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.

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 discussion GSI.


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 Piazza. We will also hold virtual office hours for real-time discussions.

Your section 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 discussion 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 Summer 2021 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 Standard Time (PST).

Waitlisted Students

If you are on the waitlist, you must still do all coursework and complete labs and homework by the deadlines. We will not be offering extensions if you are admitted into the course later. So it is your responsibility to stay up to date on the assignments.

Unfortunately, doing all the coursework is not a guarantee of enrollment. You will only be enrolled if there is space in lecture. Enrollment for lecture will proceed by CalCentral.

Live Lecture Sessions

Live lecture sessions will be held on Mondays, Tuesdays, Wednesdays, and Thursdays from 10am to 11am Pacific time, and Friday's from 10am-12pm Pacific Time over Zoom. 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.


Bi-weekly sections are a required part of the course. The only section you can attend is the one in which you are enrolled in. You are required to attend the first lab on June 21st. We will release signups for sections before the first week of class.

The weekly section has two components: questions and discussion 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 and Saturday nights.

Section sessions are not webcast. The set of questions covered in lab will be posted; for the related discussion, please attend the session.

Labs will be released Saturday and Monday nights at 10pm. You can get credit for each lab assignment in one of two ways described below:

Homework and Projects

Bi-weekly homework assignments are a required part of the course. Each student must submit each homework independently, but is allowed to discuss problems with other students and course staff. See the "Learning Cooperatively" section below.

Data science is about analyzing real-world data sets, and so a series of projects involving real data are a required part of the course. On each project, you may work with a single partner; your partner must be from your assigned lab section.


The midterm exam will be held during the class period on Friday, 7/16 from 7-9pm (Tentative).

The final exam will be held on Thursday, 8/12 from 3-6pm (Tentative).

Unless you have accommodations as determined by the university and approved by the instructor, you must take the midterm and the final at the dates and times provided here. Please check your course schedule and make sure that you have no conflicts with these exams. If you have a conflict with the final exam with another course, let the instructors know and they will see what can be done.


In order to foster a collaborative environment, Data 8 is graded on a fixed scale.

The course is graded out of 300 points, with the following mappings from points to letter grades:

Grade Points Percent
A+ 285 95%
A 277.25 92.4%
A- 264.5 88.1%
B+ 258.25 86.1%
B 249 83%
B- 244 81.3%
C+ 234.5 78.16%
C 220 73.3%
C- 203.5 67.8%

Grades will not be rounded.

In the event that our distribution does not align with departmental guidelines, we may change the raw score boundaries, but they will probably not increase (i.e. it is possible to receive a higher grade than the mapping suggests, but unlikely to receive a lower one). Throughout the semester we will provide updates on the bins.

NOTE: These bins only apply for this semster, they are not relevant to any other semester of the class.

Grades will be assigned using the following weighted components.

Activity Grade
Lab 10% (30 points)
Homework 25% (75 points)
Projects 20% (60 points)
Midterm 15% (45 points)
Final 30% (90 points)
EPA 3% (see section below)

Every assignment is weighted equally in its category. For example, there are 2 projects, so each project is worth (20/2)%=10% of your grade.

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

Note: This is the first semester we are trying this grading system. It is subject to change at any point if we believe that it has not succeeded.


There are four categories in which you can earn EPA: Lecture attendance, Guest lecture attendance, Section participation, and Piazza participation. We will take the top three of your four categories to calculate your final EPA score.

TAs will reward students where credit is due. EPA scores are kept internal to the course staff (i.e. not disclosed to students), except we will release your attendance records at the end of the semester. Please do not inquire about your EPA score, it will not be posted.

If you attend 10/12 labs (zoom attendance), you are guaranteed at minimum a 2/3 in section participation. If you attend 80% of lectures (zoom attendance), you are guaranteed at minimum a 2/3 in lecture. We are still finalizing our guest lecture schedule, but a similar policy will be in place for that category. To maximize your score, ask questions, work with other students, etc.

This policy is inspired by: https://cs61c.org/su19/policies#epa.

EPA will be added to your points totals after any grade bin adjustments occur.

Submitting Assignments


Late Submission

Late submissions of labs will not be accepted under any circumstances. The same goes for homework and projects, unless you have relevant university accommodations. If you have such accommodations, please provide the formal notification to your lab GSI before the end of the second week of classes.

Your two lowest homework scores and your two lowest lab scores will be dropped in the calculation of your overall grade. There will be no alternate due dates for assignments missed due to illness, other commitments, and so on. The drops are intended to cover those situations.

Projects will be accepted up to 2 days (48 hours) late; a project submitted less than 24 hours after the deadline will receive 2/3 credit, a project submitted between 24 and 48 hours after the deadline will receive 1/3 credit, and a project 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 discussion 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 academically dishonest, and it doesn't help them either. It sets them up for trouble on upcoming assignments and on the midterm exam.

You are also not permitted to turn in answers or code that you have obtained from others. Not only is such copying dishonest, 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 cheating 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 Piazza, in office hours, and/or during live labs and discussions. 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.

Exam Proctoring

We are planning to use zoom proctoring for exams this semester. More information is available on this page

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!