Syllabus
Table of contents
First things first
A message from DSUS
In support of our commitment to making Data Science education inviting, engaging, and respectful for people of diverse identities, backgrounds, experiences, and perspectives, I am relaying this message from 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)
Ed is a formal, academic space. Posts in this forum must relate to the course and be in alignment with Berkeleyâs 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.
Course climate (a message from me)
Learning is cooperative
We encourage you to discuss course content with your friends and classmates while working on your assignments. No matter your academic background, you will learn more if you work alongside others than if you work alone. Ask questions, answer questions, and share ideas liberally. If an emergency takes you away from the course for an extended period, or if you decide to drop the course for any reason, let us know immediately. This includes informing the instructor, your lab TA and your project partner.
Some words of encouragement and perspective
Make a strong effort on every assignment! If you get stuck, read the textbook and go over the lectures and lab discussion.
When you need help, reach out to the course staff using Ed, in office hours, and/or during labs. Here, you can discuss any doubts that you have. You are not alone in Data 8! Instructors and staff are here to help you succeed. We expect that you will work with integrity and respect for other members of the class, just as the course staff will work with integrity and respect for you.
Finally, know that itâs normal to struggle. Berkeley has high standards, which is one of the reasons its degrees are valued. Everyone struggles, even though many try not to show it. Even if you donât fully master everything covered, you can build on what you did learn, and oftentimes, things will just naturally click into place over time. You arenât expected to be perfect! Itâs OK not to get an A! I certainly didnât get Aâs in everything.
We are here for you
It can be very tough to be a student at this school! There are applications to clubs and grade requirements to declare majors, which are two things I did not have to experience as an undergrad. Some of you are navigating other challenges, like being a parent or commuting long distances from home to campus. Iâve learned these things from my students during my time so far here teaching, and if you have any other things youâd like to share with me about your experiences or if you just need someone to talk to about your academic struggles or your future path, I can be there for you. The tutors and TAs might be an even better resource than myself for some topics, because they are students just like you. So feel free to have conversations with them as well. They can also tell you what being an Academic Student Employee (ASE) is like.
With regards to reports of sexual misconduct/violence/assault, you may speak with me (and with the course staff, for that matter), but know that we will need to report the discussion to the Title IX officer. This is detailed below.
As UC employees, I and the ASEs are âResponsible Employeesâ and are therefore required to report incidents of sexual violence, sexual harassment, or other conduct prohibited by University policy to the Title IX officer. We cannot keep reports of sexual harassment or sexual violence confidential, but the Title IX officer will consider requests for confidentiality. Note that there are confidential resources available to you through UCBâs PATH to Care Center, which serves survivors of sexual violence and sexual harassment; call their 24/7 Care Line at 510-643-2005.
Other helpful, campus resources
Ombuds Office for Students and Postdoctoral Appointees
Center for Educational Justice & Community Engagement
UHS Counseling and Psychological Services (CAPS)
Campus Academic Accommodations Hub
ASUC Student Advocateâs Office
ASUC Mental Health Resources Guide
About the Course
Course Description
Foundations of Data Science combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It also delves into social issues surrounding data analysis, such as privacy and study design.
Prerequisites
The curriculum and format are designed specifically for students who have not previously taken statistics or computer science courses. Students with some prior experience in either statistics or computing are welcome to enroll and often find that this course offers a new perspective that blends computational and inferential thinking. Students who have taken several statistics or computer science courses should instead take a more advanced course like Data 100.
Materials & Resources
Our primary text is an online book called Computational and Inferential Thinking: The Foundations of Data Science. This text was written for the course by previous instructors.
The computing platform for the course is hosted at data8.datahub.berkeley.edu. Students find it convenient to use their own computer for the course. If you do not have adequate access to a personal computer, you can reach out to the Student Technology Equity Program STEP program.
Ed Discussion Forum
For questions about course material outside of Office Hours, students will use our course discussion forum, Ed Discussion (âEdâ). Ed is a great resource for asking questions about course material and getting help from the professor, course staff, and other students.
Ed is a formal, academic space. Posts in this forum must relate to the course and be in alignment with Berkeleyâs 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.
Support in learning the content
You are not alone in this course; the staff and instructor are here to support you as you learn the material. Itâs expected that some aspects of the course will take time to master, and the best way to master challenging material is to ask questions.
Office hours
Visit the tab on this website (left sidebar) to see our weekly office hour schedule. Both the instructor and the staff will be hosting weekly office hours.
Ed discussion forum
For questions about course material outside of Office Hours, students will use our course discussion forum, Ed Discussion (âEdâ). Ed is a great resource for asking questions about course material and getting help from the professor, course staff, and other students. Ed is a formal, academic space.
Group tutoring
Starting in the second week of the semester, small-group tutoring sessions will be available for students wanting additional support. These sessions are meant primarily to develop confidence with concepts that have been taught in previous weeks. In these sessions, a tutor will guide students in groups of no more than five through a specialized tutor worksheet which covers past concepts. Details about sign-ups will be available during the first week of the semester.
Course Components
Live Lecture
Live lectures will be held on Mondays, Wednesdays, and Fridays starting at 10:10am in Wheeler 150. Recordings of these sessions will be provided, but students are highly encouraged to attend in real-time. Slides and lecture examples will be provided on the course website.
Labs
Weekly labs include a discussion worksheet covering recent material and a programming-based lab assignment that develops skills with computational and inferential concepts. The problems in lab are good preparation for similar (and harder) problems you will see on your homework assignments! Lab assignments will be released on Monday night each week.
This semester, we offer two lab formats for you to choose from: regular lab, and self-service lab. Both are designed to help students learn the course material equally well.
Choosing a Lab Format
First-year students and students without prior programming experience are strongly encouraged to choose the regular lab. Working on programming-based lab assignments in a small classroom with dedicated course staff available to help is a great way to learn, especially if this is your first exposure to coding! The regular lab format also has advantages for students who wish to practice materials in a discussion-based format, wish to work with others and wish to have a dedicated lab uGSI and tutors for immediate support during lab time.
The self-service lab is designed to appeal to students who learn well from large-format lectures, work independently at their own pace, and come to drop-in office hours when they need help. It also appeals to those with a background in computer programming.
Regular Lab
- The first hour focuses on a discussion worksheet and group problem-solving. We encourage no use of technology during this portion of the lab (all materials required will be available at the lab section).
- The second hour is dedicated to completing a programming-based assignment.
- 80% of lab credit will be attendance-based.
- The remaining 20% of credit will be awarded for submitting the programming-based assignment to Pensieve with all test cases passing. Discussion worksheets do not need to be submitted.
- Active participation in the discussion and lab is required to earn attendance credit.
- Lab sessions will not be webcast or recorded.
Self-Service Lab
Students in the self-service lab are not assigned an in-person discussion section. They must submit the weekly programming-based assignment to Pensieve by Friday, 5 pm PT. If you need in-person support, you are more than welcome to visit office hours! Lab assignments include automatic feedback, so completing the lab assignment in full guarantees you a perfect score. Please note that there will be no attendance credit for self service labs. Your lab score will be solely based on your test cases (i.e. if you pass 80% of test cases you will receive a score of 80% on that lab).
Each student will be automatically awarded two lab drops that will be applied at the end of the semester. Please note that these lab drops are meant to be used only in the event of illness, emergency, or other extenuating circumstances; the expectation is that students will complete all assignments to the best of their abilities.
Want to Switch your Lab Format?
Students may switch formats at two points throughout the semester (1) during the first week of lab sections (between the start of Lab 1 and the start of Lab 2) and (2) after the midterm scores have been released. Please note that students looking to switch from self service to regular lab following the midterm will only be able to do so if capacity in lab sections are available. Additionally, if at any point a student has chosen the self service format, their lab grade will be solely based on the completion of the lab notebook, regardless of if they are attending a regular lab (e.g. if a student switches into a regular lab section from self service post-midterm, their credit will still be awarded based on the percentage of test cases passed).
How Should You Submit The Programming-Based Assignment?
Here is a video for how to submit the programming assignment to Pensieve. Before posting about code errors, please check our debugging page! We have listed common errors and reasons why they might be coming up for you.
Asking for Help
You can ask us about lab assignments in office hours or on Ed in the dedicated lab thread for a given week! Remember to never post your code publicly; please make a private post if you have to post any code. We also encourage you to review the course policies concerning collaboration and academic honesty.
Homework
Weekly homework assignments are a required part of the course. You must complete and submit your homework independently, but you can discuss problems with other students and course staff. See the Learning Cooperatively section below.
Homework will be released on Thursday at 10am PT and due the following Wednesday at 10am PT. Submissions after the deadline will be accepted for 24 hours and will incur a 20% penalty. Any submissions later than 24 hours after the deadline will not be accepted. There are 5 extra points available for submitting on Tuesday at 10am PT (the day before the regular deadline)! Similar to the lab policy discussed above, students will be awarded two homework drops for the entire semester, only meant to be used for extenuating circumstances.
Remember, there are resources to help you with assignments, including your homeworks! You can ask questions on Ed in the dedicated homework thread (make sure to navigate to the subquestion thread). Remember to never post your code publicly; please make a private post if you have to post any code. We also encourage you to review the course policies concerning collaboration and academic honesty. You can also get help at office hours. We highly recommend getting started on the homework earlier, so that if you need help, you can attend OH before the deadline on Wednesday.
How to Submit Your Homework Assignment
Here is a link to a video on the assignment submission process (make sure youâre logged into your Berkeley e-mail). Unlike labs, there are separate tests we run in addition to the ones you see in your Notebook! In order to pass these, make sure your code solves the general problem at hand and not only a specific case of the problem. As a reminder, it is your responsibility to make sure the autograder tests results in the notebook match the autograder results on Pensieve after you submit. Check out our DataHub Guide if you run into any issues working on Jupyter Notebook before posting on Ed.
Projects
Data science is about analyzing real-world data sets, and so you will also complete three projects involving real data. The experience of solving the problems in this project will prepare you for exams (and life in a data scientist role). On each project, you may work with a single partner; your partner must be from the lab you enrolled in. Both partners will receive the same score and are equally responsible for the work submitted!! You may also work on your own.
There are three projects throughout the semester. For each project, you will submit in two parts: first, a checkpoint, and then, the completed project. Donât share your code with anybody but your partner. Similar to homeworks, there are 5 extra points available for submitting 24 hours in advance of the deadline. You are welcome to discuss questions with other students but donât share the answers. If someone (who is not your partner) asks you for the answer, you might demonstrate how you would solve a similar problem.
The projects can seem long and difficult, but you are not alone! Come to office hours, post on Ed, and talk to your classmates. If you want to ask about the details of your solution to a problem, make a private Ed post and the staff will respond. If youâre ever feeling overwhelmed or donât know how to make progress, email your TA or tutor for help. You can find contact information for the staff on the course website.
We highly recommend starting early, so on the day that the project is released, and doing each bit that you can with the material that you have been exposed to in lecture/lab up until that point. Continue in this fashion, working day-by-day, and youâre more likely to have a stress-free experience!
Submitting your Project with a Partner
You may reference this walkthrough video on how to add partners on Pensieve! Make sure only one of you submits the project.
Exams
The midterm exam will be held in-person on Friday, October 17 from 7-9pm PT.
The final exam is required for a passing grade, and will be held in-person on Monday, December 15, from 8-11am PT.
There will be one alternate exam for the midterm, which is only for those with exam conflicts. There will not be an alternate exam for the final exam.
Grades
Grades will be assigned using the following weighted components. Every assignment is weighted equally in its category. For example, there are 3 projects, so each project is worth (20 / 3)% = 6.6% of your grade.
| Activity | Grade |
|---|---|
| Lab Credit | 20% |
| Homeworks | 10% |
| Projects | 20% |
| Midterm | 20% |
| Final | 30% |
The instructor nor the TA will respond to any questions regarding grade bins or letter grades. Please consult Berkeleytime for historical distributions of grade bins!
Grades for Homeworks, Projects, and Labs will be posted on Pensieve about 1 week after the assignmentâs due date. Solutions to the assignment and common mistakes will also be posted on Ed. It is up to you to check the solutions and request a regrade request before the regrade deadline (typically 5 days after grade release). Regrade requests can be made on Pensieve. Any regrade request past the deadline will not be looked at; this is to enforce the same deadline across all students, so please do not delay reviewing your work.
For the midterm exam, there will be a regrade request submission window. Please review the solutions and common mistakes before submitting a regrade request. Requests where a rubric item was incorrectly selected or not selected will be reviewed, but any regrade requests that ask to change the rubric or for partial credit will be ignored.
Submitting Assignments
All assignments (homework, labs, and projects) will be submitted on Pensieve. Please refer to this tutorial for submitting assignments.
We understand that the submission process is new for many students taking the course. To account for this, we will do our best to accommodate submission-related issues (submitting to the wrong assignment, not saving files correctly, autograder timing out) up until the third week of the course. After the third week, it is your responsibility to confirm you have submitted your work correctly. We reserve the right to impose penalties for having to resubmit studentsâ work beyond this point.
Late Submission
The deadline for homeworks in this course is 10 AM PT. The deadline for labs and projects is 5 PM PT. Submissions after the deadline will be accepted for 24 hours and will incur a 20% penalty. Any submissions later than 24 hours after the deadline will not be accepted.
If you need an extension, instructions on how to request an assignment extension are in the following section.
Your two lowest homework scores and two lowest lab scores will be dropped in the calculation of your overall grade. If you have an ongoing situation that prevents you from completing course content, please contact the course instructor.
Assignment Extensions
We understand that life happens and want to provide you with the support you need. If you need to request an extension, please fill out this form. Submissions to the form will be visible only to the course instructors and select Lead TAs. extension requests need to be submitted at least 24 hours before the deadline to be considered. . Extensions requests are subject to more detailed review and may require a meeting with course staff or be denied. However, we will try to accommodate requests if they are reasonable and the new deadline does not extend past the solution release date.
Please read the entirety of the form and its instructions before/while submitting a request to reduce confusion.
We hope that this policy encourages you to be proactive in communicating difficulties in advance while also allowing flexibility in the case of unforeseen circumstances.
Accommodations
We will provide appropriate accommodations to all students enrolled in Berkeleyâs Disabled Students Program (DSP). To ensure that you receive the appropriate accommodations, have your DSP specialist submit a letter confirming your status and accommodations.
If youâre not enrolled in DSP, or are in the process of being onboarded by DSP, you may still be eligible for accommodations. We also aim to provide fair and appropriate accommodations to any students who, because of extenuating circumstances, may need them. Please reach out to data8@berkeley.edu in this case.
Privacy
All DSP and accommodations-related materials for this course are kept in a repository separate from the rest of the course materials that is visible only to the instructors and selected Lead GSIs.
For any DSP and accommodations-related communications, please reach out to data8@berkeley.edu and the DSP Lead will get back to you. This inbox will be visible to future members of course staff, so if you ever have a communication that you wish to remain private, let us know and we can delete the email exchange once the conversation is resolved.
Do Not Cheat
It is important to keep in mind the limits of collaboration. As noted above, you and your peers are encouraged to discuss course content and approaches to problem solving. But you cannot share your code or written answers with other students. The two exceptions to this rule: you can share everything related to a project with your project partner (if you have one), and, if you are attending a lab session and have a lab partner, you can share everything related to that lab with your lab partner. Otherwise, you must write your answers in your own words and use your own code, and you must not share your work.
In addition, you must solve problems using the resources made available in the course and only the resources made available in the course! This means that you are not permitted to turn in written or code answers on any assignment (including exams) that you have obtained from others, online sources or from prior experience that does not include what we have taught in the course. This also means that you are not permitted to submit written material or code created with any generative AI tools, including but not limited to ChatGPT. Any usage of external materials or materials not taught in Data 8 on homework, projects, or labs will result in an automatic 0 on the assignment, and a homework or lab drop cannot be applied on such assignments. Any usage of external materials or materials not taught in Data 8 on the exam will not be graded. In addition, posting course content such as homeworks, projects, and exams on any 3rd party websites or submitting your own answers on outside sites/forums is considered academic misconduct.
This list of prohibited behaviors is not exhaustive, and we reserve the right to punish other dishonest behaviors. We will report any academic dishonesty directly to the Center for Student Conduct, and you will immediately receive an F in the course. Please read Berkeleyâs Code of Conduct carefully.
Thanks for reading!
The main goal of the course is that you should learn and have a fantastic experience doing so. The other goal is, of course, to have fun. Welcome to Data C8!