Math 315 Syllabus

Author

Robin Donatello

Published

May-2026

In this project-based course, you will have the opportunity to answer questions that you feel passionately about through independent research based on existing data. The course offers a lot of one-on-one support, directed opportunities to work with other students, and training in the skills required to complete a project of your own design. We will use collaborative tools and software that are common in many workplaces and research labs. These skills will prepare you for many different career types.

Course Details Section 01
Meeting Times MWF 11:00 - 11:50 pm
Meeting Location [UPDATE ME]

Prerequisites: MATH 105, MATH 109, or MATH 120, or faculty permission.
Recommended: Math 130 is a 1 unit, credit/no credit course that runs for 5 weeks and is specifically designed to support your R learning in this class

Instructional Team

Instructor

  • Name: Dr. Robin Donatello (Dr. D, she/her/hers)
  • Office Location: Holt 202
  • E-mail: rdonatello@csuchico.edu
  • Office Hours: [UPDATE ME] at Community Coding, [UPDATE ME]

You can address me as “Robin”, “Dr. D”, or some other respectful title that you feel comfortable with.

I have a Doctorate in Public Health (DrPH) Biostatistics from UCLA, but I’m a Chico alum. I double majored in Statistics & Biology, with minor in Chemistry, and a first generation college student who started at Butte College.

My campus life consists of training the next generation of Scientists how to harness the power of Statistics and Data in a responsible and ethical manner, leading the Data Science Initiative (DSI) provide training and experiences for students and faculty, and providing analytical support and statistical consulting for many projects on and off campus.

When I’m not on campus, typically I’m growing food for my family, out adventuring with my dogs, or getting some game time in. You can learn more about the projects I’m involved in on my website.

Best method of contact? Discord!

Learning Outcomes

Upon successful completion of this course, students will be able to

  • Develop a testable research question that builds on existing research and can be answered with available data.
  • Use a codebook to identify relevant variables and guide data cleaning decisions.
  • Write data cleaning code that explains how and why the data were modified.
  • Summarize and describe data using visualizations, summary statistics, and clear writing.
  • Choose an appropriate statistical analysis for the research question and data, justify that choice, and interpret the results in context.
  • Explain study results and limitations to a non-technical audience.
  • Build and use a reproducible research pipeline.

Tentative topic list

  • Writing testable research questions
  • Preparing data for analysis
  • Describing distributions of data - in numbers, words and pictures
  • Understanding the role of variability and its ability to
  • Bivariate statistical inference
  • Multiple regression modeling - linear and logistic

Required Materials

I use a combination of tools that are most appropriate for the task or outcome at hand. Your professional life does not live inside of a LMS and so it’s time to stretch outside your comfort area and engage with multiple platforms. Homework 1 provides a checklist for getting connected and testing out your tools. All materials are free.

  • Textbook: Intro to Modern Stats [UPDATE ME]
  • Course Notes: [UPDATE ME]
  • Lecture Videos: This course uses lecture videos from a program called Passion Driven Statistics. These videos provide content and coding examples, help set your understanding of the material in the broader context of research.
  • Reliable Laptop & Internet: Expect to bring your fully charged computer daily. ITSS has laptops available for short and long term checkout and the Library may have some chargers.
  • Course Website: https://math315.netlify.app/. The “home page” for all the class materials including the Schedule overview, assignment and quiz links, readings etc.
  • Analysis Software: We will be using the R programming language, in the R Studio environment for this class. A JupyterHub platform has been set up for Chico so all you will need is a web browser to use this software.
  • Canvas: Assignment submission, gradebook, class schedule/calendar
  • Gradescope: We will use Gradescope for the actual grading of most assignments. Clicking the assignment submission button in Canvas will take you to Gradescope to submit your assignment, review grading comments, and request regrades. You can also log into Gradescope directly if you wish.
  • Google Drive: Daily/Weekly updates, quizzes and collaborative work.
  • Discord: Used for outside class announcements, discussions, meme sharing, homework help and general chatter. EXCELLENT for sharing code problems and helping others debug. I will not answer most class-content based questions through email.

Class Flow

Before class

  • Watch the Passion Driven Statistics (PDS) videos where assigned.
  • Read through the course packet and corresponding textbook sections and start to fill in the blanks.
  • Take the corresponding individual quiz.

During class

  • We will briefly review the topic for the day (Housekeeping)
  • On group quiz days - go over any quiz questions that were very low scoring as a group.
  • Work through exercises together from the course packet.
  • Open work time (as much as I can) for you to work on homework and your research project with my support.

After class

  • Work with your research partner on your project
  • Complete the homework

Grading

See Canvas for due dates and submission instructions. I highly recommend using the Canvas calendar to manage your schedule.

The following statement applies to all graded work: “AI slop, gibberish, and answers using methods not discussed in class will not receive credit”. See my Academic Integrity policy for more details.

Your final grade will be an equally weighted sum of each the domains described below. You can check your grade and do a “what if” analysis at any time in Canvas. I use a letter grade following a mostly standard scale: A [100 - 93], A- (93 - 90], B+ (90 - 87], B (87 - 83], B- (83 - 80], C+ (80 - 77], C (77 - 73], C- (73 - 70], D+ (70 - 67], D (65 - 60] F < 60

Late work policy

Assignments close one week after the original due date, or 48 hours before the exam, whichever comes first. Something submitted late is better than nothing, but work turned in after we’ve moved on rarely helps you — and that shows up on exams.

Regrade requests on individual questions are accepted through Gradescope until 48 hours before the exam. After that, grades are final. Entire assignment submissions are allowed if you scored below 50%.

Peer review deadlines are firm with no grace period. Your colleagues are depending on you.

Grading Domains

Research Project

  • You will be working with a co-author on a research project throughout the term.
  • Written assignments throughout the semester are intentionally connected and serve as practice opportunities
  • You will present your work as a poster at the end of the semester.
  • You will have an opportunity to provide feedback and critique of your research partner to ensure the contributions to work do not become imbalanced.
  • I reserve the right to adjust your grade up or down based on your peer evals and my observations of your engagement in the project.

Assignments

  • 10 Written assignments done in R + Quarto and submitted through Gradescope/Canvas.

Quizzes

  • 12 Individual + Group quizzes
  • Administered through Google Forms. Must use campus login.
  • Cannot be made up if missed
  • Lowest score dropped
  • Open note/book, but not open friend on the individual quiz.

Exams

  • Two non-cumulative exams. Approximately Week 8 and week 15.
  • An Exam Error Assessment is available for Exam 1.

Active Learning

  • You will keep a learning learning journal
  • Peer reviews of your classmates projects
  • Active participation (details below)

Learning statistics and data science is not a spectator sport. When students ask questions, explain their thinking, or help one another, it improves understanding for everyone. Active participation is therefore an important part of this course. Participation can take many forms, and you are not expected to do everything listed below. Instead, you will choose multiple ways (minimum 2) to contribute to our learning community over the course of the term. The goal of this grading area is to support an engaged, respectful learning community while giving you flexibility in how you choose to participate.

  • Question Prompt: After covering a topic, I’ll open the floor for questions. If no one speaks up, I’ll call on someone at random — not to answer a question, but to ask one or make a comment. You can always pass. Students have generally found this low-stakes once they realize the expectation is curiosity, not performance.
  • Discord collaboration: Discord is our primary method of communication outside of class and can serve as a very helpful resource. Ask questions, answer others’ questions, share screenshots, explain your thinking, or contribute useful discussion. Memes are encouraged.
  • Attend Office Hours: Office hours are open, no appointment required. You are welcome to stop by to ask a question, work quietly, or just check in—even for a short time. It is time that I have set aside to specifically help you. Meaningful participation is at least 2
  • Attend Community Coding: Similar to drop in Mathematics tutoring hours, students, staff, faculty, and the public are invited to join our Community Coding sessions. Bring your homework, research project and your questions to this open working environment. This can be a great time to sit with a friend and work on the homework or finish the notebooks.

How this is graded

  • Participation is tracked over 4- 4-week periods
  • Full Marks: You will earn full marks if either of the following is true:
    • You participate in two different participation options, or
    • You show meaningfully participation in one option. Examples include (but are not limited to):
      • Regularly asking and answering questions in Discord
      • Attending Community Coding weekly and actively working or asking questions
      • Consistent in-class participation when called on or doing group work (but not dominating the conversation).
  • Partial Marks: You participate in only one activity.
  • No Marks: You do not participate in any of the options listed

My Class Policies

Code of Conduct

Everyone is welcome here

It is my intent that students from all diverse backgrounds and perspectives be well-served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that the students bring to this class be viewed as a resource, strength and benefit. It is my intent to present materials and activities that are respectful of diversity: gender identity, sexuality, disability, age, socioeconomic status, ethnicity, race, nationality, religion, and culture.

Supportive Learning Environment

This course will push you into unfamiliar territory — that’s intentional, and it’s where real learning happens. I’ve built in multiple support structures to help you get there: office hours, Community Coding, and Discord. Use them.

To help accomplish this:

  • Let me know if you have a name and/or set of pronouns that differ from those that appear in your official Chico records.
  • Help me pronounce your name as accurately as possible. Corrections and patience are welcome.
  • If you feel like your performance in the class is being impacted by your experiences outside of class, please don’t hesitate to come and talk with me. I am a resource for you.
  • Everyone must agree to treat each other with kindness and respect, understand that we all come from different backgrounds and have different experiences and strengths.
  • When in doubt, assume a positive intent.

Attendance

Class attendance is expected. Talk to me ahead of time if you need to miss a class for a planned reason. In the event of an unplanned reason, PM me in Discord when you can so that I know you are still alive.

This is a college class and you all are adults with lives outside this class. Things happen. Each class session will be live streamed, with the recording posted to Canvas within a few days. Common reasons to join the class virtually: - you’re sick - you’re out of town

Don’t expect as good of quality of presentation, and this is not a long term solution/resource. Don’t abuse this resource.

Academic Integrity

Students are expected to be familiar with the University’s Policy on Student Academic Integrity. Specific sections of this policy are highlighted below as they pertain to this class.

Artificial Intelligence

How I use AI

There is only one of me and a lot of you. I use AI as a thinking partner, a first-draft machine, a research and code assistant — while applying my own expertise to catch when it’s wrong. I use it to provide better, faster support to you.

Your work never touches AI. I do not use AI to grade assignments or evaluate your writing or code.

Tools available to you

I am providing two course-specific AI tools, both linked in Canvas:

  • Course Assistant (custom ChatGPT): Trained on the syllabus, learning activities, and advice from prior students. Use this to navigate the course and figure out how to succeed. It does not contain course content.
  • Research & Content Assistant (Notebook LM): Trained on course notes, homework, research instructions, and dataset codebooks. Use this for help with content, your research project, and R code questions.

Use these instead of general-purpose AI. Open LLMs and internet sources may give code that is technically correct but overly complex or confusing for where you are in your R learning right now. Often it also does not use the style and functions that we are learning in this class and it becomes much much more confusing for students.

In addition, all the code you need to succeed in this class is provided as part of the assignments and course packet. I expect you to copy my code and reuse your own prior work — that’s how programming is learned, and it’s a hallmark of good reproducible practice. Don’t start from scratch if you’ve done something before; find it, copy it, and adapt it.

What you are not permitted to do is pull code from general internet sources or open AI tools. If you use code you don’t understand, you won’t know when it’s broken.

How to use AI well

Use it a thinking partner, to help debug code and understand error messages, to learn how to write code to take your ideas for a new variable or modification to an existing variable. Just expect to push back on it when it shares code that doesn’t make sense or uses variable names that aren’t correct.

There will also be opportunities to have it help you revise writing to ensure that your ideas are clearly connected to each other.

Collaboration

You are highly encouraged to work together with classmates to learn the material. However, your submitted work must be 100% a product of your personal effort.

Summary

Not Allowed
  • Working with or getting help from others on exams and individual quizzes
  • Copying code from another student’s homework and presenting it as your own
  • Copy/paste from AI tools or internet sources without customization, citation, or explanation
  • Getting someone else to write code for you
  • Submitting any assignment that is not 100% your own personal effort
  • AI-generated writing submitted as your own
  • Code using tactics or styles not taught in this course
  • Using AI on a quiz or exam
Allowed
  • Helping each other solve homework problems (concepts or code)
  • Using course-provided AI tools to explain a concept or generate starter code that you modify
  • Copy/paste code from my course notes or your own prior assignments (Encouraged!)

If at any time I suspect that the work you are submitting is not reflective of your personal knowledge, I will assign a 0 and ask you to come talk with me — not to penalize, but as a learning opportunity. Any use outside of this permission constitutes a violation of Chico State’s Integrity Policy and may result in referral to the Office of Student Rights and Responsibilities.

University Policies and Campus Resources

Adding and Dropping the course

The last day to add or drop classes without instructor permission is September 4. No adds or drops are allowed after November 13 without a serious and compelling reason approved by the instructor, department chair, and college dean.

IT Support Services

Computer labs for student use are located in multiple locations in the library, Tehama Hall Room 131, and in the lobby of the new BSS building. You can get help using your computer from IT Support Services; contact them through the ITSS web site at http://www.csuchico.edu/itss. Additional labs may be available to students in your department or college.

Americans with Disabilities Act

If you need course adaptations or accommodations because of a disability or chronic illness, or if you need to make special arrangements in case the building must be evacuated, please make an appointment with me as soon as possible, or see me during office hours. Please also contact Accessibility Resource Center (ARC) as they are the designated department responsible for approving and coordinating reasonable accommodations and services for students with disabilities. ARC will help you understand your rights and responsibilities under the Americans with Disabilities Act and provide you further assistance with requesting and arranging accommodations. Phone: 530-898-5959. Location: Student Services Center 170. Email: arcdept@csuchico.edu. Website: http://www.csuchico.edu/arc

Chico State Basic Needs Project

The Hungry Wildcat Food Pantry provides supplemental food, fresh produce, CalFresh application assistance and basic needs referral services for students experiencing food and housing insecurity. All students are welcomed to visit the Pantry located in the Student Service Center 196. Check the website for a location map and for the most up to date information on open hours: https://www.csuchico.edu/basic-needs/pantry.shtml.

Confidentiality and Mandatory Reporting

As an instructor, one of my responsibilities is to help create a safe learning environment on our campus. I also have a mandatory reporting responsibility related to my role as a your instructor. I am required to share information regarding sexual misconduct with the University. Students may speak to someone confidentially by contacting the Counseling and Wellness Center (898-6345) or Safe Place (898-3030). Information on campus reporting obligations and other Title IX related resources are available here: www.csuchico.edu/title-ix.