MATH/CSCI 385: Introduction to Data Science

Author

Robin Donatello

Published

Sep-2026

Course Information

Data Science is the science of learning from data in order to gain useful predictions and insights. This course provides an overview of the wide area of data science, with a particular focus on the tools required to store, clean, manipulate, visualize, model, and ultimately extract information from various sources of data. Topics include the data analytics lifecycle, data wrangling and visualization, modeling, and communicating results. Emphasis is placed on reproducible research, version control, and communicating results to a non-technical audience.

Course Details Section 01
Meeting Times TTh 12:30-1:45pm
Meeting Location BSS 243
Discord https://discord.gg/xCQTAgu8bE
Class Website https://data385.netlify.app/

Prerequisites: Prerequisites: CSCI 111, MATH 130, or MATH 230; MATH 109 or MATH 120

Instructor

  • Name: Dr. Robin Donatello (Dr. D, she/her/hers)
  • Office Location: Holt 202
  • E-mail: rdonatello@csuchico.edu
  • Office Hours: M 1-2pm, TR 2-3pm in Holt 202, and Wed 1-3pm at Community Coding

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. Post in the #385-class-chat channel.

Learning Outcomes

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

  1. Implement Reproducible Workflows
    • 1a. Write clean, readable R code using tidyverse framework and apply it to real data problems
    • 1b. Maintain reproducible, well-documented workflows using Quarto, Git, and GitHub
  2. Work with Data
    • 2a. Wrangle, reshape, and clean data to prepare it for analysis
    • 2b. Create accurate and effective data visualizations that support data-driven decisions
    • 2c. Conduct exploratory data analysis and summarize findings in writing
  3. Model patterns and communicate findings
    • 3a. Build and evaluate predictive models
    • 3b. Navigate the data science lifecycle — from formulating an answerable question to communicating results to a non-technical audience
  4. Work in Context
    • 4a. Collaborate on a data science project using version control and code review
    • 4b. Use LLMs effectively as a coding and research aid, and evaluate their outputs critically
    • 4c. Identify and discuss ethical implications of data science methods and products

Tentative Topic List

See the schedule for more details and Canvas Calendar for due dates.

  • The data analytics lifecycle
  • Reproducible workflows with Quarto
  • Version control and collaboration with Git and GitHub
  • R fundamentals — data types, structures, objects
  • Data visualization with ggplot2
  • Data wrangling with dplyr
  • Tidy data principles
  • Exploratory data analysis
  • Using LLMs as a data science efficiency tool
  • Customized functions
  • Predictive modeling — linear and logistic regression
  • Ethics and social implications of data science

Course Logistics

Class Flow

This is an self regulated learning classroom. You are responsible for engaging with learning material and content before class. You may have heard this as a “flipped classroom”, but I will integrated lecture and activities such as team work and code-alongs. I will not lecture on all the content that you are required to learn.

  • Sun: week’s assignments due by midnight
  • Mon: prepare for Tuesday’s class
  • Tue: class
  • Wed: prepare for Thursday’s class
  • Thu: class
  • Fri–Sun: complete that week’s homework/reading response, due Sunday at midnight

Activities started in class are due by the end of that class

Toolkit

Here are the platforms and accounts that you will need for this class and briefly how they will be used. All materials are free.

  • Course Website https://data385.netlify.app/
    • All course material
    • Schedule
  • Textbooks
  • Canvas
    • Assignment submission (sometimes)
    • Gradebook
    • Due dates
  • 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.
  • Analysis Software R and RStudio through the Cal ICOR JupyterHub server in the cloud. We will connect to this in class in the first week. Local installation possible later in the semester.
  • Git and GitHub
    • Version control and collaboration platform
    • You will make an account if you don’t already have one.
    • Assignment submission (sometimes)
  • 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 any coding related and most class-content based questions through email.

Can I use a local install of R and RStudio instead of using the RStudio containers?

The short answer is, I’d rather you didn’t at the start. One: this will save yourself some headache, Two: RStudio now comes pre-loaded with an AI agent and its extremely important for your early learning to not rely on AI assistance.

Later on in the semester, depending on how you all are doing and general vibes I would love to transition everyone to a local installation. But let’s do that as a group. Successful installation of these software is not a specific learning goal of this course, but could be a great eventual goal.

A note on course videos

Most videos linked throughout this course come from Duke’s DS in a Box / STA 199 courses, recorded around 2020. A few things to watch for:

  • Some content is Duke-specific (their policies, their toolkit setup) — that’s expected, focus on the R/data science content itself
  • Terminology has shifted since 2020:
    • “R Markdown” → we use Quarto, its modern successor. Same core idea (code + narrative in one document), slightly different syntax. e.g. Knit → Render
    • “RStudio Cloud” / “Posit Cloud” → we use RStudio via JupyterHub (Chico State’s setup)

If a video shows something unfamiliar, it’s probably just an older/different setup — not a mistake on your end. Ask if you’re not sure. Don’t just stay confused.

See more on the Course Support page of the class website.

Grading

Grading Domains

  • (30%) Exams - One midterm around week 8, and a final during finals week
  • (20%) Project - Semester long group project with several check ins
  • (20%) Homework - Approximately every other week
  • (20%) Participation - Attendance, in class activities, and reading responses
  • (10%) Quizzes - Approximately every 2-3 weeks

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 AI policy for more details on allowable and disallowable use of AI in this class.

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

  • Homework: Up to 3 days late (-5% per day)
    • After 3 days it’s automatically half credit and will not receive feedback.
    • One-time waiver for homework late penalty. Must request before the due date.
    • HW not accepted more than 1 week late.
  • In class activities, Exams, Quizzes, Presentations & Projects: No extensions or make-ups

Assignment Submissions

Here is some general guidance on how to submit homework. Exceptions will exist so always check the submission instructions for each assignment carefully.

  • Application Exercises (AE) are activities we do in class and counted as participation. You get full credit by

      1. participating in the exercise while in class - even if we don’t finish during class
      1. submitting it via a Github push by EOD.

  • Code alongs are a type of lecturing where you watch someone present the content (either me during class or via Duke video outside of class) and you follow along in a set of template “fill in the blank” notes. These are submitted through Github and count towards participation credit.

  • Homework will be submitted in 2 places:

      1. github to assess commit history and collaboration when appropriate
      1. the rendered PDF to gradescope to grade content and completion.

  • Project assignments will be similar to homework, but the deliverables will vary from presentations to team documents published as a website.

  • Quizzes and Exams are paper and pencil and done during class time.

Gradescope

  • To limit the number of websites you have to remember, you can go to Canvas to submit your homework assignment. There will be a button in that assignment that will take you directly to Gradescope to submit your file.
  • Be sure to assign all pages to questions, and that all questions have pages assigned to them.
  • After an assignment is graded, grades will be released and automatically synced with Canvas gradebook.
  • You can see how to request a regrade on individual questions here. Do not re-upload your entire assignment unless authorized by the instructor.

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 and activities done during the class period will be turned in by end of that period. Talk to me ahead of time if you need to miss a class for a planned reason (e.g. sportsball). In the event of an unplanned reason, PM me in Discord when you can so that I know you are still alive.

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.

Collaboration

You are highly encouraged to work together with classmates to learn the material. However, unless the assignment is an explicit group project, your submitted work must be 100% a product of your personal effort.

Artificial Intelligence

Course AI Policy

This course has an explicit two-phase AI policy tied to the academic calendar.

Phase 1 (approximately Weeks 1–8): LLMs and AI coding assistants are not permitted. Students build R fundamentals and data science reasoning without AI assistance. The goal is to develop enough vocabulary and mental model of how R works that you can articulate what you need — which is a prerequisite to using AI well.

Phase 2 (approximately Weeks 9–16): LLMs are permitted and explicitly taught — as a coding aid, a research support tool, and as course content. We will discuss how they work, where they fail, and how to use them responsibly. Using AI without understanding the output remains a violation of academic integrity in both phases.

How to use AI well

Use it as a thinking partner, to help debug code and understand error messages, and to learn how to write code to implement ideas you can already articulate. Push back on it when it shares code that doesn’t make sense or uses approaches not taught in this class. It will often over-engineer, provide extra and unnecessary code, and not remember context or instructions. It’s pretty good, but not that good.

Summary

ImportantNot Allowed
  • Working with or getting help from others on exams and 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
  • Using AI on a quiz or exam
  • Using AI tools during Phase 1 of the course
WarningAllowed
  • Helping each other solve homework problems (concepts or code)
  • Using AI tools during Phase 2 for code assistance, debugging, and research support
  • Copy/paste code from 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 9/4/26. No drops are allowed after 11/13/26 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 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

Chico State’s Basic Needs Initiative: Chico State is committed to helping students meet their basic needs. Students having trouble securing food or experiencing housing insecurity are urged to visit the Basic Needs website at https://www.csuchico.edu/basic-needs/ or the office in SSC 190. For long-term food solutions visit the CalFresh office located in the Cross-Cultural Leadership Center and El Centro, MLIB 172 and MLIB 161. You can also make an appointment online at CalFresh website.

The Hungry Wildcat Food Pantry provides supplemental food, fresh produce, 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/

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>.