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Syllabus

Table of Contents

• About the Course
• Prerequisites
• Course Components
  • Lectures and Textbook
  • Support (Discussion, OH)
  • Practice (Lab, HW)
  • Exams
    • Exam Format
• Grading Policies
  • Grading Scheme
  • Regrade Requests
  • Extenuating Circumstances
  • DSP Accommodations
  • LLM and Generative AI Policy
  • Alternatives to LLM’s
  • Collaboration and Academic Integrity
• Waitlist
• Community Resources

About the Course

This course develops the probabilistic foundations of inference in data science. It builds a comprehensive view of the decision-making and modeling life cycle in data science, including its human, social, and ethical implications. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, robustness, bandit algorithms, fairness in classification, and an introduction to machine learning tools including decision trees, neural networks and ensemble methods.

This class is listed as Data 102.

Prerequisites

We currently require the following (or equivalent) prerequisites:

1. Principles and Techniques of Data Science: Data 100 covers important computational and statistical skills that will be necessary for Data 102.

2. Probability: Data 140, EECS 126, STAT 134, IEOR 172, or Math 106. Data 140 and EECS 126 are preferred. These courses cover the probabilistic tools that will form the underpinning for the concepts covered in Data 102.

3. Math: Math 54, Math 56, Math 110, both EE 16A and EE 16B, STAT 89a, or Physics 89. We will need some basic concepts like linear operators, eigenvectors, derivatives, and integrals to enable statistical inference and derive new prediction algorithms.

Please consult the Resources page for additional resources for reviewing prerequisite material.

Course Components

Lectures and Textbook

Lectures will be held in-person Tuesdays and Thursdays from 12:30 - 2 PM in the Gateway, Room 1210, and the companion to the lectures is the Data 102 textbook. Here’s what you need to know about the relationship between the two:

• Both cover the same material, but sometimes will provide complementary perspectives.
• We will sometimes direct you to reading outside the textbook. These external sources have been selected to expose you to professional writing on the topics surveyed in the textbook.
• Each lecture page will contain a listing of the corresponding textbook sections or relevant external sources.
• Lectures will be interactive, with several discussion questions and understanding checks that give you an opportunity to talk with your fellow students and solidify your understanding.
• Most textbook sections have videos included with them, which are similar to, but not the same as, the corresponding lecture content.
• Lecture recordings will not be made available (except as specified in arrangement with the DSP office). You may request lecture recordings through the lecture recording request form. All students will be granted two recordings without explanation. Further recording requests must be justified. For all policies, check the form.

The most common approach that students find helpful are to attend lecture and read the corresponding textbook sections, in whichever order you find most helpful to your own learning. Some students are able to follow all the material using only one or the other, but this is less common as a pathway to success.

Laptop use during lecture is discouraged. If you have circumstances that require or would benefit from the use of a laptop to support your learning during class (including but not limited to accommodations), you should fill out the exemption form.

Support (Discussion, OH)

Discussion

Discussion section will be held on Wednesdays, led by your GSIs. These sections will cover important problem-solving skills that bridge the concepts in lectures with the skills you’ll need to apply the ideas on the homework and beyond. Each week, discussion worksheets will be posted on Wednesday. Answers (without full explanations) will be posted on Friday after the submission period closes.

There will be discussion sections held almost every week, excluding week 1, exam weeks and the Wednesday before Thanksgiving.

Discussion attendance will be opt-in mandatory: this means that you have the option at the start of the semester (and again at week 7) to choose between one of two options:

• Option 1 (discussion attendance): if you choose this option, you are committing to attend discussion section every week, and work with a group of 3 of your peers (i.e., groups of 4) on discussion questions. If you attend and participate, then you will get full credit for the day. Note, you will not get credit for attending and working by yourself, or waiting for the TAs to provide solutions.

• Option 2 (no attendance): if you choose this option, you are choosing to not attend discussion section. You will be expected to submit the worksheets directly to Pensieve as you would a homework assignment (scanned PDF) by Friday at noon. Submitted worksheets will be graded for completion. When submitting discussions on Gradescope/Pensieve, you must match each page to the corresponding question on Gradescope/Pensieve. If you fail to do so, you may not receive credit for your work! If you switch to option 1 after the first midterm, then your remaining discussion grades will be based on participation. We will not accept late submissions.

Regardless the option you choose, we will drop your lowest two discussion scores. If you need to miss additional discussions for a reason outside your control, or cannot submit on time, submit the discussion excusal form.

Choose the option that will ultimately best suit your learning. In other data courses that have similar policies, choosing Option 1 is positively correlated with earning a higher grade (despite the additional attendance requirement). Complete this discussion opt-in and scheduling form to select one of the two options and so that course staff can assign students to sections.

You will have the option to switch between Option 1 and Option 2 after Midterm 1 grades have been returned, in week 7.

TA Office Hours

We will run office hours in three separate formats:

1. Tutoring (sign up only, groups of up to 6). Use this tutoring sign up form to register.

2. Drop-In (in-person). In the Gateway building, B1040

3. Drop-In (remote). See post and calendar for zoom link (to come).

See Ed, or the course calendar, for further details.

Professor Office Hours

Professor Strang will host OH in the Gateway building, room B1040 D, from 2:30 - 4:00 pm on Thursdays. These are reservation only (up to groups of 5). Use this booking link to reserve a spot.

Practice (Lab, HW)

This semester we will not grade any take-home work. We will still release labs and problem sets as practice. We strongly encourage you to work through these genuinely, and to come into tutoring, OH, or lab sessions to get help. The best way to learn a subject is to take responsibility for your own intellectual engagement with problems. Make study groups, budget time to work on the problems, and make sure you track your own engagement with the course.

Lab

Labs will be released most Friday evening as Jupyter notebooks. Labs are a chance to get hands-on practice with the material in a more guided setting. Lab notebooks usually cover material from the past week’s lecture, and give you a chance to implement and code up the more abstract ideas from lecture.

Lab-specific office hours will be held on Mondays by GSIs. These provide a good opportunity to work on lab assignments with your GSI. You may use these as a drop-in session to get help on specific questions, or as a section to work through the lab while in the room. During these office hours, questions on the lab assignment will be prioritized over any other questions, and other office hours will prioritize non-lab questions.

If lab sections are under-attended we may reduce the number of hours offered.

Problem Sets (Homework)

Problem sets will be released every other week on Fridays. These assignments are designed to help students develop an in-depth understanding of both the theoretical and practical aspects of ideas presented in lectures. They contain both math and coding tasks, as well as critical reflection questions that require you to explain your answers and put them into context.

Exams

There will be two midterms in this class and a final exam:

• Midterm I on September 28th, 8-10PM
• Midterm II on November 2nd, 8-10PM
• Final exam on Friday, December 18th, 8-11AM

All exams must be taken in-person. You must sit the midterms at the specified time: if you have a conflict, please contact course staff ASAP. Please use the exam conflict/clobber request form available through our form glossary. We will not accept any foreseeable conflicts after the drop deadline.

Exam Format

All exams will follow a regular format. The exams will include two parts:

1. Concepts and Definitions: This part will ask you to complete concept question (T/F, M/C, fill-in-the-blank) or to provide, from scratch, key definitions or results from the course. Relevant results and definitions will be clearly highlighted in lecture and provided when we release the exam scope. Make sure you memorize these before you study anything else.

2. Practice Problems: To ensure that your practice problems are relevant, and that you know what to prepare for the exam, the last two thirds of each exam will consist exclusively of problems drawn verbatim (allowing new numbers or copy-edits for concision) from homework or lab. We will provide at most 4 problems on midterms, and ask you to choose 2 to complete. Since the final is a comprehensive exam, we will provide at most 7 problems and ask you to complete 4.

If you complete your practice thoroughly, you will be prepared for the exams.

Grading Policies

Grading Scheme

Grades will be assigned using the following weighted components:

Category	Percentage	Details
Discussion	10%	Drop 2 absences
Lower Midterm Score	15%
Higher Midterm Score	35%
Final Exam	40%

Regrade Requests

• After each assignment is graded, course staff will post the deadline for regrade requests for that assignment on Ed.
• To ensure that our grading team is not overworked, regrade requests for each assignment must be submitted before the deadline (except in cases of emergencies).

Extenuating Circumstances

We recognize that our students come from varied backgrounds and have widely-varying experiences. If you encounter extenuating circumstances at any time in the semester, please do not hesitate to let us know. Navigate to our form glossary and select the appropriate form.

If no form matches your need, please email us at data102@berkeley.edu. Within two business days, a member of course staff will reach out to you and provide a space for conversation, as well as to arrange course/grading accommodations as necessary.

In general, the sooner we are made aware, the more options we have available to us to help you.

We recognize that at times, it can be difficult to manage your course performance — particularly in such a huge course, and particularly at Berkeley’s high standards. Sometimes emergencies just come up (personal health emergency, family emergency, etc.). This policy is meant to lower the barrier to reaching out to us, as well as build your independence in managing your academic career long-term. Please do not hesitate to reach out.

Note that extenuating circumstances do not extend to the following:

• Logistical oversight, such as Datahub/Gradescope/Pensieve tests not passing, submitting only one portion of the homework, forgetting to save your notebook before exporting, submitting to the wrong assignment portal, or not properly tagging pages on Gradescope/Pensieve. It is the student’s responsibility to identify and resolve these issues in advance of the deadlines.
• Workload-related issues. It is the student’s responsibility to manage their other coursework and extracurricular commitments. We will not grant accommodations for these cases; instead, please use drops to cushion these issues.
• Requests made after the assignment or evaluation deadlines. Please make sure to submit a request before the assignment is due.

Finally, simply submitting a request does not guarantee you will receive your requested exception.

DSP Accommodations

If you are registered with the Disabled Students’ Program (DSP) you can expect to receive an email from us during the first week of classes confirming your accommodations. Otherwise, email data102@berkeley.edu. DSP students who receive approved assignment accommodations will have a 2-day extension on discussion submission.

You are responsible for reasonable communication with course staff. If you make a request close to the deadline, we can not guarantee that you will receive a response before the deadline.

LLM and Generative AI Policy

You are responsible for how you engage with the practice provided. You are welcome to use tools like ChatGPT, Gemini, Claude, etc. However, do not use them as a crutch. If you do, you will not be prepared for your exams. Make sure that you develop your ability to answer problems independently.

Alternatives to LLM’s

Problem sets are hard. They should be. College is meant to challenge you. So, don’t be embarassed or discouraged if they are difficult. Completing problem sets genuinely requires grit and deliberation. Like all hard things, it gets easier the more you work at it. Like physical exercise, or a good puzzle, chewing on a problem set can be fun, even if it is hard.

So, start early. Since we won’t grade take-home work, don’t plan around deadlines. Direct your own schedule. Work with friends. Read relevant resources. Memorize the essential ideas. Make plans that keep you honest to your work.

Then, use your resources. You have access to 7 TA’s, lab OH, tutoring sessions, drop-in OH, professor OH, discussion sections, and Ed with a professional course staff who are eager to help you. Come work with us. It is both our job, and our genuine pleasure, to help you, wherever you are at.

Collaboration and Academic Integrity

We will be following the campus policy on Academic Honesty, so be sure you are familiar with it.

Waitlist

If you are on the waitlist, you should complete and submit all discussions as if enrolled.

For all other enrollment related issues, please reach out to the Data Science advisors, as instructors and staff do not manage enrollment into the class.

Community Resources

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. We must demonstrate appropriate respect, consideration, and compassion for others. Please be friendly and thoughtful; our community draws from a wide spectrum of valuable experiences. For further reading, please reference Berkeley’s Principles of Community and the Berkeley Campus Code of Student Conduct.