Syllabus

All information in the syllabus is subject to change as long as this message is visible!

Students in Stat 131A are expected to have read the syllabus in its entirety by the second week of the course.

Course Details πŸ₯—

Description πŸ”Ž

Stat 131A is a upper-division course that follows Data 8 or STAT 20. The course will teach a broad range of statistical methods that are used to solve data problems, including group comparisons, standard parametric statistical models, multivariate data visualization, and multiple linear regression and classification. Students will be introduced to the widely used R statistical language and they will obtain hands-on experience in implementing a range of statistical methods on numerous real world datasets.

In short, Stat 131A will provide you with a Swiss army knife of foundational statistical methods to use for data science projects!

Lectures πŸ§‘β€πŸ«

MWF 2-3pm @ GSPP 150

Lecture attendance is mandatory. See below for more details.

Office hours (OH) πŸ—“οΈ

Josh’s Office Hours:

  • MWF right after lecture at 3pm

Coming to office hours does not send a signal that you are behind or need extra help. In fact, the students who come to OH are often the most successful in the course.

  • 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!
  • Finally, attending and participating in office hours is a great way to set yourself up for a terrific letter of recommendation. This is true for most courses!
  • If you don’t already, I highly recommend that you attend the instructors’ office hours in other classes from time to time.

Optional (and free!) Group Tutoring πŸ—“οΈ

More information coming soon. Starting in Week 2 or 3.

Course platforms πŸ–₯️

bcourses will only be used for secure course material, like exam solutions, grades, and office hour Zoom links.

All other course materials will be posted on the public course homepage.

Assignments should be submitted via Pensive.

All course communication will take place via Ed.

Grades πŸ’―

Grades are calculated as follows:

  • Prerequisite Quiz: 5%
  • Lecture attendance and participation: 10% + up to 2% bonus for having a variety of neighbors in neighbor discussions
  • Homework: 15%
  • Midterm 1: 15%
  • Midterm 2: 15%
  • Midterm 3: 15%
  • Final: 25%

Grades will not be curved.

  • In other words, there is no limit to the proportion of students with an A, B, etc. You are incentivized to help each other learn and succeed.
  • You are guaranteed an A if you score 93% or higher, an A- if 90% or higher, a B+ if 87% or higher, a B if 83% or higher, and so on.
  • A+ grades are awarded rarely, and only for truly exceptional performance.
  • Grade cutoffs may be adjusted downward at the end of the semester, but this is not guaranteed.

See attendance policy below for an opportunity to earn up to two percentage points of extra credit.

Lecture technology policy ❌ πŸ‘©β€πŸ’» \(~\) βœ…πŸ“±

Most lectures will consist of an interactive problem-solving session, along with a hands-on demo or coding session.

  • Laptops and tablets with attached keyboards are not allowed during the problem-solving session, though you are permitted to use a tablet to take handwritten notes.

  • If you need to use technology for accessibility reasons, the previous bullet does not apply to you.

  • Laptop use is permitted (and encouraged!) during the hands-on demo and coding sessions.

  • Phones are allowed during lecture. It is preferable to use a phone to submit conceptual questions and neighbor discussion answers during lecture.

  • This article explains why we have the laptop policy. Long story short, laptop use can negatively impact the learning of nearby students (i.e., this policy is not intended to punish you; the policy prevents you from punishing others).

  • The course staff reserves the right to reduce your lecture attendance grade for violating the technology policy.

Lecture recordings πŸŽ₯

Lectures will be recorded automatically.

  • The course staff cannot guarantee audio or video quality.
  • Lecture recordings are posted on bcourses.

Office hours are not recorded.

The homework assignments may occasionally ask you to watch additional recordings to supplement the lecture material (e.g., if we run out of time covering an essential topic).

Attendance and participation βœ‹

In-person lecture attendance is mandatory.

  • It is critically important to practice learning in a live setting.
  • Difficulty with paying attention in live meetings is a common hurdle for new grads.

Lecture attendance is a substantial component of your grade.

  • Lecture cannot be attended remotely.
  • You are allowed three unexcused lecture absences. Each additional absence will impact your lecture attendance grade.

If you cannot attend a lecture due to an extenuating circumstance, please complete the lecture attendance excusal form before the lecture starts.

  • This form can be completed months, weeks, or days in advance of lecture.

Acceptable extenuating circumstances include:

  • Illness. DO NOT come to class if you are sick! Even a sniffle!
  • Personal emergencies.
  • Important life events (e.g., weddings)
  • Pre-planned collegiate athletic events in which you are a participant.
  • This list is not exhaustive. If you think an absence should be excused, complete this form and explain your reasoning. We cannot guarantee that your absence will be excused, but we will be reasonable.

Concept checks (5% of grade) βœ…

We will use in-class concept checks to track attendance.

  • Concept checks are not graded.
  • Concept checks are answered via this form.
  • Submitting a concept check outside of standard lecture time is considered cheating and an honor code violation. We will use your seat number and submission time to validate that your responses were entered during lecture time. We reserve the right to photograph the lecture hall to verify attendance.

Neighbor discussions (5% of grade + 2% bonus) πŸ—£οΈ

In addition to concept checks, there may be one or more neighbor discussions during each lecture.

  • Neighbor discussion answers are submitted via this form.
  • Neighbor discussion answers are not graded.

To encourage discussion among all classmates, we will award up to two percentage points of extra credit for having a variety of neighbors.

  • The students with the highest number of unique neighbors will receive the full two percentage points of extra credit.
  • Everyone else will receive, at the minimum, a fraction of extra credit proportional to their number of unique neighbors.
  • For example, if you sit next to the same person all semester, you can receive full participation credit for neighbor discussions, but you will earn very little extra credit.
  • The extra credit policy will only take effect if at least one student has spoken to at least 20 unique neighbors over the course of the semester.
  • As above, submitting a neighbor discussion answer outside of standard lecture time or a bogus neighbor discussion is considered cheating and an honor code violation.

Homework (15% of grade) πŸ“

There are 10 homework assignments planned, though the exact number may change.

  • Homework will be a combination of computational exercises and data analysis using the computer, as well as conceptual questions.
  • Homework assignments are weighted equally.

HW is generally due every week or two weeks.

  • Homework assignments will be posted to the course website at least one week before the HW deadline.
  • All homework assignments will be submitted via Pensive and are due by 11:59 pm of the due date.

Homework is graded on completion.

  • However, your work must show a legitimate effort. You can’t submit nonsense.
  • Sure, ChatGPT can finish your homework for you, but you will suffer on exams as a result. Beware!

Late HW ⏰

You are allotted ten slip days for homework assignments.

  • Each slip day adds 24 hours to the deadline.
  • Slip days are intended to account for unexpected delays, like minor illness or homework overload.
  • There is no extra credit awarded for unused slip days.
  • You cannot use partial slip days.

You are allowed to use, at most, four slip days per assignment.

  • In other words, assignments will not be accepted more than 96 hours after the original due date.
  • This policy ensures that we can grade all assignments in a timely fashion.

If you plan to use slip days, do not contact the course staff.

  • We will automatically account for slip days when calculating grades.

Extensions will only be granted if required by a DSP Letter of Accommodation (LoA), or in extraordinary circumstances (e.g., medical emergencies).

Final exam (25% of grade) βŒ›

The written final exam will take place on Thursday December 17, 3-6pm. Location TBD.

Prerequisite Quiz and Midterms βŒ›

Quizzes and midterms are all 50-minutes. They will take place in the computer-based testing facility (CBTF).

You will sign up for a slot on PrairieTest.

  • We will announce when slots are available.

  • Exam slots are subject to availability. Once you are signed up for a particular slot, you are guaranteed that time. If a particular window is full, you will be unable to sign up for that day and time.

Prerequisite Quiz (5% of grade)

The prerequisite quiz is scheduled for Week 2 (8/31-9/4).

  • Once you score at least 85% on the prerequisite quiz, you will receive the full 5 percentage points for your final grade.

  • The prerequisite quiz can be retaken more than once. Your score on the prerequisite quiz is the highest of all attempts.

  • You can practice the prerequisite quiz on PrairieLearn.

Midterm 1 and Midterm 1 Retakes (15% of grade)

Midterm 1 can be taken Thursday October 1 or Friday October 2 in the CBTF.

  • Midterm 1 can be retaken once on Wednesday October 7 or Thursday October 8 in the CBTF.

  • You must score below 90% to be eligible for a retake.

  • If you choose to retake, your Midterm 1 score will be the average of your first attempt score and (your retake score - 10 percentage points).

Midterm 2 and Midterm 2 Retakes (15% of grade)

Midterm 2 can be taken Monday October 26 or Tuesday October 27 in the CBTF.

  • Midterm 2 can be retaken once on Monday November 2 or Tuesday November 3 in the CBTF.

  • The retake rules are the same as Midterm 1.

Midterm 3 and Midterm 3 Retakes (15% of grade)

Midterm 3 can be taken Wednesday December 2 or Thursday December 3 in the CBTF.

  • Midterm 3 can be retaken once on Monday December 7 or Tuesday December 8 in the CBTF.

  • The retake rules are the same as Midterm 1.

Textbooks and resources πŸ“–

Everything you need to know for Stat 131a will be covered in lectures and assignments.

  • It is possible to do very well in Stat 131a without ever referring to an outside textbook or resource.

However, most of the course material is covered by the online textbook developed specifically for 131A.

  • You can find the textbook here.

The StatQuest YouTube Channel is an excellent resource.

  • StatQuest provides videos on many of the topics we will cover in class. The instructor is very entertaining!

If you would like some additional optional reading, you can try the following books:

  • R for Data Science, by Garrett Grolemund and Hadley Wickham. This is a free online book that covers the tidyverse set of R packages.
  • The Statistical Sleuth: A Course in Methods of Data Analysis by Ramsey and Schafer
  • Introductory Statistics with R by Peter Dalgaard

None of these books covers all of the topics we will cover in 131A, nor do they necessarily have the same perspective and focus as this class. But for those students wanting some additional structure or R assistance, these books may be helpful and should be at the right level for this class.

Stat 20 and Data 8 are similar courses, but each covers some subjects that the other does not. While we will cover these topics in class, you may find the following useful background if you are seeing them for the first time (more to follow):

This is the online book used by Data 8. These chapters introduce hypothesis testing using only resampling ideas, ideas which are not necessarily covered in Stat 20.

Policy on Large Language Models (LLMs) πŸ’¬

LLMs (e.g., ChatGPT) are becoming increasingly essential in the workplace.

  • To that end, the use of LLMs is not only permitted in this course, but encouraged.
  • Use this course as an opportunity to learn where LLMs are most useful, and where they fall short.

Potential uses of LLMs in Stat 131A:

  • Generating practice quiz questions
  • Explaining course concepts
  • Helping you code

LLM use on exams is forbidden. We have a zero-tolerance policy for LLM use on exams. If you are caught using an LLM on an exam, you will fail the course.

If you find an especially interesting use case of an LLM for any component of the course, please share it with the course staff! We are excited to hear what you find.

Course communication πŸ—£οΈ

We use the Ed platform to manage course questions and discussion, and to make announcements.

In general, do not email the instructor or course staff.

  • Exception: You are welcome to email individual members of the course staff if you have a private concern that you do not want shared with the entire course staff.

Please post on Ed publicly when possible.

  • Public posts benefit many more students than private posts.
  • We may ask you to change your private post to a public post if the answer could be of use to other students.
  • You are always allowed to remain anonymous!

If you include code in your Ed post, please use the code editing fonts:

Standard font is hard to read:

── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ── βœ“ ggplot2 3.3.2 βœ“ purrr 0.3.4 βœ“ tibble 3.0.3 βœ“ dplyr 1.0.2 βœ“ tidyr 1.1.2 βœ“ stringr 1.4.0 βœ“ readr 1.3.1 βœ“ forcats 0.5.0 ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ── x dplyr::filter() masks stats::filter() x dplyr::lag() masks stats::lag()

# here’s my plot code

x <- ggplot(df) + geom_point(aes(x = year, y = count))

Code font is easier to read:

── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
βœ“ ggplot2 3.3.2     βœ“ purrr   0.3.4
βœ“ tibble  3.0.3     βœ“ dplyr   1.0.2
βœ“ tidyr   1.1.2     βœ“ stringr 1.4.0
βœ“ readr   1.3.1     βœ“ forcats 0.5.0
── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

# here's my plot code
x <- ggplot(df) + geom_point(aes(x = year, y = count))

Computing environment πŸ–₯️

The official course materials use the R programming language.

  • As in Data 8 and Stat 20, labs and assignments will be distributed via DataHub.

You do not need to know anything about R to take this course.

  • We will provide resources for you to learn everything you need to know.

The concepts taught in this course are language-agnostic.

  • In other words, everything you learn in this class can be readily implemented using a combination of other tools (e.g., Python, SQL, etc.).
  • Note that LLMs are an excellent aid for translating your knowledge across different programming language and software.

Weekly topics πŸ₯—

The following is a rough and optimistic guideline for the material we will cover in the semester.

  • The actual topics may vary as the semester goes along.
  • It is likely that we will proceed more slowly than this schedule indicates.
  • Relevant sections of the textbook are linked for each week, though you are only responsible for the material we cover in lecture, lab, or HW assignments.

Week 1. Principles of visualization.

Week 2. Boxplots and histograms. Discrete and continuous distributions. 2.1, 2.3.

Week 3. Probability. Bayes’ theorem. Naive Bayes algorithm. 2.2.

Week 4. Sampling distributions. Bootstrapping. 2.4.

Week 5. Confidence intervals. 3.

Weeks 6 and 7. Non-parametric hypothesis testing. Type I and II errors. Power. Study design. Multiple testing. 3.

Weeks 8 and 9. Linear regression. Feature generation. Transformations. 4.1-4.3, 6

Weeks 10, 11, and 12. Cross-validation. Bias-variance tradeoff. Logistic regression. Classification error metrics. 7 7.5

Weeks 13 and 14. Intro to non-parametric methods. Kernel density estimation (KDE). LOESS. Clustering. Causal inference. 2.5. 4.4-4.5

Week 15. Most likely, catch-up and review. If time permits, decision trees and random forests.

Academic Honesty Policy πŸ‘

Homework must be completed independently, with the following exceptions:

  • You may discuss specific issues/questions you have about the homework at a high level, but you must not sit down and do the assignment jointly.
  • Giving advice about code or coding tips is also not cheating, but you can not directly share code with other classmates.

For exams, cheating includes, but is not limited to, using electronic materials in an exam beyond that allowed, copying off another person’s exam or quiz, allowing someone to copy off of your exam or quiz, and having someone take an exam or quiz for you.

Requesting, obtaining, and/or using solutions from previous years or from the internet or other sources, if such happen to be available, is considered cheating.

In fairness to students who put in an honest effort, cheaters will be harshly treated.

  • Any evidence of cheating will result in a score of zero (0) on the entire assignment or examination, and perhaps a failing grade in the class.
  • We will report incidences of cheating to the Office of Student Conduct, which may administer additional punishment.

Accommodations πŸ’™

UC Berkeley is committed to creating a learning environment that meets the needs of its diverse student body including students with disabilities.

  • If you anticipate or experience any barriers to learning in this course, please feel welcome to discuss your concerns with Josh, whether after class, in office hours, via Ed, or via email.

If you already have a Letter of Accommodation, please open a private Ed post ASAP and attach your LoA.

  • We can accommodate you more easily if you provide this information early in the semester.
  • We cannot guarantee that last-minute requests for accommodation will be provided.

If you have a disability, or think you may have a disability, you can work with the Disabled Students’ Program (DSP) to determine any accommodations you may need to have equal access in this course.

  • The Disabled Students’ Program (DSP) is the campus office responsible for authorizing disability-related academic accommodations, in cooperation with the students themselves and their instructors.
  • You can find more information about the DSP application process here.
  • Josh is available if you have any questions or concerns about your accommodations.
  • In the event of a disagreement, the proper procedure is for you to work with your DSP Specialist and your DSP Specialist to work with Josh toward a resolution.

Accessible DS education for all ⭐

In support of our commitment to making Data Science education inviting, engaging, and respectful for people of diverse identities, backgrounds, experiences, and perspectives, I want to relay the following three items from the Data Science Undergraduate Studies (DSUS):

Device Lending options

Students can access device lending options through the Student Technology Equity Program (STEP) program.

Data Science Student Climate

Data Science Undergraduate Studies faculty and staff are committed to creating a community where every person feels respected, included, and supported. We recognize that incidents may happen, sometimes unintentionally, that run counter to this goal. There are many things we can do to try to improve the climate for students, but we need to understand where the challenges lie. If you experience a remark, or disrespectful treatment, or if you feel you are being ignored, excluded or marginalized in a course or program-related activity, please speak up. Consider talking to your instructor, but you are also welcome to contact Executive Director Christina Teller at cpteller@berkeley.edu or report an incident anonymously through this online form.

Community Standards

Ed is a formal, academic space. Posts in this forum must relate to the course and be in alignment with Berkeley’s Principles of Community and the Berkeley Campus Code of Student Conduct. We expect all posts to demonstrate appropriate respect, consideration, and compassion for others. Please be friendly and thoughtful; our community draws from a wide spectrum of valuable experiences. Posts that violate these standards will be removed.