Syllabus
Table of contents
First things first
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. And please help create a welcoming environment for your fellow students. Thanks!
Device Lending options
Students can apply to borrow a computer through the Student Technology Equity Program (STEP) program.
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 right away. This includes informing data8@berkeley.edu, 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. The assignments are designed to prepare you for exams and for using this material on future projects.
When you need help, reach out to the course staff using Ed, in office hours, and/or during labs. You are not alone in Data 8! Instructors and staff are here to help you succeed.
Finally, know that it’s 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 don’t fully master everything covered, you can build on what you did learn, and oftentimes, things will just naturally click into place over time. Try to enjoy the journey, even when it’s challenging, and please talk to the course staff about your challenges.
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. 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 such as 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 some of its 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.
Support in learning the content
It’s 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 “Weekly Calendar & OH” 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 (“Ed”). 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 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.
Group tutoring
Once the semester gets going, 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 around five through a worksheet that covers past concepts. Details about sign-ups will be posted to Ed in the first few weeks of the semester.
Course Components
Live Lecture
Live lectures will be held on Mondays, Wednesdays, and Fridays starting at 11:10am 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.
If you are enrolled in the in-person lecture, you may come in person or join on Zoom. If you are enrolled in the online lecture, please join via Zoom at the start of the semester. If space opens up in the in-person lecture later in the semester, all students will be invited to attend.
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 problems you will see on your homework assignments! The discussion problems are often similar to exam problems. 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.
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.
Homework will be released on Thursday at 11am PT and due the following Wednesday at 11am PT. There are 5 extra points available for submitting on Tuesday at 11am PT (the day before the regular deadline)! Students will be awarded two homework drops for the entire semester, only meant to be used for extenuating circumstances. You can ask questions on Ed in the dedicated homework thread. (Make sure to navigate to the subquestion thread.) You can also get help at office hours. We highly recommend getting started on the homework shortly after it is released, 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 you’re logged into your Berkeley e-mail). 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 future real-world projects. 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. Don’t 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 don’t 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 encouraged to get help! 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 you’re ever feeling overwhelmed or don’t 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 you’re more likely to have a stress-free experience!
Submitting your Project with a Partner
Here’s a 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, likely during the week of March 8. As soon as the university schedules the exam, we’ll let you know.
The final exam is required for a passing grade, and will be held in-person on Tuesday, May 12, from 7-10pm PT.
There will be one alternate exam for the midterm, which is only for those with course conflicts.
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% |
Grades for Homeworks, Projects, and Labs will be posted on Pensieve about 1 week after the assignment’s 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.
Submitting Assignments
All assignments (homework, labs, and projects) will be submitted on Pensieve. Here’s a 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 students’ work beyond this point.
Late Submission
The deadline for homeworks in this course is 11 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 Berkeley’s 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 you’re 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.
Academic Misconduct
It is important to keep in mind the limits of collaboration. 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, you can share everything related to the lab with the other members of your discussion group. 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 may not use answers that you find online or that are generated by AI. Relying on AI or online solutions to solve assignments is a bad idea, both because you may incur academic misconduct penalties, and because you will not be prepared for exams.
Students who are involved in academic misconduct on an exam will receive a failing grade in the course.
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!
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