Syllabus

N.B. Syllabus subject to change.

Students in Stat 33B are expected to have read the syllabus in its entirety by the second week of the course.

Description

STAT 33B is a course designed primarily for those who are already familiar with programming in another language (e.g. Python, C, Java), and want to understand how R works, and for those who already know the basics of R programming and want to gain a more in-depth understanding of the language in order to improve their coding.

The focus is on the underlying paradigms in R, such as atomic and non-atomic vectors, functional programming, environments, and object systems (time permitting).

The goal of this course is to better understand programming principles in R and to write better R code that capitalizes on the language’s design.

Topics might include (not necessarily covered in the following order):

• 🔠 data types and data structures in R (e.g. vectors, arrays, lists, data frames)
• 🔨 Tools for data manipulation
• 📊 Tools for data visualization
• 📥 Data input/output
• 📝 Writing simple functions
• 🔀 Control flow structures (e.g. conditionals, iterations)
• 📑 Argument matching
• 📢 Function calls
• ➕ The formula language of R

Communication

Strictly follow these guidelines whenever you need to get in touch:

1. Create a public ED post. This will be helpful for so many others!
2. If the matter is confidential, create a private ED post (visible to the course staff only).
3. If the matter is even more confidential, you can email one specific staff member, but this should be an absolute last resort. We reserve the right to ignore your emails, if we believe you should have gone through ED instad. Canvas messages will always be ignored.

🏫 Mode of Instruction

This semester STAT 33B is structured as a flipped class, meaning that you will gain initial exposure to new concepts before class (mostly in the form of slides and suggested readings) and then apply that knowledge through activities like discussions, problem-solving, and coding during class-and-lab time, as well as outside the classroom.

Before Class. It is your responsibility to become familiar with the topics that appear in the slides sections. Optionally, you may want to review the recommended readings. Likewise, there will be ungraded practice coding exercises in case you want to apply your knowledge or test your understanding of the weekly concepts.

During class on Mondays. Class time will be spent on live demos to illustrate R programming concepts with some small, yet real-life, examples. Do not be surprised or worried about not following the details of R code during class – that is not the point (furthermore, there will sometimes be code that is really specific to the instructional purposes of the lecture, e.g. to make a specific plot, and is beyond the scope of what you would be expected to understand how to do yourself). [TBD]Commented demo files will be shared in bCourses right after class, or one day after class.

During Section on Wednesdays. Lab time will be dedicated to applying your knowledge by working through questions (in the format of lab exercises) solo or in groups.

After Section. You will keep working on the lab exercises and start/continue working on the homework assignments. These assignments are typically extensions of the examples discussed in lecture. However, they will sometimes ask you to learn about a topic related to but not covered in lecture/lab.

Office hours (OH) and Group Tutoring 🗓️

• We may reschedule OH if needed, even at the last minute. The Google Calendar is the up-to-date source of truth.

• For security reasons, Zoom links for OH are posted on bcourses.

Andrea’s Office Hours:

• Thursdays (Zoom) 10:30am-12:00noon

Thong’s Office Hours:

• Wednesdays (Evans 432) 3:30pm-4:30pm

Group Tutoring (John/Ethan):

• Thursdays (Evans 332) 2:00pm-3:30pm

Why/when come to OH/Group Tutoring:

• OH is a great opportunity to discuss not only topics directly related to the course, but also anything else that’s on your mind.
• We also welcome questions about career trajectories and research opportunities at UC Berkeley and beyond.
• Keep in mind that you do not need to come to office hours with an agenda. Listening in is welcomed and encouraged!
• Group tutoring is gonna be just you and the tutors, so it’s an awesome opportunity to ask questions or give feedback that you might feel uncomfortable expressing in front of the instructors, for whatever reason (sad but real). Remember to be respectful of the tutors.

Study groups 👥

We encourage you to work together in groups to solidify your understanding of the course material.

You can use ED Discussion to find group mates.

Our goal is to form the study groups ASAP, so students can begin discussing the first homework assignment.

ℹ️ Prerequisites

Although this course does not have any prerequisites, the curriculum and format are designed specifically for students who have previous programming experience. Students with no or minimal programming experience should not be taking this course; instead they should consider other courses such as STAT 33A Introduction to Programming in R or BJC The Beauty and Joy of Computing. BTW: you don’t need to have taken an introductory course in statistics.

📚 Textbooks

This course does not have an “official” textbook although weekly readings will be assigned from the following books:

• R Coding Basics (by G. Sanchez). https://www.gastonsanchez.com/R-coding-basics
• Advanced R, 2nd Edition (by H. Wickham). https://adv-r.hadley.nz
• R for Data Science (by H. Wickham & G. Grolemund). https://r4ds.had.co.nz
• Hands-On Programming with R (by G. Grolemund). https://rstudio-education.github.io/hopr

🤞 Expectations

This is a programming course rather than a math or statistics course. Programming is a broad topic, so it’s not possible for lecture to provide you with a specific recipe for every situation you may encounter. Instead, one of the goals of this class is to help you become comfortable reading R code, and understanding its underlying working principles, as well as its documentation.

We don’t expect you to become an R expert. That takes YEARS of practice and learning. Instead, we want to give you a good foundation to understand the R programming language.

🏫 Course Culture

Students taking Stat 33B come from a wide range of backgrounds. We hope to foster an inclusive and supportive learning environment based on curiosity and mutual help rather than competition. All members of the course community—the instructor, GSI, tutors, students, and readers—are expected to treat each other with courtesy and respect.

You will be interacting with course staff and fellow students in several different environments: in class, in lab, over the discussion forum, and in office hours. Some of these will be in person, some of them will be online, but the same expectations hold: be kind, be respectful.

If you are concerned about classroom environment issues created by other students or course staff, please come talk to us about it.

⏳ Waitlisted Students and Late Joining

We will not be offering extensions if you are admitted or enrolled into the course later. So it is your responsibility to stay up to date on the assignments. With that said, please keep in mind our policies for Participation and HW (see sections below).

Unfortunately, doing all the work is not a guarantee of enrollment. You will only be enrolled if there is space in your lab. Enrollment will proceed through CalCentral.

📝 Attendance and Labs (5% + 10% of final grade)

PLEASE, DO NOT COME TO CLASS IF YOU THINK YOU MIGHT BE SICK! EVEN A LITTLE!!!

• You’ll recover more slowly, get other people sick, and lose lecture time, OH, group tutoring and in general make the class harder and less useful for yourself and everyone else.

• we are a mask-friendly class

• Attending lab section every week is an essential part of the course and you will be working on lab exercises to apply your knowledge by working through questions solo or in groups.

• You must attend the lab section you are officially enrolled in.

• We will drop 2 attendances in the calculation of your overall grade.

• They consist of questions to check your understanding from the slides you have to read before each lecture.

• Regardless of whether you work solo or in groups, you must turn in your answered lab on gradescope (i.e. individual submission).

• Labs will be graded credit / no-credit for completion where full credit is given if you earnestly engaged with the assignment (that is, put in a good effort to complete the problems).

• Solutions will be posted on bCourses on the Friday after lab discussion.

• The first lab will not count towards your score. From the remaining labs, your lowest 2 scores will be dropped in the calculation of your overall grade.

📁 Homework (45% of final grade)

• There will be bi-weekly homework (coding) assignments.
• These assignments will typically be extensions of the examples presented during lecture.
• HW assignments will be submitted through gradescope (individual submission).
• HW will be due at 11:59PM on the Sunday of the second week after they were assigned in section (~12 days). Late submissions until class time on Monday will incur an automated 15% deduction.
• You must write your own answers (using your own words and/or code). Cheating and plagiarism will not be tolerated (see Academic Honesty policy).
• If you don’t submit all required files, or incorrect files, you will receive no credit on those parts of the assignment.
• Your lowest HW score will be dropped in the calculation of your overall grade.

🕚 Late Submission Policy

Given that STAT 33B is a 1-unit course, unless you have applicable DSP accommodations, we won’t be able to offer extensions of any kind on top of what above specified.

In case you are not able to submit an assignment on time, our default policy is:

• HW submissions within the start of the next Monday class will incur an automated 15% deduction.
• Late lab submissions will not be accepted.

This late submission policy is in place to take care of any extenuating circumstances that prevent you from submitting a HW assignment by the due date. In addition, recall that we are dropping the lowest HW score and two lowest lab scores.

Please plan ahead and pace yourself. Don’t wait until the last day to do an assignment.
Don’t wait until the last minute to submit your assignments.

📄 Exams (40% of final grade)

• There will be one in-person midterm scheduled for March 16th 2026. The midterm will take place during class time (Monday, 3pm-4pm, Hearst Mining 390)

• There will be one in-person final exam. The final exam will be held on Wednesday, May 13th, 7pm-10pm, location TBD as scheduled by the University.

• Unless you have accommodations as determined by the university and approved by the instructor, you must take the exam at the date indicated above.

The overall grade for the exams is a combination of midterm and final.

• The final will be divided into two parts: final_p1 (covers same material as midterm) and final_p2 (covers material after midterm).
• Exams grade = 50% final_p2 + 50% max(final_p1, 50% final_p1 + 50% midterm)
• The idea is that if you don’t do well on the midterm, you can neutralize that result by doing better on that same part of the final.

💯 Grading Structure

Grades will be assigned using the following scheme:

1. 5% Attendance (2 drops)
2. 10% Labs (2 drops)
3. 45% HW (1 drop)
4. 40% Exams

• To give you an idea of the grading scheme, the assignment of letter grades will be roughly:

  • 😀 >= 88% (Excellent) A-/A/A+ range
  • 🙂 >= 77% (Good) B-/B/B+ range
  • 😐 >= 66% (Fair) C-/C/C+ range
  • 🙁 >= 60% (Deficient) D
  • 😞 < 60% (Failed) F

• N.B. Small adjustments might be applied to better reflect exam and assignments difficulty

• If you are taking the class pass-fail, the cut-off for passing is 66% (C-).

• As a matter of course policy, I do not round up when calculating letter grades. Ex: if your overall score is 79.9999%, then the highest letter grade that you can expect is a C+, not a B-.

• There is no curve: your grade will depend only on how well you do, and not on how well everyone else does.

• Also, please remember that we grade your course performance, not your personal worth. You will get all my respect, help, and care, no matter how well you do in the class, as long as you don’t disrupt or disrespect others.

⚠️ Generative A.I. Policy

Generative A.I. refers to artificial intelligence technologies, like those used for ChatGPT, Gemini or similar, that can draw on a large corpus of training data to create new written, visual, or audio content.

There are two principles we use to guide our class policy on AI use:

• Cognitive dimension: Working with AI should not reduce your ability to think clearly. The use of AI should facilitate—rather than hinder—learning.
• Ethical dimension: Students using AI should be transparent about their use and make sure it aligns with academic integrity.

In this course, we’ll be developing skills that are important to practice on your own. Because use of generative A.I. may inhibit the development of those skills, the use of these tools is permitted in this course for the following activities:

• Brainstorming and refining your ideas;

The use of generative A.I. tools is not permitted in this course for the anything else, including:

• Impersonating you in classroom contexts, such as by using the tool to compose discussion board prompts assigned to you or content that you put into a discussion forum/chat.
• Attempting to pass off AI-generated work as your own.
• Writing a draft of your assignment.
• Writing/checking code, functions, or scripts to complete class assignments.

In the very rare event that any of your work is produced by AI, please give credit to the tool(s) you are using.

If you are unsure of whether you are in violation of a certain policy, please reach out to us and ask for guidance.

☝️ Academic Honesty

You should not share your code or answers, directly or indirectly, with other students. Doing so doesn’t help them; it just sets them up for trouble on exams. Feel free to discuss the problems with others beforehand, but not the solutions. Please complete your own work and keep it to yourself (e.g. avoid sharing it in hosting platforms like Github or similar). If you suspect other people may be plagiarizing you, let us know ASAP.

We expect you to do your own work and to uphold the standards of intellectual integrity. Collaborating on homework is fine and we encourage you to work together—but copying is not, nor is having somebody else submit assignments for you. Likewise, obtaining and/or using solutions from previous years or from the internet, if such happen to be available, is considered cheating.

Beyond the templates or starting code provided by the teaching staff, any writing, code, media, or other submissions not explicitly identified as AI-generated will be assumed as original to the student. Submitting AI-generated work without identifying it as such will be considered a violation of the Code of Student Conduct.

Cheating will not be tolerated. Any evidence of academic misconduct will result in a score of zero (0) on the entire assignment or examination, and a failing letter grade. We will always report incidences of cheating to the Center for Student Conduct.

If you are having trouble with an assignment or studying for an exam, or if you are uncertain about permissible and impermissible conduct or collaboration, please contact us.

Rather than copying someone else’s work, ask for help. You are not alone in this course! The course staff is here to help you succeed. If you invest the time to learn the material and complete the projects, you won’t need to copy any answers.

🚸 Special Accommodations

Students needing accommodations for any physical, psychological, or learning disability, should contact the teaching staff during the first two weeks of the semester, and see http://dsp.berkeley.edu to learn about Berkeley’s policy. If you are a DSP student, please contact us at least three weeks prior to a midterm or final so that we can work out acceptable accommodations.

For relevant DSP accommodations that provide occasional extensions on assignments, please see the above Late Policy.

❗Incomplete Grade

Under emergency/special circumstances, students may petition me to receive an Incomplete grade. By University policy, for a student to get an Incomplete requires (i) that the student was performing passing-level work until the time that (ii) something happened that—through no fault of the student—prevented the student from completing the coursework. If you take the final, you completed the course, even if you took it while ill, exhausted, mourning, etc.

The time to talk to me about incomplete grades is BEFORE you take the final (several weeks before if possible), when the situation that prevents you from finishing the course presents itself. Please clearly state your reasoning in your comments to me.

It is your responsibility to develop good time management skills, good studying habits, know your limits, and learn to ask for professional help. Life happens. Social, family, cultural, scholar, and individual circumstances can affect your performance (both positive and negatively). If you find yourself in a situation that raises concerns about passing the course, please contact me as soon as possible.

Above all, please-please-please do not wait till the end of the semester to share your concerns about passing the course because it will be too late by then.

🌻 Safe and Inclusive Environment

Whenever a faculty member, staff member, post-doc, or GSI is responsible for the supervision of a student, a personal relationship between them of a romantic or sexual nature, even if consensual, is against university policy. Any such relationship jeopardizes the integrity of the educational process. Although faculty and staff can act as excellent resources for students, you should be aware that they are required to report any violations of this campus policy. If you wish to have a confidential discussion on matters related to this policy, you may contact the Confidential Care Advocates on campus for support related to counseling or sensitive issues. Appointments can be made by calling (510) 642-1988.

The classroom, lab, and work place should be safe and inclusive environments for everyone. The Office for the Prevention of Harassment and Discrimination (OPHD) is responsible for ensuring the University provides an environment for faculty, staff and students that is free from discrimination and harassment on the basis of categories including race, color, national origin, age, sex, gender, gender identity, and sexual orientation. Questions or concerns? Call (510) 643-7985, email ask_ophd@berkeley.edu, or go to https://svsh.berkeley.edu/.

🎉 Last But Not Least

The main goal of STAT 33B is that you should learn, and have a fantastic experience doing so. Please keep that goal in mind throughout the semester.