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.


This course does not have any prerequisites beyond high-school algebra. The curriculum and format is 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 will find much of interest due to the innovative nature of the course. Students who have taken several statistics or computer science courses should instead take a more advanced course.

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 the course instructors. A complete PDF of the textbook can be found in the Student Materials Google Drive.

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, we have machines available for you; please contact your lab TA.


You are not alone in this course; the staff and instructors are here to support you as you learn the material. 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. For questions, use Ed. We will also hold in-person and virtual office hours for real-time discussions.

Your lab TA will be your main point of contact for all course related questions/grade clarifications. The TAs are here to support you so please lean on your lab TA if you need more support in the class or have any questions/concerns.

Small-group tutoring sessions will be available for students in need of additional support to develop confidence with core concepts. In past semesters, students who attended have found these sessions to be a great use of their time. Details about sign-ups will be available later in the term.


The rest of this page details the policies that will be enforced in the Spring 2022 offering of this course. These policies are subject to change until the beginning of the semester and throughout the remainder of the course, at the judgement of the course staff.

All times listed below are in Pacific Time (PT).

Live Lecture Sessions

Live lecture sessions will be held on Mondays, Wednesdays, and Fridays from 10am to 11am Pacific Time in Wheeler 150; students may also attend live lectures remotely using this Zoom link. These lectures will be used to highlight and review vital concepts of the course. Accompanying notebooks with examples will typically be provided to students. Recordings of these sessions will be provided, though students are highly encouraged to attend in real time.


This course has a 2-hour weekly lab. You are required to attend at least five of the fourteen total labs, as well as attend the first week of labs, as part of your final grade in the course. See the Grades section for a breakdown.

The weekly lab session has two components: a worksheet about recent material, and a lab assignment that develops skills with computational and inferential concepts. These lab assignments are a required part of the course and will be released on Monday nights.

Lab worksheets will be posted electronically, so it is recommended to bring a tablet to lab or use one of the following resources:

Lab sessions are not webcast.

To receive credit for the lab notebook, you must submit it by 5pm on the Friday the same week it was released.

Homework and Projects

Weekly homework assignments are a required part of the course. You must complete and submit your homework independently, but you are allowed to discuss problems with other students and course staff. See the "Learning Cooperatively" section below.

Homeworks will be released on Friday after lecture and due the following Thursday night. If you submit a homework or project 24 hours before the deadline (Wednesday night) or earlier, you will receive 5 bonus points on that assignment.

Data science is about analyzing real-world data sets, and so you will also complete three projects involving real data. 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.


The midterm exam will be held on Friday, March 11 from 7-9pm PT. Please note the date and time carefully.

The final exam is required for a passing grade, and will be held on Tuesday, May 10 from 3-6pm. Please double check your course schedule to make sure that you have no conflicting finals.

There will be one alternate exam for the midterm and the final exam, for those in alternate time zones or with conflicting exams. We will announce these alternate times before the drop deadline. If you cannot make either the regular or alternative times, please contact your lab TA immediately.

Both exams will be in-person, though you may request to take an online exam instead. Online exams will be proctored via Zoom.


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 (25/3)%=8.3% of your grade. For the Lab Attendance portion, if you attend 5 labs, you will receive the full 2%, and partial credit will be assigned if you attend less than 5 labs.

Activity Grade
Lab Notebooks 8%
Lab Attendance 2%
Homeworks 20%
Projects 25%
Midterm 15%
Final 30%

In past semesters of Data 8, more than 40% of the students received grades in the A+/A/A- range and more than 35% received grades in the B+/B/B- range.

As we progress through the semester, grade reports indicating your current standing in the course will be released along with more detailed grade bins.

Grades for Homeworks, Projects, and Labs will be posted 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 Gradescope. 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 in 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 Gradescope. Please refer to the following tutorials:

Late Submission

Late submissions of labs will not be accepted under any circumstances. The same goes for homeworks, unless you have relevant university accommodations filed with the DSP office.

Your two lowest homework scores and three 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 your lab TA.

Projects will be accepted up to 2 days (48 hours) late. Projects submitted fewer than 24 hours after the deadline will receive 2/3 credit, and projects submitted between 24 and 48 hours after the deadline will receive 1/3 credit. Projects submitted 48 hours or more after the deadline will receive no credit.

Learning Cooperatively

We encourage you to discuss course content with your friends and classmates as you are 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 some emergency takes you away from the course for an extended period, or if you decide to drop the course for any reason, please don't just disappear silently! You should inform your lab TA and your project partner (if you have one) immediately, so that nobody is expecting you to do something you can't finish.

Academic Honesty

You must write your answers in your own words, and you must not share your completed work. The exception to this rule is that you can share everything related to a project with your project partner (if you have one) and turn in one project between the two of you, 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.

Make a serious attempt at every assignment yourself. If you get stuck, read the textbook and go over the lectures and lab discussion. After that, go ahead and discuss any remaining doubts with others, especially the course staff. That way you will get the most out of the discussion. It is important to keep in mind the limits to collaboration. As noted above, you and your friends are encouraged to discuss course content and approaches to problem solving. But you are not allowed to share your code or answers with other students. Doing so is considered academic misconduct, and it doesn't help them either. It sets them up for trouble on upcoming assignments and on the exams.

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.

You are also not permitted to turn in answers or code that you have obtained from others. Not only does such copying count as academic misconduct, it circumvents the pedagogical goals of an assignment. You must solve problems with the resources made available in the course. You should never look at or have in your possession solutions from another student or another semester.

Please read Berkeley's Code of Conduct carefully. Penalties for academic misconduct in Data 8 are severe and include reporting to the Center for Student Conduct. They might also include a F in the course or even dismissal from the university. It's just not worth it!

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. We expect that you will work with integrity and with respect for other members of the class, just as the course staff will work with integrity and with respect for you.

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 learn everything that's being covered, you'll be able to build on what you do learn, whereas if you cheat you'll have nothing to build on. You aren't expected to be perfect; it's ok not to get an A.

A Parting Thought

The main goal of the course is that you should learn, and have a fantastic experience doing so. Please keep that goal in mind throughout the semester. Welcome to Data 8!