PSTAT 100: Data Science Concepts and Analysis
Course Policies
Welcome to PSTAT 100: Data Science Concepts and Analysis! I am very excited to join you as your instructor this quarter. Our journey together will take us through the basics of Data Science, and aims to prepare you for your future endeavors in the field, whether they be in classes, industry, or academia. Here’s to a great quarter! – Ethan
Graphical Syllabus
Course Policies
Course Staff and Lecture Logistics
Instructor: Ethan P. Marzban (he/him)
T, R from 9:30am - 10:45am, in BUCHN 1940.
Teaching Assistants: TBD, TBD
Sections:
- M 2 - 2:50pm in PHELPS 1518 (TBD)
- M 3 - 3:50pm in PHELPS 1518 (TBD)
- M 4 - 4:50pm in PHELPS 1517 (TBD)
- M 5 - 5:50pm in PHELPS 1513 (TBD)
Course Description
As stated in the catalog description:
Overview of data science key concepts and the use of tools for data retrieval, analysis, visualization, and reproducible research. Topics include an introduction to inference and prediction, principles of measurement, missing data, and notions of causality, statistical “traps”, and concepts in data ethics and privacy. Case studies will illustrate the importance of domain knowledge. Credit units: 4.
Indeed, this course is designed to be a hands-on introduction to Data Science for intermediate-level students with some exposure to probability and basic computation skills, but with few or no upper-division courses in statistics.
Prerequisites:
PSTAT 120A (Probability at a calculus-based level)
MATH 4A (a first pass at Linear Algebra)
Prior experience with a programming language (e.g. Python through CMPSC 9 or CMPSC 16).
Note on Programming: the primary programming language of this course will be R, though you are not expected to necessarily have prior experience coding with R (so long as you have experience coding in another language, e.g. Python, Julia, etc.)
Textbooks/Readings
There are two “required” textbooks this quarter (required in the sense that readings will be assigned from them), both of which are freely available at the links below courtesy of the authors:
R for Data Science (2e), by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund
Introduction to Modern Statistics, 2nd Ed., by Mine Çetinkaya-Rundel and Johanna Hardin
Another good resource for those of you very interested in Data Science (keep in mind this book is written primarily with the Python programming language in mind, which is why I will not be assigning reading from it):
- Learning Data Science, by Sam Lau, Joey Gonzalez, and Deb Nolan
If you are new to programming in R, you may also find this textbook useful:
- An Introduction to R, by Alex Douglas, Deon Roos, Francesca Mancini, Ana Couto & David Lusseau
There will also be a handful of articles which will be assigned as reading. All required reading can be found in the Weekly tabs of the Materials page.
Learning Outcomes
By the end of this course, you should be able to:
critically assess data quality and sampling design
retrieve, inspect, and clean raw data
understand the basics of exploratory, descriptive, visual, and inferential techniques
interpret and communicate results in context
Assessments
Labs: short, structured coding assignments designed to introduce programming concepts and skills. Labs will be assigned weekly, and are designed to be mostly completed during Section on Monday but won’t be due until 11:59pm on Wednesdays.
Homeworks: slightly larger in scope than labs; will contain both programming and theoretical/conceptual concepts. We will have a total of 3 homework assignments released throughout the quarter; typically you will have 1-2 weeks to work on the homeworks and you are encouraged to start as early as possible!
Mini Projects: more open-ended than homeworks and labs, and are designed to more closely simulate real-world data science projects and endeavors. We will have a total of 3 mini projects (released in non-exam weeks where there are no homeworks due) throughout the quarter, and you are encouraged to work collaboratively.
Final Project: a final, comprehensive project that will be due during finals week. You will be required to work in teams on the Final Project.
The final project will be due by 11:59pm on Tuesday, June 11.
- 2 In-Class Assessments (ICAs) will be administered (see below for dates). I hesitate to call these “exams” because they are not intended to be as high-stakes as exams, however they are designed to test your retention of course material. More information will be released as we approach the date of the first ICA.
- In-Class Assessment 1: Thursday, April 25, 2024 in BUCHN 1940 (our Lecture classroom) starting at 9:30 (our regular class time)
- In-Class Assessment 2: Thursday, May 23, 2024 in BUCHN 1940 (our Lecture classroom) starting at 9:30 (our regular class time)
There will be no ICAs offered at alternate times, for any reasons. So, please plan accordingly!
Final Course Grades
Your final course grade will be computed according to the following weights:
| Assessment | Weight |
|---|---|
| Labs | 10% |
| HW | 15% |
| Mini Projects | 20% |
| Final Project | 25% |
| In-Class Assessments | 30% |
Your final letter grade will be issued according to the following scheme (cutoffs between plusses and minuses will be calculated at the end of the quarter):
- A- – A+: 90 – 100%
- B– – B+: 80 – 89.99%
- C– – C+: 70 – 79.99%
- D– – D+ : 60 – 69.99%
- F: 0 – 59.99%
I have elected to adopt an uncurved grading scheme to eliminate any sense of “competition” among students; I highly encourage you all to collaborate with and uplift each other. Having said that, I will certainly consider adjusting the cutoffs at the end of the quarter if necessary.
Policies
Late Submissions
You are allowed two late submissions across homeworks and labs (so, 2 in total; not 2 each), that must be submitted within 48 hours of the original deadline. No work will be accepted beyond 48 hours after the original deadline. Additionally, because projects may/will be collaborative, late project submissions will not be accepted.
Communication
There are two primary means of communication outside of scheduled class meetings: office hours and an EdStem Discussion Forum (please see Canvas for a join link; for security purposes, we are only allowing currently-enrolled students join the discussion forum). If you are unsure of how to reach out to appropriate parties, please consult the following table:
| Topic | Redirect to… |
|---|---|
| Troubleshooting codes | EdStem |
| Checking answers | Office hours or EdStem |
| Clarifying assignment content | Office hours or EdStem |
| Assignment submission | Gradescope |
| Re-evaluation request | Gradescope |
| Question about missing grades | Fill Out This Form |
Some additional comments:
Please note that we (the course staff) request you refrain from emailing us except in case of extreme emergency (it is up to you to decide what is an ‘emergency’). Please bring all of your questions to the course staff during either Office Hours or after Lecture/Section. Thank you!
If you have questions or concerns about missing grades, please use the form linked in the table above. You are allowed to submit the form multiple times, but we ask that you please wait at least 48 business hours before submitting follow-ups. Thank you!
Collaboration and Academic Integrity
Data Science (as we will see) is an inherently collaborative field. As such, you are not only allowed but also encouraged to collaborate on assignments (be they lab, homework, or project). However, there are limitations to collaboration:
- collaboration on the ICAs is strictly prohibited
- do not copy other people’s work and try to pass it off as your own
- if/when you work in groups, include the names of all group members on the assignment
Anyone found guilty of academic misconduct will be reported to the Academic Senate, and will receive at minimum a failing grade on the assignment in question; further actions may also include failing the course, and marks being made on permanent records. Depending on the severity of the infraction, expulsion is also a possibility.
Basically, don’t cheat- please! If you’re ever struggling with course material, please come talk to me or the TA’s. We are truly here for you, and want only the best for you.
Section Switching
Lab Sections take place in special “Collaborate Classrooms” which are equipped with laptops. There are only a fixed number of seats and laptops in these classrooms, meaning we cannot under any circumstance over-enroll sections. Therefore, if you want to switch section unofficially (we do not have the ability to switch your official enrollment through GOLD), please follow the steps at this link. Any requests to switch sections that do not adhere to the guidelines posted at that link will be ignored.
Disabled Students Program (DSP)
If you have a disability, or otherwise require accommodations for the exams and/or quizzes please reach out to the Disabled Students Program (DSP) ASAP to ensure your request(s) for accommodation can be processed. We ask that all requests be logged at least a week in advance, to ensure the system enough time to process. Please note that we cannot grant any requests for accommodations unless they come to us from DSP directly.
Technology Needs
As a part of this course, you will be required to program in R. Though the Lab Sections take place in specially designed classrooms that come equipped with computers, your homework and quizzes may cover R-related questions, which means we expect you to have access to a laptop capable of connecting to the internet. If you do not currently possess such a laptop, please check out UCSB’s Basic Needs Resource page on Technology Resources to try and acquire one.
Disclaimer
The instructor reserves the right to modify this syllabus if he deems such modifications academically advisable. Such modifications, should they occur, will be announced publicly.
Faculty Mentor
The faculty mentor for this course is Dr. Drew Carter. They can be reached at carter@pstat.ucsb.edu. Please note that Dr. Carter will not be able to authorize regrades or accommodations/extensions for the course; for such inquiries, please utilize the communications channels listed above. Thank you!