Syllabus

First things first
About the Course
1. Course Description
2. Prerequisites
Course Components
Materials & Resources
Grades
Thanks for reading!
1. Other helpful, campus resources

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.

Course Components

Lecture

In-person

Live lectures will be held daily starting at 11:00am in Physics 1 and last one hour. On Friday, the lecture will continue for a second hour with a break in between. Recordings of these sessions will be provided, but students are highly encouraged to attend in real-time. Slides and lecture examples (demos) will be provided on the course website.

Online

Online lectures will be held daily starting at 2:00pm on Zoom and last one hour. On Friday, the lecture will continue for a second hour with a break in between. If you miss the Zoom session, you are welcome to watch the recording of the in-person session. Slides and lecture examples (demos) will be provided on the course website.

Labs

Lab sessions are biweekly and are each two hours long. They consist of a discussion worksheet covering recent material (first hour) and a programming-based lab assignment that develops skills with computational and inferential concepts (second hour). There will typically be two lab assignments to complete per week, one per session, that will be due the night of the particular session. Check the front page of the course website for the specifics on any given week. Lab sessions will not be recorded for either the in-person or online sections.

In-person

Labs will be on Monday and Wednesday afternoons following lecture. You will have a chance to sign up for your exact time slots.

80% of lab credit will be based on attendance. The remaining 20% of credit will be awarded for submitting the programming-based assignment to Pensive. Your lab score will be solely based on your test cases (i.e. if you pass 100% of test cases you will receive the full 20% for this portion).

Online

Labs will be either:

Tuesday and Thursday afternoons from 4-6pm. Wednesday and Friday afternoons from 4-6pm.

You will have a chance to sign up for your exact time slots.

90% of lab credit will be based on attendance. The remaining 10% of credit will be awarded for submitting the programming-based assignment to Pensive. Your lab score will be solely based on your test case score (i.e. if you pass 100% of test cases you will receive the full 10% for this portion).

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.

Homework

Biweekly homework assignments are longer-form versions of the assignments you receive during lab and feature both written and programming tasks. You must complete and submit your homework independently, but you can discuss problems with other students and course staff. Homework assignments are typically released each Tuesday and Friday at 11am PT, with deadlines set for the subsequent Friday and following Tuesday at 11am PT, respectively. Check the front page of the course website for the specifics on any given week.

How Should You 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 Pensive 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 two 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 section you enrolled in (whether online or in-person). Both partners will receive the same score and are equally responsible for the work submitted. You may also work on your own.

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. You are welcome to discuss questions with other students but don’t share the answers.

Asking for Help

The projects can seem long and difficult, but please don’t hesitate to ask for 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 (or on your own)

Here’s a walkthrough video on how to add partners on Pensive! Make sure only one of you submits the project.

Exams

All exams, regardless of section, will be held in-person. In-person exams are vastly more efficient for students and for staff when it comes to the administration and the distribution of grades in a timely manner. If you are in the online section and absolutely cannot attend the in-person exams, you will be asked to provide supporting documentation multiple times during the semester and must take a remote exam. In order to preserve the integrity of the test for all students, the remote exam requires a rigorous setup process on the part of the student. This process must be strictly adhered to, and any violation of the process, to any degree, will result in an automatic 0 on the exam.

The midterm exam will be held in-person on Friday, August 17, from 11am-1pm PT.

The final exam is required for a passing grade, and will be held in-person on Friday, August 14 from 11am-2pm PT.

Materials & Resources

Course website

The course website, which you are on right now, is the most important resource you’ll have. It contains the reading, lecture and assignment calendar (which contains links to your assignments), the office hour calendar, the syllabus, study materials for your exams, and more.

Textbook

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.

Computing platform

The computing platform for the course is hosted at data8.datahub.berkeley.edu. Students typically 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.

Discussion forum

The EdStem discussion forum (Ed) is the primary place to ask the instructor, staff and other students questions about course content and logistics.

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.

If you have a general question about concepts or logistics, keep your post public so that other students can benefit from the answer (or perhaps, another student can answer your question). If you want to ask about the specific details of your solution to a problem in the course (homework, project, lab) make a private Ed post and the staff will respond. You may also use the private feature if you need help navigating a personal issue. 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.

Pensive

Pensive is the platform on which you’ll turn in your assignments (projects, labs, homeworks) this semester. Once you login with your Berkeley email, you should be able to navigate to our course portal.

Office hours

As mentioned earlier, viisit 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.

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.

Grades

Grades will be assigned using the following weighted components. Every assignment is weighted equally in its category. For example, there are 2 projects, so, for the in-person section, each project is worth (20 / 2)% = 10% of your grade. The instructor nor the TAs will respond to questions about grade bins. Please consult Berkeleytime for historical grade distributions.

In-person

Activity	Grade
Lab Credit	20%
Homework	10%
Projects	20%
Midterm	20%
Final	30%

Online

Activity	Grade
Lab Credit	15%
Homework	10%
Projects	15%
Midterm	25%
Final	35%

Regrades

Grades for Homeworks, Projects, and Labs will be posted on Pensive. 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 Pensive. 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 Pensive. 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.

Submission Deadlines

All homeworks, labs, and projects in this course is 11:59 PM PT will be due at 11:59 PM PT on their respective due dates.

Early Submission Bonus

For homeworks and projects, you will receive an additional 5 points if you submit 24 hours or more ahead of the due date.

Late Submissions

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 on time, 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 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 select TAs.

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. If you answer programming questions on assignments using code that has not been taught in the course, you will receive an automatic zero on the assignment in question.

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!