COSC 1010 (Intro to Programming) and MATH 4720 (Intro to Statistics), or MATH 2780 (Intro to Regression and Classification).
Programming experience is helpful because the course involves doing regression analysis using programming language.
The course will also assume facility with using the internet and a personal computer/laptop. The course involves coding in R and Python using Posit Cloud, a cloud integrated development environment (IDE).
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 [math3570] or [cosc3570] 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 messages 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.

No textbook is required for this course. Course materials are mainly Dr. Yu’s slides. Below are some good references.

(r4ds) R for Data Science by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund.
(tmwr) Tidy Modeling with R by Max Kuhn and Julia Silge.
(py4ds) Python for Data Analysis by Wes McKinney.
(IS) Introduction to Statistics by Cheng-Han Yu. (Good resource for brushing up your basic probability, statistics and simple linear regression knowledge.)

40% In-class lab exercises and participation.
30% Homework
30% Final project competition
Extra credit opportunities
Every student has to participate (in-person) in the final presentation in order to pass the course.
You will NOT be allowed any extra credit projects/homework/exam to compensate for a poor grade. Everyone is given the same opportunity to do well in this class. I may use class participation to make grade adjustments at the end of the semester.
The final grade is based on the grade-percentage conversion Table 1 on the next page. \([x, y)\) means greater than or equal to \(x\) and less than \(y\). For example, 94.1 is in \([94, 100]\) and the grade is A and 93.8 is in \([90, 94)\) and the grade is A-.

There are several in-class lab exercises, which are graded as complete/incomplete and used as evidence of attendance and class participation.
You are allowed to have two incomplete lab exercises without penalty. Beyond that, 2% grade percentage will be taken off for each missing/incomplete exercise.
No make-up lab exercises for any reason.

The homework assignments are individual. You should submit your own work.
You may not directly share or discuss answers/code with anyone other than the instructor. But you are welcome to discuss the problems in general and ask for advice.
Homework will be assigned through GitHub. You need to clone/pull the homework repo into Posit Cloud and work on the Quarto file in the repo. A step-by-step guide will be discussed in class before homework is assigned.
You will have at least one week to complete your assignment.
No make-up homework for any reason unless you got COVID or excused absence.
If you miss a homework assignment due to COVID-19 symptoms, exposure, diagnosis, quarantine, and/or isolation, or you have an excused absence as defined in Attendance in Academic Regulations, the homework percentage will be added to your final project. If you miss more than one assignment, only one assignment percentage can be added to the final project percentage. You get 0% for the other assignment.

You will be team up to do the final project. Your project can be in either of the following categories:
1. Data analysis using statistical models or machine learning algorithms
2. Introduce a R or Python package not learned in class, including live demo
3. Introduce a data science tool (visualization, computing, etc) not learned in class, including live demo
4. Web development: Shiny website or dashboard, including live demo
Details about the project will be provided as the course progresses. You must complete the final project and be in class to present it in order to pass this course.

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.