Math 315 Syllabus

Author

Robin Donatello

Published

Jan-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 Section 02
Meeting Times MWF 12:00 - 12:50 pm MWF 1:00 - 1:50 pm
Meeting Location Holt Hall 171 Holt Hall 173

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: Mon 3-5pm at Community Coding, Tue 1-2pm over Zoom. Wed 2-3pm Holt 202

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 Assistants

Khushi Choudhary and Jayana Sarma are Masters in Data Science and Analytics students that will be helping grade, answer questions over Discord or in person at Community Coding, and help out directly in class during open work periods. Their bios and contact information is posted in Canvas.

Learning Outcomes

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

  • Develop a testable research question.
  • Understand how to use a codebook to identify data relevant to that question.
  • Process, screen, recode, transform, and clean data.
  • Describe data using visualizations and words.
  • Select and carry out an appropriate statistical analysis.
  • Explain study results and limitations to a non-technical audience.
  • Understand and implement a reproducible research pipeline.
  • Become a data nerd (Optional, but recommended).

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 (the stuff)

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: Open Intro Statistics. Downloadable PDF from https://www.openintro.org/book/os/ – SET THE PRICE TO 0 for free
  • Course Notes: The physical course packet will be handed out during the second week of classes. It is a hefty 182 page, and 3 hole punched. Due to the generosity of our Math Department, this will be free for students this term but you will need to supply your own 3 ring binder. You can opt for a PDF version as part of Homework 1.
  • 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 (how do we use that stuff?)

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 the support of me and the learning assistants.

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

I don’t penalize for late work, but I prioritize on time submissions. If you submit an assignment after I’m done grading everyone elses, and we’re moving on to the next topic then your assignment may have to wait a week before I can get back to it. That is likely to negatively impact your ability to move forward and to get feedback in time.

There are exceptions for when your work is peer reviewed. There is no grace period for peer reviews. You must be responsible and timely for your colleagues.

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. 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.

  • Random Call Out: During class I will call on a student at random to answer, or ask a question. You have the right to pass at any time, or better yet, ask a friend or neighbor. When the pile is empty I will record participation and refresh the pile. Do not wait to be called on to ask a question! If you have a question, highly likely others will also.
  • 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 but only if statistically relevant)
  • 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

I would like to create a learning environment where you feel safe to be pushed outside your comfort zone. That’s where learning happens!

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. Refer to the linked document for definitions.

As an instructor I recognize there are a variety of AI programs available to assist in creating text and writing code. I expect that you also recognize that AI programs are not a replacement for human creativity, originality, and critical thinking. Writing (text and code) is a skill that you must nurture over time in order to develop your own individual voice, style, and view.

The use of AI is allowed/encouraged to help you learn how to code but all code used must be fully explained in text. We will cover what this means and how to do this when we start data management. How ChatGPT can help you write code. You are responsible for fact checking the accuracy of statements composed by AI language models. These models are known to produce bullshit responses.

  • Writing code: Using AI to directly write code that you don’t understand is not permitted for any work done in this class. A Notebook LM specifically trained on learning materials for this class may be made available by the instructor. You are allowed to use this resource to help write code.
  • Learning Assistant: Using AI to help debug, or suggest coding approaches is a useful tool for any coder - but only once you have a solid foundation.
  • Collaboration: You are highly encouraged to work together with a classmate to learn the materials in this class. However your work must be 100% a product of your personal effort. Coding styles are similar to writing styles, each person will have their own unique voice and style.
  • Plagiarism and Self-Plagiarism: Plagiarism from the course notes and from your prior work are highly encouraged. Don’t try to start from scratch each time. If you’ve done something before and need it again - find it and copy/paste/adjust. The biggest benefit to programming in a language such as R is to automate repetitive tasks, and ensure your work is reproducible. Self-plagiarism is a hallmark of a good programmer.

In 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 work.
  • Copy/paste from AI tools or internet sources without customization, citation or explanation
  • Getting your sibling/friend/colleague to write code for you
  • Submitting any assignment that is not 100% your own personal effort.
Allowed
  • Helping each other solve homework problems (concepts or code)
  • Use AI to help explain a concept
  • Use AI to generate starter code that you modify for your own example.
  • Any AI assisted tool provided by me to aid your understanding
  • Copy/paste code from my course notes or your own work on a prior assignment(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 score and ask you to come talk with me about the code. Not to penalize or punish, but as a learning opportunity where I can explain why the way that i’ve shown in class is better.

Any use outside of this permission constitutes a violation of Chico State’s Integrity Policy and may result in you being reported to the Office of Students Rights and Responsibilities.

Your own commitment to learning, as evidenced by your enrollment at California State University, Chico, and the University’s Academic Integrity Policy requires you to be honest in all your academic course work. If you act against these policies your actions will be considered academically dishonest, and a violation of Chico State’s Integrity Policy and you may be reported to the Office of Students Rights and Responsibilities.Faculty members are required to report all infractions to the Office of Student Judicial Affairs.

University Policies and Campus Resources

Adding and Dropping the course

This course only runs for a few weeks and all materials are available on the course website. It will be difficult to get caught up if you add the class after the first week. The last day to add or drop classes without special permission by the instructor is 1/30/26. No adds or drops are allowed after 2/16/25 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 libray, 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.