Syllabus
Logistics
Class schedule (in person)
| Number | Time | Location | |
|---|---|---|---|
| Lecture | MWF 11:00A-11:59A | Hearst Field Annex A1 | |
| Section | 101 | Tu 9-11am | Etcheverry 3105 |
| 102 | Tu 10-12pm | Hearst Field Annex B1 | |
| 103 | Tu 12-2pm | Social Sciences Building 185 | |
| 104 | Tu 2-4pm | Hearst Gym 242 | |
| 105 | Tu 2-4pm | Hearst Mining 310 | |
| 106 | Tu 4-6pm | Moffitt Library 106 |
Staff
| Position | Name |
|---|---|
| Instructor | Elizabeth Purdom |
| GSIs | Florica Constantine |
| Chuao Dong | |
| Chloe Shen |
(to see email, office hours and other information, click on the person’s name)
Lectures
Lectures will cover core theory and concepts, with supporting data analysis examples. To get the full benefit of lecture, it is best to read the supporting material ahead of time. When slides or R code are shown in class, they will be posted online after class. However, lectures will not always have associated slides – I usually will write on the board/ipad. I will not be posting recordings of the lectures, but if you have to miss class due to an emergency or unavoidable circumstance, the class notes and text should suffice. Please do come and see me so we can review what you have missed.
Lectures and sections cannot be recorded, whether video or audio-only. This includes use of smart glasses, phones, etc. (Note, that UC policy prohibits distributing any recordings of classes without the instructor’s explicit permission.)
Sections
The sections meet once a week, on Tuesdays, for two hours. The individual section times will be listed in the course calendar. Section time will be spent working on practice problems and additional examples. Since some problems will involve computing you should plan to always bring your laptop. They will not be recorded.
Office Hours
Prof. Purdom and one of the GSIs will hold individual office hours each week. In addition, there will be group office hours (“homework parties”), the room and hours TBA. The group office hours are intended for people to show up and just do homework together, whether having a question or not. You can to any of these sessions to get questions answered about homework or any concepts discussed in class, or help with your coding assignments.
Stat Scholars Program
The new Stat Scholars Program will be available to Stat 135 students to provide community, academic support, and more such as answers to your questions about the stat major, new directions in statistics, research being done in the stat department etc. Applications will be sent out soon, begin checking your mail next week.
Email/Communication policy
If you have a question for us, the fastest way to get a response is to post the question on Ed. If it deals with private matters, please make it a private post. If you are not comfortable writing a private post, come see one of the staff in person, either to office hours or make an appointment.
Assessments
Homework Assignments
Homework assignments will be due roughly every week or so during the semester, starting in the second week. Homework will be posted to Gradescope usually by Friday, and will generally be due the following Friday at 11:59 pm. Homework will be a combination of analytical exercises done “by hand” and data analysis using R.
Homework will be graded for completeness/effort, not for accuracy.
Quizzes
There will be four 50-minute quizzes to test your understanding of homework and lecture. We will drop your lowest quiz score.
You will take the quizzes in the CBTF (Computer-Based Testing Facility) in Moffit Library. The dates of the quizzes are:
| Quiz | Dates |
|---|---|
| Quiz 1 | Sep 14 - Sep 17 |
| Quiz 2 | Sep 28 - Oct 1 |
| Quiz 3 | Oct 26 - Oct 29 |
| Quiz 4 | Nov 16 - Nov 19 |
You will have to schedule your quiz during the given dates at the CBTF. We do not schedule your quiz for you. Note that DSP students will also take it in the CBTF and will get their extended time and other accommodations. See more details here
Students are responsible for reading and following all CBTF policies and procedures, including instantiating a PrairieTest account, submitting any DSP accommodations in advance, and reserving and attending quiz sessions on time. During CBTF exams, students may use only the materials and resources expressly authorized by the course; unauthorized devices, materials, or resources are prohibited.
Exams
There will be one midterm exam and the final exam.
- The midterm exam will be a two hour exam which is tentatively scheduled to be held in the evening from 7-9 pm on Thursday, October 15 (Location TBD).
- The final exam time and day are Monday, December 14, 11:30 am - 2:30 pm. If you do not take the final exam, you will not pass the class.
Attendance
There will be attendance quizzes handed out in lecture. These will be graded on completion, and you will need to complete five from lecture to get all the attendance points for lecture.
Attendence will be taken in section starting in Week 3 (September 8th), and you need to attend 9 out of 13 weeks to get all attendance points for section.
Late Assignments
Homework You can automatically receive extensions for 3 days for up to 2 homework assignments by submitting a request at this page once assignments have been posted on Gradescope. You must make the request before the due date.
Quizzes You will self-schedule a time to take your quizzes in a three day window. There is guaranteed space for all students somewhere in that window, but no specific time is guaranteed so you should reserve your time well in advance, particularly if you know you will have conflicts on certain days. (Students from other classes will also be taking their exams during these time periods in the same space). You can, however, cancel your time and reserve another time up until the start of your scheduled time if something comes up closer to the time.
This policy is intended to handle most standard needs, and students are expected to use their extensions wisely and plan ahead for scheduling their quizzes. If you have DSP accommodations or need more extensive extensions due to extraordinary circumstances, please send us a private message on Ed.
Overall Score
Your letter grade for the course will be based a weighted average of your assessments, as follows:
- Homework (each assignment weighted equally): 10%
- Quizzes: 20% (equally weighted, drop lowest)
- Midterm: 30% (can be replaced by the final if higher)
- Final exam: 35%
- Attendance: 5%
Guaranteed grade bins: Students earning an overall score of at least 90% are guaranteed to receive an A- or better, students earning an overall score of at least 80% are guaranteed a B- or better, and students earning an overall score of at least 70% are guaranteed at least a C-. These are guidelines to help you be able to concretely monitor how you are doing. However, I do curve grades, meaning I may move these cutoffs down, based on how the rest of the class does, but they will not go up. We will also report summary statistics, like median/mean/upper and lower quartiles, so you can see how you are doing relative to the rest of the class.
Curriculum
Prerequisites
- STAT 134 or an equivalent course in probability theory. Do NOT take 134 and 135 concurrently!!
- Multivariable calculus, especially Lagrange multipliers.
- Familiarity with basic R concepts equivalent to the first ~6 weeks of Stat 133. Note that assignments involving computing must be completed in R.
- Familiarity with linear algebra (matrix operations, inverses, and eigenvalues).
There is a diagnostic test to assess your familiarity with probability concepts. The first homework will also include basic probability questions. If the material on these two assignments is unfamiliar to you, you should not be taking this course.
What we will cover
Below is a rough guide to what we will cover this semester, but this may change as the semester progresses.
| Weeks | Topic |
|---|---|
| 1-2 | Introduction to modeling data |
| Case Study: Simple Regression | |
| 3-4 | Estimation: MLE and Beyond |
| Sampling Distribution | |
| 5-6 | Finite properties of Estimators |
| Confidence Intervals | |
| Case Study: Two group comparisons | |
| 7-9 | Beyond Finite sampling distributions: |
| Asymptotic Distributions | |
| Bootstrap | |
| 10 | Case Study: Multiple Regression |
| 11-12 | Prediction and Classification |
| Case Study: Logistic Regression | |
| 13-14 | Hypothesis Testing |
Textbooks
Official textbooks for the course:
- Foundations of Statistics for Data Scientists by Alan Agresti and Maria Kateri: This is the main text that we will follow. It does a good job of laying out the main subjects and gives a modern treatment of the area, but is light in detail.
- Mathematical Statistics and Data Analysis (3rd Edition), by John Rice: This is the classical book for this course, and has very good, detailed exposition of the subjects it covers as well as many exercises. However, it has a very classical perspective, and we will be covering topics this semester not in this book or with a different emphasis as to the relative importance. There are many places online where you can find a pdf of the book, and I will assign reading out of this textbook to supplement the main text.
Other useful texts:
- R for Data Science, by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund
Important sites to bookmark
R Studio / R Datahub
All coding in this class will be done in R. You may download the desktop version of RStudio if you wish, but you can also use the datahub. It is up to you. Just make sure you have the latest version of R and RStudio, if you would like to download it to your laptop.
Bcourses
Important coursewide announcements will be sent out on bcourses.
Ed Discussion
I have created a Ed discussion page for this course, which you can access through the link in bCourses (so you will be logged in). This is an online forum to ask questions to fellow students and answer other students’ questions. The GSIs and I will have access to the forum and may endorse or occasionally answer questions but this is primarily a forum for students to help each other – if you need an instructor or GSI’s assistance please attend office hours. Extra credit of up to 0.5% on the final course score will be awarded to the five students who have responded to the most questions on Ed by the end of the semester (with reasonable attempts at answering the questions).
Gradescope
Homework assignments, and regrade requests will be submitted through Gradescope, which you can also access through the link in bCourses (you should not add yourself to Gradescope, as the roster has already been uploaded). You should view your grades here, not bCourses.
Flextensions
This is the online platform for requesting extensions for homeworks and other assignments. Everyone has automatic extensions for homeworks, as described above. Beyond those, extensions will only be granted in extraordinary circumstances.
Academic Honesty Policy
The student community at UC Berkeley has adopted the following Honor Code: “As a member of the UC Berkeley community, I act with honesty, integrity, and respect for others.”.
My expectation is that you will adhere to this code. Beyond the importance of respecting your fellow students, acting with integrity in completing course assignments helps ensure that they achieve their purpose, which is to help you learn and develop valuable statistical understanding and skills. Homework must be done independently. If you get stuck or want to explore alternative approaches, feel free to discuss issues with students or course staff at the homework parties or on Ed.
You may ask an AI tool for help understanding a concept, or figuring out a coding error (but note that the answers are not always correct), but putting assignment questions into an AI tool and then copying and pasting/paraphrasing the answers will be considered plagiarism. It is also completely negates the purpose of the homework, which is to cement and internalize concepts from the course. It will not serve you for the exam either, when you have to tackle the problems on your own. I leave it to you to decide how much you would like to use genAI tools, but please use them with caution.
CBTF proctors monitor all sessions and will document and report any suspected academic misconduct to the course staff. Suspected violations will be handled in accordance with the Berkeley Honor Code and the UC Berkeley Code of Student Conduct, and may result in penalties up to and including a failing grade on the exam or in the course and referral to the Center for Student Conduct.
Inclusivity and Accommodation
If you need accommodations for any physical, psychological, or learning disability, please speak to me after class or during office hours. Please note that you must make arrangements in a timely manner through DSP so that I can make the appropriate accommodations.
Possibility of revisions to course policies
All course policies, including assessment, are subject to change during the course of the semester in response to unforeseen events including, but not limited to, campus shutdowns due to various reasons, power outages, forest fires, and medical emergencies among members of the course staff.