Syllabus#
When and where?#
Communication: Slack | Github
Location: Mandler 3545 (Crick Conference Room)
Lectures: Mon+Wed 2:00-3:50pm
Lab: Tues 5:00-6:50pm
2025 Instructional Team#
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Role |
Instructor |
Teaching Assistant |
Teaching Assistant |
Slack |
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Github |
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Office |
Mandler 3509 |
McGill 2318 |
Mandler 1503 |
Office Hours |
Wednesdays 4-5pm or by appt |
Thursdays 9-10am |
Tuesdays 1-2pm |
How we’ll learn#
Github Classroom#
We will be using Github Classroom to manage all lectures, labs, HWs, exams, and projects. Each week, we’ll update the course website with links (prefixed with: 📚) to new Github Classroom assignments that contain all the materials you’ll need for that day’s lecture, labs, or HWs. At the start of class/lab, or when a HW problem-set is made available, you should accept assignments and git clone them to your local computer to work interactively.
When you’re finished with an assignment or when you want to get feedback on work-in-progress, you should commit your changes to your local copy of the assignment, and then push them to Github. This will allow your instructors to review your work, provide Feedback, and/or have a private discussion with you while referencing questions/issues in your code directly. At the same time, you’ll be building up a set of references (with feedback) that you can always check-out and refresh after this class is over!
We’ve made instructions and a detailed tutorial video about using Github Classroom available on the course website, under the Computing Resources section. If you consistently have trouble with Github Classroom, please reach out to your instructors for help and we can figure out solutions together.
Course Website#
When in doubt, the course website should be the first place you look for any logistical information! We’ll be updating it regularly week-by-week with a new section for that week’s plans and materials.
Attention
While you’ll always be able to view each week’s materials and labs on the course website, you should be using the Github Classroom to download and work on these materials interaactively, and submit your work to receive credit.
Under the Overview page for each week, you’ll find:
Our plan for that week’s topics (subject to change)
📚 links to Github Classroom assignments for that week’s lecture & labs
A notice at the top of the page with any HWs, links, and due dates
Any required or suggested readings
Under the Lab page for each week, you’ll find:
Our goals for that lab (subject to change)
📚 a link to the Github Classroom assignment for lab materials (also on the week’s Overview page)
Additional helpful technical resources for that lab’s topics
Communication#
We will primarily be communicating using the course Slack so please make sure you join by the end of Week 1 or let your instructors know if you have any issues!
Reading Materials#
Required or optional readings/videos will be linked on that week’s Overview page. We’ll always make PDFs/links available for any materials so you won’t be required to purchase or refer to any other course textbook. We will be leaning heavily on readings from the following sources:
Statistical Thinking for the 21st Century by Russ Poldrack
Computational and Inferential Thinking: The Foundations of Data Science by Ani Adhikari, John DeNero, David Wagner.
Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
Regression and other stories by Andrew Gelman, Jennifer Hill, and Aki Vehtari
The Truth about Linear Regression by Cosma Rohilla Shalizi
Statistical Methods for Behavioral and Social Sciences by Tobias Gestenberg
Computing Resources#
During Week 1, your instructors will help you configure your own computer so you have a working version of Python, scientific libraries we’ll use, git/github configured to fetch and submit assignments, and code-editor (JupyterLab or VSCode) setup so you can work on notebook files interactively. If you have consistent issues with your local computer, please reach out to your instructors and we can help setup a UCSD provided Jupyterhub instance or a Github CodeSpace for you to work from.
Throughout the term, we’ll link to additional resources and guides under the Computing Resources section of the course website. These include a variety of introductory tutorials, cheatsheets, and with a glossary of terminology to help you navigate the scientific Python ecosystem.
Course work and grading#
Course work and grading will primarily consist of:
interactive lectures
interactive labs
weekly social HW problem-sets
a take-home solo Final problem-set
Lectures#
During lectures, we will step-through interactive Python notebooks together explaining core statistical concepts with a combination of illustrative examples, widgets, mini-exercises, and larger challenges. Where needed, we’ll refer to additional slides and materials. Because of varied experience levels, it’s likely we won’t always be able to complete the full set of notebooks when we meet. When this happens, we expect you to walk through and attempt anything we don’t finish yourself outside of scheduled class time and push your changes to Github. Your instructors will be available to answers questions over Slack, Github, or in-person. Unless otherwise noted, you don’t need to look at materials ahead of lecture, but should prepared to participate in any class discussions.
Labs#
During labs, we’ll build upon ideas introduced in lectures, deep-dive into scientific Python libraries and their particulars, and review HW problem-sets as needed. Previous Python experience is not required, but prior programming experience in another language (e.g. R, Matlab) is helpful. We’ll start slow and provide plenty of resources, links, and additional tutorials on the course website. Labs will also familiarize you with any additional tools you’ll be using to complete and submit assignments (i.e. jupyter notebooks, github classroom, etc). At the end of each lab, you’ll be asked to submit your work to Github Classroom, and your instructors will be available to answer questions over Slack, Github, or in-person.
Homework Problem-sets#
Approximately every two-weeks, you’ll receive a problem-set of questions covering that week’s topics. Unless otherwise noted, problem-sets will always be due the midnight the day before we review that HW. You should complete and submit problems using Github Classroom, submitting any code, figures, and prose that answer the questions. You are encouraged to work on weekly assignments with other students. However, you should list the names of all students you worked with in the notebook you submit.
Final#
The final exam will be formatted just like a homework problem-set, however you must complete it by yourself. More details about the final will be provided later in the term on the course website.
Grading#
Mastery based grading. We’re interested in grading you on your ability to achieve the skill sets that are taught in this course regardless of your starting experience with Python.
For this reason, except for the midterm and final, you can attempt any Github Classroom assignment multiple times, especially if you think you could do better or if you want to incorporate instructor feedback. Practically, this just means making additional code changes and pushing another commit to your assignment. Your instructors will automatically be able to see your code changes and your latest submission. We’ll grade you based partially on your accurate completion of the assignment, but mostly on your ability to demonstrate:
You attempted the assignment in good-faith (lecture, lab, or HW notebooks)
You made effort to clearly document and explain your thought process, reasoning, code, and where/why you got stuck if you did
What attempts you made to fix issues you ran into, how you approached debugging, and what you learned from the process
Why you made a particular choice in your code/analysis, and/or what assumptions you made for a particular statistical inference
Cooperative extra-credit. When working with other students on problem-sets, you have the opportunity to earn extra-credit. Working collaboratively with others is not sufficient for extra-credit. You can only submit a consideration for extra-credit if you and/or your peers feel like you went above-and-beyond on a given weekly problem-set. To do so, include a note in your jupyter notebook, indicating either: (a) who you helped, how, and what they learned; OR (b) who helped you, how, and what you learned.
Extra credit can contribute up to 10% of your final grade.
Grading Breakdown: Your final course grade will be calculated based on:
Class participation (30%)
attending lectures and labs
submitting lecture and lab notebooks to Github Classroom
asking & answering questions (code-review) about submitted assignments using the Feedback Pull-Request on your assignment repository
completing any required readings and participating in class discussions
Weekly problem-sets (40%)
~5 total, ~8% each
attempting and submitting weekly problem-sets to Github Classroom, alone or with peers
working with other students to help each other out (and earning up-to 10% extra-credit)
demonstrating improvement by updating your submissions based on instructor feedback
Final problem set (30%)
TBD
Academic Integrity#
All students are expected to adhere to standards of academic integrity. Cheating of any kind on any assignment will not be tolerated. It is disrespectful to your peers, the university, and to your instructors. If you are unsure what might constitute a violation of academic integrity, ask your instructors and/or the UCSD website on academic integrity: http://academicintegrity.ucsd.edu. Any evidence of academic misconduct will be reported to the Academic Integrity Office.
Generative AI Course Policy#
Adapted from the UC San Diego & University of Waterloo Academic Integrity Offices
Warning
GenAI is known to fabricate sources, facts, and give false information. It also perpetuates bias. You should also be aware that there are copyright and privacy concerns with these tools. You should exercise caution when using large portions of content from AI sources for these reasons. Also, you are accountable for the content and accuracy of all work you submit in this class, including any supported by generative AI.
We encourage the use of Generative artificial intelligence (GenAI) tools like OpenAI’s ChatGPT, Anthropic’s Claude, Github’s Co-pilot, or Google’s Gemini to help you master concepts and skills in this class in accordance with the UCSD Academic Integrity Guidelines on GenAI and the following guidelines:
If you use GenAI for any submitted coursework, you must attach a link or text transcript to any assignments you submit. Many services offer a “share your chat” link-creation function or you can use a Google Chrome Browser Extension like ChatGPT Exporter or Claude Exporter. This will help us provide feedback on using LLM tools effectively (if desired) and make it transparent to us how you are completing assignments, while respecting the standards of academic integrity.
Directly prompting GenAI with course assignments, or copying/pasting GenAI output instead of performing the work yourself, will not earn you assignment credit and could result in an academic integrity violation.
Instead you should aim to master GenAI as tools that supplement your programming and critical thinking skills, not as a substitute for them. They can be especially helpful for: debugging and troubleshooting unfamiliar code, reviewing Python fundamentals, reasoning about statistical concepts via analogy/example, or simply conversing in natural language about technical concepts.
Absence policy#
Family emergencies and illness are excused absences, as per UCSD policy. Please do not come to class if you have active symptoms (instead, please rest!). In general, absences will have a direct impact on your ability to learn the skills presented in this course as well as your participation grade.
Life happens. That being said, I really care about you, and your well-being. I know that life happens, and that sometimes you simply can’t be in class or turn in an assignment on time. There may also be times when I am unable to make it to class for a given reason, and I will ask for your grace and understanding then as well. Please, prioritize your well-being in graduate school and use this class as a way for you to learn skills that will be useful for your career (versus focusing on passing the requirements for a grade).
OSD Accommodations#
Any student with a documented disability will be accommodated according to University policy. For details, please consult the Office of Students with Disabilities (OSD): http://disabilities.ucsd.edu. If you require accommodation for any component of the course, please provide the instructor with documentation from OSD as soon as possible. Please note that accommodations cannot be made retroactively under any circumstances.