This document and others linked within it should be your PRIMARY source for understanding the expectations of this course. Be sure to read it carefully. You must contact the instructor for clarification if you receive information from any another source that is in contradiction to what is provided below.

Course Title:  Data Science Foundations

Course Description

Data is everywhere in the modern world. Each of us has access to data on virtually any topic imaginable via the internet, and public and private institutions are making use of data in decision-making more than ever before. However, access to data is not the same thing as access to information. The purpose of this course is to develop some of the foundational skills needed to consume data and create information. The main theme in the course is understanding the sources of data, the variability inherent in data, and the inherent uncertainty associated with conclusions drawn from data.

The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets from a variety of disciplines. It delves into social issues surrounding data analysis such as privacy and design.


This course does not have any prerequisites beyond high-school algebra (and a desire to learn). The curriculum and format is designed specifically for students who have not previously taken statistics or computer science courses, which is one of the reasons why this course is open only to the first- and second-year students. Students who have taken several statistics or computer science courses should instead take a more advanced course.



Our primary text is an online book called Computational and Inferential Thinking: The Foundations of Data Science.

The computing platform (Jupyter Notebooks) for the course is hosted at


You are not alone in this course; the mentors (staff and the 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 and practice. Take advantage of the office hours and the open-lab hours and attend them even if you don’t have specific questions but feel uncertain about your understanding of the material. Small-group tutoring sessions can be available for students in need.

Contact us on Piazza!

We will be communicating with you and making announcements through an online question-and-answer platform called Piazza. We ask that when you have a question about the class that might be relevant to other students, post it on Piazza instead of emailing us (if you wish, you can post your question anonymously to your classmates). That way, all the staff can be on the same page and everyone can benefit from the response. You can also post private messages to instructors on Piazza, which we prefer to email.

Diversity and Inclusiveness

We (the instructor and the mentors) strive to create an environment in which students from diverse backgrounds and perspectives can be well-served in this course, where students’ learning needs can be addressed both in and out of class, and where the diversity that the students bring to this class is viewed as a resource, strength, and benefit. It is our intent to present materials and activities that are respectful of diversity: gender identity, sexuality, disability, age, socioeconomic status, ethnicity, race, nationality, religion, and culture. Your suggestions are encouraged and appreciated. Please let me know ways to improve the effectiveness of the course for you personally, or for other students or student groups.

We (like many people) are still in the process of learning about diverse perspectives and identities. If something was said in class/section (by anyone) that made you feel uncomfortable, please, don’t hesitate to talk to me about it. Help us create a welcoming, inclusive atmosphere that supports a diversity of thoughts, perspectives and experiences, and honors your identities (including gender identity and expression, sexual orientation, disability, neuro(a)typicality, physical appearance, age, race, socioeconomic status, ethnicity, nationality, culture, or religion (or lack thereof)).

(Inspired by and adopted from Mine Çetinkaya-Rundel and Hacker Hours).

Disabled Students Program (DSP)

UCSB provides academic accommodations to students with disabilities. Students with disabilities are responsible for ensuring that the Disabled Students Program (DSP) is aware of their disabilities and for providing DSP with appropriate documentation. DSP is located at 2120 Student Resource Building and serves as the campus liaison regarding issues and regulations related to students with disabilities. The DSP staff works in an advisory capacity with a variety of campus departments to ensure that equal access is provided to all disabled students. If you have a disability that requires accommodation in this class, please go see the DSP very early on in the quarter. I will only honor these types of requests for accommodation via the DSP. More information about the DSP is found here:

Managing Stress

Personal concerns such as stress, anxiety, relationships, depression, cultural differences, can interfere with the ability of students to succeed and thrive. In addition to the course staff and resources, you can contact UCSB Counseling & Psychological Services (CAPS) at 805-893-4411 or visit If you encounter a student in distress, please contact 805-893-3030 immediately and/or consult the Responding to Distressed Student Protocol at or phone 893-3030.

Building Academic Skills

For general academic support, students are encouraged to visit Campus Learning Assistance Services (CLAS) early and often. CLAS offers instructional groups, drop-in tutoring, writing and ESL services, skills workshops and one-on-one consultations. CLAS is located on the third floor of the Student Resource Building, or visit


The rest of this page details the policies that will be enforced in the Fall 2019 offering of this course. These policies are subject to change throughout the remainder of the course, at the judgement of the course staff (with a potential announcement on Piazza).


Your mastery of class material will be assessed in the following ways, and final grades will be computed as follows:

It is certainly possible for all students to receive high grades in this course if all of you show mastery of the material on exams and complete all assignments.


Lecture attendance is highly encouraged. You are adults and are responsible for your learning. I expect you to come to all classes, since this is an essential part of your education. This is also your time to engage with the material and ask your questions. Additionally, participating by answering or clarifying questions on Piazza is highly encouraged. During class, you will work alone and in groups to work through problems and answer questions. On some days, the groups will be asked to turn in their in-class work. If you were absent, you miss the opportunity for the points on that in-class assignment. There is no makeup. In lieu of providing a makeup opportunity, I will drop the lowest in-class-assignment grade. Each class activity will be of equal value. The participation portion of your grade will also include providing good answers on Piazza and engaging with the various activities that the instructor will provide throughout the quarter (e.g., online surveys). Disrespectful, unprofessional, and otherwise inappropriate behavior can be grounds for receiving a zero for participation in this course.


Data science is about analyzing real-world data sets, so a series of projects involving real data are a required part of the course.

Weekly homework assignments are a required part of the course. Each student must submit each homework independently, but you are allowed to discuss problems with other students without directly sharing the answers.

Make a serious attempt at the assignment yourself, and then discuss your doubts with others. In this way you, and they, will get more out of the discussion. Please write up your answers in your own words and don’t share your completed work. We take academic integrity seriously and we ask for your cooperation.


Weekly labs are a required part of the course and should be submitted during your lab session. To receive credit, you must attend lab, work on the lab assignment until you’re finished or the lab period is over, and get checked off by a TA. Labs will be released on Wednesday. If you cannot attend lab physically, you may complete a lab assignment remotely, but you must complete it by the date and time listed at the top of each lab. The advantage of attending the lab in person is that you can get help from the TA and tutors that are available to answer questions and provide guidance during the closed labs. Each person must submit each lab independently, but you are welcome to collaborate with other students in your lab room.

The swimming/guitar/painting analogy

You cannot learn to swim, play guitar, or paint from a textbook or a lecture. You can only:

The same is true of learning about computing and, especially, programming. Programming is not a series of facts to be memorized—you cannot "cram" for an exam. You must practice, practice, practice. Here are some additional tips on what you can do instead of cramming: Why You Cram for Exams (and How to Stop) and How to Enjoy Studying.


Instead of a midterm and a final exam, we will have two exams during the quarter with the final project at the end. Unless you have accommodations as determined by the university and approved by the instructor, you must take the exam at the date and time provided here. Please check your course schedule and make sure that you have no conflicts with the exams. If you have a conflict, please post a private note on Piazza visible to Instructors before the end of the second week of classes.

Final Project

More information will be provided later in the class.

Late Submission

Late submissions of labs will not be accepted under any circumstances. The same goes for homework, unless you have relevant DSP accommodations and you contact us before the assignment is due.

Reminder: If you are registered for another UCSB course that overlaps with this one, you MUST HAVE specific written permission from both instructors, or we are within our rights to give you a failing grade on any work you miss as a result, and will NOT make any accommodations for you. This includes exams.

Excused absence

There is no make-up, except for excused absences that are arranged with and agreed to by the instructor in advance, for official UCSB activities.

In rare cases, if there is a documented family emergency, documented extended illness, documented required court appearance, or other situation beyond the students’ control (with appropriate documentation) the instructor may extend an assignment deadline, entirely at the instructor’s discretion—but this is not a guarantee or a right.

Learning Cooperatively

With the obvious exception of exams, we encourage you to discuss all of the course activities with your friends and classmates as you are working on them. You will definitely learn more in this class if you work with others than if you do not. Ask questions, answer questions, and share ideas (not answers!) liberally.

Academic Honesty

Cooperation has a limit, however. You should not share your code or answers directly with other students. Doing so doesn’t help them; it just sets them up for trouble on exams. Feel free to discuss the problems with others beforehand, but not the solutions. Please complete your own work and keep it to yourself. The exception to this rule is that you can share everything related to a final project with your project partner and turn in one project between you. If you are not sure about whether some kind of collaboration is permitted or not, it is your responsibility to verify with the instructor and ask questions.

Penalties for cheating are severe — they range from a zero grade for the assignment or exam up to dismissal from the University, for a second offense. The Office of Judicial Affairs has policies, tips, and resources for proper citation use, recognizing actions considered to be cheating or other forms of academic theft, and students’ responsibilities, available on their website at: Students are responsible for educating themselves on the policies and to abide by them.

Rather than copying someone else’s work, ask for help. You are not alone in this course! We are here to help you succeed. If you invest the time to learn the material and complete the projects, you won’t need to copy any answers.

A Parting Thought

This page shouldn’t end with a list of penalties for cheating or lateness, because penalties and grades aren’t the purpose of the course. We actually just want you to learn and have a great time in the process. Please keep that goal in mind throughout the semester. Welcome to Data Science Foundations!

Last major revision: Sep 26, 2019

Update on 10/12 to add the exam dates from the calendar and correct the textbook link.