FAQs - Courses and Program

This page provides updated FAQs about individual Data Science courses (such as Data 8, connectors, and Data 100) and more information about the program as a whole. It is current as of January 2018.

A separate page covers FAQs for the proposed Data Science major and minor.

To ask questions and see more answers about the proposed Data Science major and minor and other aspects of the Data Science program, please check out: piazza.com/berkeley/other/data001.

Data 8 (COMPSCI / INFO / STAT C8, Foundations of Data Science)

Is Data 8 a computer science class, a statistics class, or something else?

  • Both, and more. Data 8 lets students get a fundamental understanding of key computational and statistical concepts and practices by working hands-on with real data. It weaves together these disciplines and their implications in a powerful and interesting way. More of the topics come from statistics than from computer science. Some of these topics are quite different from what appears in a typical introductory course from either discipline.

Who takes Data 8? Who does well in it?

  • Students from a wide range of majors in every College. It’s not just for “technical” or “non-technical” people.

Does Data 8 have prerequisites?

  • No. The course is designed for students who are new to statistics and programming, although students with experience in one or the other definitely still say they learn a lot.

Does Data 8 depend on any previous background in statistics?

  • No. Data 8 teaches statistics differently from most high school or college courses.

Does Data 8 depend on any previous experience with computer programming?

  • No. About half the students in Data 8 typically say they have no or very limited programming skills.

What programming language is Data 8 taught in?

  • Python.

If I want more background in programming, can I take another course as well?

  • Yes, you can also take CS 10, CS 61A, or programming courses elsewhere or online. We’re sometimes able to offer a summer course CS/STAT C8R that may help students who want additional time and practice before Data 8.

Who designed Data 8? Who teaches it?

  • Data 8 was designed by a multidisciplinary team of Berkeley faculty. It is taught by different faculty in different semesters.

How can I learn more about Data 8?

  • An in-depth introduction to Data 8 is here. Or just visit data8.org. All the course materials are online.

Data 8 Logistics

Is there any difference between COMPSCI C8, INFO C8, and STAT C8?

  • No, they’re all the same class.

All three classes show up in Cal Central. Which one should I enroll in?

  • All the seats in each semester are associated with one department. Enroll in whichever one has seats that semester. (Fall 2017: COMPSCI C8, CCN 42193; Spring 2018: STAT C8, CCN 31678; Summer 2018: STAT C8, CCN 15968, Fall 2018: COMPSCI C8, CCN 27696)

How many seats are available in Data 8?

  • The class is now a regular offering. We aim to offer it every semester, and we are trying to meet student demand. For the first time in Spring 2018, we may have to limit the course to ~1000 students. We hope to be able to offer more seats in Fall 2018.

If there are seats in the Data 8 lecture, but no lab sections open for enrollment, how do I enroll in a lab section?

  • In some semesters (such as Fall 2017), lab section enrollment is decided at the start of the semester. In those semesters, there will be a 999 section. In that case, sign up for the 999 section.

Are seats reserved for particular categories of students?

  • Freshmen and sophomores have priority to start. As soon all undergraduates have had their Phase II appointments, no seats will continue to be held for particular categories of students, and the waitlist will be processed in order.

I'm interested in doing the EdX course, Data 8X, online. Does this count to satisfy Data 8?

  • No, unfortunately Data 8X doesn't have all the content of Data 8, and does not transfer academic credit.

Can graduate students enroll?

  • Yes, they can enroll like juniors and seniors.

Which majors’ statistics/quantitative or computing requirements does Data 8 satisfy?

  • Please review the current list of requirements satisfied. If your major or intended major has a statistics/quantitative requirement or a computing requirement and isn’t listed here, please consult an advisor for your major. We would be grateful to hear about it on Piazza.

Does Data 8 satisfy the L&S Quantitative Reasoning requirement?

  • Yes.

Can I receive credit for both Data 8 and CS 61A?

  • Yes.

Can I receive credit for both Data 8 and Stat 2/20/21?

  • Yes, but this path is not recommended.

Should I take Data 8 if I have already completed both CS 61A and Stat 20/21?

  • No. You should take a more advanced data science course, such as Data 100CS/STAT C100 (which also requires additional math prerequisites). In the proposed Data Science major we hope to allow a “grandfathered” option for students taking this pathway before Data 8 was available.

How does Data 8 set me up for courses in other areas of study that draw on introductory statistics as found in Stat 2 or Stat 20/21?

  • For students planning to pursue other areas of study, the Statistics Department recommends Data 8 for all areas where students have historically taken Stat 2, and Data 8 plus the Stat 88 connector for areas where students have historically taken Stat 20/21.

Connectors

What is a connector?

  • A connector course lets you weave together core concepts and approaches from Data 8 with complementary ideas or areas. Along the way you gain additional experience, broader insights, or deeper theoretical or computational foundations. Connectors are taught by instructors from departments across campus. Data 8 and connectors complement each other and often use similar materials or tools.

How do I find a connector?

  • Most connectors are numbered 88 (but not all of them). Check the list of all courses this semester. Then enroll through Cal Central.

What areas are connectors offered in?

Can I take more than one connector?

  • Yes. Each connector delves into a rich, distinct area of study.

Am I required to take a connector?

  • No, it’s not required to take a connector, but it’s highly encouraged.

Can I take a connector course at the same time as Data 8? After Data 8?

  • Yes, and yes.

How about before Data 8? What does it mean that Data 8 is a “co- or pre-requisite” for a connector?

  • Data 8 should taken at the same time as or before a connector. Taking the connector first is not recommended.

Do connectors have other prerequisites besides Data 8?

  • Mostly not, but a few do. Please check the course description.

What’s the difference between a connector with 2 units and one with 3 or 4 units?

  • The workload is proportional to the number of units. Connectors with 3 or 4 units may also satisfy other kinds of requirements (major, breadth) on a case-by-case basis.

How many seats are available in connectors?

  • Overall we aim to offer 40-50% as many seats in connectors as in Data 8. The number of seats in each class is set by the department that offers it. Because of the demand, if you’re interested in taking a connector, we encourage you to consider several options from the list available each semester.

Do any connectors fulfill requirements?

  • STAT 88 fulfills some requirements in conjunction with Data 8.

  • STAT 89A in its 4-unit Spring 2018 format can be used as an alternate way to satisfy the linear algebra co-requisite for Data 100 in Spring 2018.
  • It’s under consideration that CS 88, STAT 88, and STAT 89A may be used to fulfill requirements in those programs and the proposed Data Science major and minor programs.
  • It’s possible that other majors will begin to require connectors in their own areas or incorporate them into prerequisite chains. Please check with advisors for each major.

Data 100 (COMPSCI / STAT C100, Principles and Techniques of Data Science)

What is Data 100?

  • Data 100 is an upper-division course on the Principles and Techniques of Data Science. It explores key areas of data science including question formulation, data collection and cleaning, visualization, statistical inference, predictive modeling, and decision making.

Should I be interested in Data 100?

  • Data 100 gives students experience in working with real-world data, tools, and techniques so they can apply computational and inferential thinking to address real-world problems. It provides the necessary foundation and context for more advanced Data Science courses. Data 100 is a good choice for students who may be thinking of majoring or minoring in Data Science.

What prerequisites does Data 100 have?

  • Data 100 has Data 8 and additional prerequisites (currently CS 88 or CS 61A for computing) and co-requisites (currently Math 54, EE 16A, or STAT 89A for linear algebra).

Who designed Data 100? Who teaches it?

  • Data 100 was designed by a multidisciplinary team of Berkeley faculty. It is taught by different faculty in different semesters.

Is there any difference between COMPSCI C100 and STAT C100?

  • No, they’re both the same class.

Both classes show up in Cal Central. Which one should I enroll in?

  • All the seats in each semester are associated with one department. Enroll in whichever one has seats that semester. (Fall 2017: STAT C100, CCN 45085; Spring 2018: COMPSCI C100, CCN 37227)

How many seats are available in Data 100?

  • The class is now a regular offering. We aim to offer it every semester and are trying to meet student demand. In Spring 2018 Data 100 will have 600 seats.

How can I learn more about Data 100?

Additional Course Pathways Starting from Data 8

Will there be new courses with Data 8 as a prerequisite?

  • Yes, that’s expected. Faculty are working on designing new courses and integrating Data 8 into sets of prerequisites for existing courses.

Should I be interested in STAT 28?

  • STAT 28 (which may be renumbered STAT 128) covers Statistical Methods for Data Science. It examines how statistical data science methods can be used for concrete applications. It’s a good choice for students who want to gain more experience without majoring or minoring in Data Science. STAT 28 has only Data 8 as a prerequisite, and no additional math.

Should I be interested in STAT 140?

  • STAT 140 covers Probability for Data Science. It uses computation to develop the theoretical foundations of data science. It satisfies the STAT 134 prerequisite for advanced statistics courses as well as for the Statistics major. It is also a good choice for students who may be thinking of majoring in Data Science. STAT 140 has Data 8 and 1 year of calculus as prerequisites, and Math 54 or EE 16A as a co-requisite.

Are other courses well-suited for students who have taken Data 8?

Can you recommend pathways for further data science-related classes in relation to my major?

  • We’re working on developing pathways relevant to many majors. You can find our current lists of data science-related classes offered in Spring 2018 here.

In what ways can Data 8 be used to count toward the majors in Statistics, Computer Science, and EECS?

  • Please consult advisors in those departments as they update their majors.

Data Science Modules in Other Courses

Can I learn data science skills in other classes at Berkeley?

  • Yes, many classes across the university incorporate Data Science modules. A module is a short or long exploration of data science methods that an instructor has integrated into an existing course. It allows students to work hands-on with a data set relevant to their course and receive some instruction on the principles of data analysis, statistics, and computing.

Which classes have Data Science modules?

  • Several dozen classes have developed modules so far. These include classes in the humanities, social sciences, and natural sciences. You can learn more about modules here.

I’m in a class that doesn’t have a Data Science module, but I think it would be a good fit. What can I do?

  • You should speak to your instructor. If they’re interested in adding a module, we’re happy to explore whether that’s possible. While it’s hard to add a module in a semester that’s already started, we can often develop a module for the next offering, so your peers will get the benefit.

I really enjoyed a module in a class I took. What should I do next?

  • If you enjoyed your experience with modules, we recommend you consider taking Data 8, Foundations of Data Science. It uses the same pedagogical approach. After you take Data 8, you can even help develop new modules in other classes!

Information, Community, and Support

Who is responsible for planning the Data Science curriculum?

  • The Division of Data Sciences, led by the Interim Dean and the Data Science Education Program Faculty Lead. The Division was formed in July 2017, so we’re actively building our programs and staff.

Are there Data Science major and minor advisors? How can I get my questions answered?

  • We are currently building up our advising team, but don't yet have the capacity for individual advising.
  • For questions about classes in existing departments (prerequisites, pathways, etc.), ask those departments' advisors. For questions about the Data Science program, please review these FAQs and ask your regular advisor for now.
  • While we build up our advising program and processes, you can direct your questions to our online Q&A forum, the Data Science Support Piazza (DATA 001). Many questions may have previously been answered there, and we hope it will continue to be a resource for everyone to consult until our advisors can take on individual advising appointments.

Is there a Facebook page for students connected with the Division of Data Sciences?

Is there a student community around Data Science at Berkeley?

  • Yes, it’s energetic and engaged with many different groups and offerings. Please explore and find your niche. Along with the Data Science Education Program, there are other Facebook groups (such as here).

Is there support for students from first-generation, low-income, or underrepresented backgrounds?

  • Yes, our Data Scholars program is intended to address the issues of underrepresentation in the data science community by establishing a community that is welcoming, educational, and empowering for underrepresented and nontraditional students. You can apply to join the program when we open up applications near the end of each semester.

Are there leadership roles for students in building the Data Science Education Program?

  • Yes, absolutely. We have a large student team working on course modules, connector assistance, curriculum mapping, student experience, diversity, outreach, and technical development.

How can I get involved in the Data Science Education Program?

  • At the beginning and end of each semester the student teams recruit new members, so keep a look out for information session events and application notifications on our News pages, our Facebook page, and on our Piazza forum!