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This is a very stressful and unusual time for all of us. We recognize that many of you are sheltering in different parts of the world. First and foremost, over the next few months, make sure you take care of yourselves and your families.
With that in mind, we are very excited to share this course with you. We want you to learn and have a great time in the process. Let this class be an escape from some of the uncertainty in the rest of the world. Welcome to Data Science Foundations!
Data Science is a rapidly evolving field that does not have a uniformly agreed-upon definition. It is an inter-disciplinary field that uses scientific methods, statistical and computer science concepts and processes to extract and communicate knowledge and insights from data. The key component that differentiates Data Science from Computer Science or Statistics is its connection to and the need to understand the contexts from other domains such as Biology, Environmental Science, Economics, etc.
In this course, we will introduce and use critical concepts and skills in computer programming and statistical inference to analyze real-world datasets and interpret real-world phenomena. 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, biases and fallacies, and the inherent uncertainty associated with conclusions drawn from data.
By the end of the course students will have practiced and learned the following concepts:
- Foundational programming concepts:
- Memory concepts: variables, name, type, value, assignment statements, scope of variables
- Control structures: for loops, if/else, while loops
- Basic data structures: lists
- Functions: function call vs. function definition, formal vs. actual parameters (arguments)
- Input/output concepts: printing output, reading from files
- Problem solving strategies and code design:
- breaking down a problem into a sequence of steps
- abstracting specific problems into general ones, and
- finding general solutions
- Debugging programs by:
- reading code and predicting the output of programs or parts of code
- using print statements to localize program bugs
- Data Science concepts:
- Basic operations on tables: loading data, extracting columns, extracting rows
- Selecting rows that match certain criteria, composite queries, join / aggregate
- Computing summary statistics
- Exploratory Data Analysis (EDA)
- Simulating experiments
- Probability basics
- Analysis and critical thinking with a special attention to biases and fallacies, ethics and fairness
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 created to target the first- and second-year students. Students who have taken several statistics or computer science courses should instead take a more advanced course.
Unfortunately, due to the COVID-19 pandemic, students in the class will not have access to the physical lab space and computers. Due to the remote nature of the course this quarter, the additional new "technological prerequisites" for the course are access to a computer or a tablet (not smartphone) and a reasonably stable/fast Internet connection.
You will probably also need headphones and a non-distracting area. You'll watch/listen to live video/audio, discuss and work in groups, and type to chat or code.
- Instructor: Prof. K (Yekaterina Kharitonova)
She/her - Lecture: Tuesday and Thursday, 11:00am-12:15pm PST (Pacific/California time), synchronously on Zoom
- Lab / Discussion Sections:
- Wednesday, 10:00am - 10:50am, on Zoom
- Wednesday, 11:00am - 11:50am, on Zoom
Zoom links and passcodes will be posted on Piazza.
There are no exams.
- Midterm project due TBA.
- Final project due Dec 11 at 9pm.
Our primary text is an online book called Computational and Inferential Thinking: The Foundations of Data Science (https://www.inferentialthinking.com).
The computing platform (Jupyter Notebooks) for the course is hosted at https://data1.lsit.ucsb.edu/.
We will be communicating with you and making announcements through an online question-and-answer platform called Piazza. It is your responsibility to monitor this forum and stay up-to-date with the announcements.
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.
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, culture, or religion (or lack thereof). Your suggestions are encouraged and appreciated: help us improve the effectiveness of the course for you personally, or for other students or student groups.
If at any point you are made to feel uncomfortable, disrespected, or excluded by the course staff or fellow students, please, don't hesitate to talk to us about it so that we may address the issue and maintain a supportive and inclusive learning environment. If you are uncomfortable bringing up an issue with the course staff directly, submit anonymous feedback or contact the Office of the Ombud.
Help us create a welcoming, inclusive atmosphere that supports a diversity of thoughts, perspectives and experiences, and honors your identities.
(Inspired by and adopted from Mine Çetinkaya-Rundel, Hacker Hours, and Kevin Lin).
Learn about pronouns and how to change your name and pronouns at UCSB. The UCSB Resource Center for Sexual & Gender Diversity maintains a list of LGBTQ Student Organizations at UCSB.
Educational Opportunity Program (EOP) serves all students while focusing on low-income and first-generation undergraduates. ONDAS Student Center at UCSB promotes the success and retention of first-generation college students with an emphasis on the first year transition and underrepresented student experience. UCSB has a variety of resources for students facing food insecurity.
Check out a list of Latinx Resources at UCSB and information about clubs and programs at UCSB available to black students. More information about student clubs and organizations can be found via Shoreline.
If you have a resource that you would like us to link here, send us a message on Piazza with the information.
UCSB provides academic accommodations to students with disabilities. DSP serves as the campus liaison regarding issues and regulations related to students with disabilities. If you have a disability that requires accommodation in this class, please contact the DSP very early on in the quarter. We will only honor these types of requests for accommodation via the DSP. More information about the DSP is found here: http://dsp.sa.ucsb.edu.
Students may feel overwhelmed or depressed with coursework, stress, anxiety, relationships, cultural differences, and/or other personal challenges. If you find yourself, or another student, in need of support, please do not hesitate to reach out to Counseling and Psychological Services (CAPS), 24/7 at (805)893-4411, http://caps.sa.ucsb.edu.
From building academic skills at CLAS to reporting bias incidents, you can find a list of additional resources at UCSB: http://academics.sa.ucsb.edu/programs/campus-service/student-support-syllabus-text.
The rest of this page details the policies that will be enforced in the Fall 2020 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:
- 30% Homework Assignments
- 20% Lab Assignments
- 15% Participation Activities
- 15% Midterm Project
- 20% Final Project
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.
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.
Data science is about analyzing real-world data sets, so a series of projects involving real data are a required part of the course.
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 your answers in your own words and don't share your completed work. Both students will be reported if we detect an academic integrity violation.
Weekly labs are a required part of the course. If you cannot attend the assigned lab section, you may still complete a lab assignment remotely by the due date 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 who are available to answer questions and provide guidance during the scheduled labs. Each student must submit each lab independently, but you are welcome to discuss the concepts with other students in your lab.
In order to make sure that you are following along with the course content, we will provide various participation activities that will allow you to engage with the readings, put what you are learning into practice, and/or check your understanding via quizzes.
Lecture attendance is highly encouraged. You are adults and are responsible for your learning. I expect you to attend 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.
Asking questions during the lecture, working with other students in groups on the lecture activities, engaging with the various activities that the instructor will provide throughout the quarter (e.g., online surveys), and participating by answering and/or clarifying questions on Piazza is highly encouraged and can benefit your final grade if your score is on the border between grades.
Disrespectful, unprofessional, and otherwise inappropriate behavior can be grounds for receiving a zero in this course.
More information will be provided later in the class.
Late submissions of labs are normally not accepted. The same goes for homework, unless you have relevant DSP accommodations and you contact us before the assignment is due.
That being said, given the unusual situation that the entire world is in with regards to the covid-19 pandemic, we will consider making reasonable exceptions on an individual basis. Contact the instructor as soon as you can with a request for accommodation. Note that late submissions will not be accepted if that submission has already been graded for the class.
All lecture material including slides and lecture recordings will be posted on the website after class.
The lectures and course materials for this course, including PowerPoint presentations, tests, outlines, and similar materials, are protected by the U.S. copyright law and the University policy. The instructor is the exclusive owner of the copyright in those materials, unless stated otherwise. You may take notes and make copies of course materials for your own use.
You may not reproduce, distribute or display (post/upload) lecture notes or recordings or course materials — whether or not a fee is charged — without the instructor's express prior written consent. You also may not allow others to do so.
If you do so, you may be subject to student conduct proceedings under the UC Santa Barbara Student Code of Conduct.
Similarly, you own the copyright in your original papers and exam essays. If we are interested in posting your answers or papers on the course web site, we will ask for your written permission.
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 later. Feel free to discuss the problems with others beforehand, but not the solutions or exact implementation. Please complete your own work and keep it to yourself. The exception to this rule is that you can share everything related to a group 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 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: https://studentconduct.sa.ucsb.edu/academic-integrity. 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.
We don't want to end this page with a list of penalties, 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: Oct 1, 2020
Last updated: Nov 3, 2020 (updated links to campus resources and copyright info)