Course Policies - Spring 2024#
Warning
You are looking at the course policies for Spring 2024. The course policies for Spring 2025 will be similar, but are not identical! You can take a look at the Spring 2025 core syllabus [PDF] here.
Important
The official course policies for PHYS/ASTR 7730 (Spring 2024) are posted on Canvas (login required). Below is a copy of the policies for your convenience. If there is any discrepancy between this page and the Canvas page, the latter takes precedence.
Note
The following sections detail the plan, structure, requirements, and expectations of this course. They are meant to serve as an outline and guide for our course. Please note that these course policies may be adjusted. Any adjustment will be communicated to you in a timely fashion.
Learning Objectives#
This course is a graduate-level course focusing on a selection of widely applicable statistical and computational methods in physics and astronomy. The main learning objectives of this course are:
Being able to identify statistical or computational methods that are potentially applicable for a research problem in physics and astronomy;
Being able to design or set up statistical tests, computational models, and/or simulations to tackle the said problem;
Being able to interpret the results, assess the method’s effectiveness, and revise the method as needed;
Being able to examine and experiment with other statistical or computational methods that may not be covered in this class.
Please note that we will use Python as the programming language for demonstration and use many examples in physics and astronomy. Students are assumed to be comfortable in programming and have an introductory-level knowledge in physics.
Course Materials and Schedule#
There is no required textbook for this course. Access to required reading, labs, and other course materials will be posted on the course website (this site).
Course Components and Grading#
This course has multiple components: lectures, in-class discussions, pre-lecture reading, homework, mock exam, and presentations — each of these is designed to help you learn the materials better. You are expected to participate in/work on all of the course components. However, if there is anything that is preventing you from participating in the coursework or learning effectively, please talk to the instructor so that we can find creative solutions.
If you would like to request accommodations (either with or without a letter from the Center for Disability & Access), please reach out to the instructor at your earliest convenience.
Your grade will be determined from all of the components, with the following weights:
Component |
Grading Weight |
|---|---|
Pre-lecture reading |
20% |
Homework (completing labs) |
20% |
In-class participation & discussion |
40% |
Midterm mock exam & presentation |
10% |
Final presentation |
10% |
See the respective sections below for details of how each component is evaluated.
Letter Grade Policy#
The table below lists the “guaranteed” letter grade thresholds – that is, if your final numerical score is higher than a listed threshold in the table, you are guaranteed to receive at least the corresponding letter grade. These thresholds may be lowered, but will not be raised.
Letter Grade |
Maximal Thresholds |
|---|---|
A |
94% |
A- |
90% |
B+ |
86% |
B |
82% |
B- |
78% |
C+ |
72% |
C |
66% |
C- |
60% |
Pre-lecture Reading#
Pre-lecture reading will be assigned via Canvas. You will need to complete the assigned reading before each lecture, and submit your answers to the accompanying questions on Canvas. These accompanying questions may:
ask you to briefly summarize what you read;
ask you if you have any questions about what you read;
ask you simple questions that are related to what you read.
The pre-lecture reading assignment for a class will be due at noon on the day that class meets. That is, a reading assignment for a Monday class will be due at noon on that Monday, 3 hours before the class meets. I will typically announce the reading assignment one week in advance. The reading assignments be available on the course website (this site), and you should submit your answers on Canvas.
Each pre-lecture reading assignment will be graded on a 5-point scale, with 4 points on completeness, and 1 point on quality. In other words, if you complete all the questions, you will receive at least 4 points, and can receive up to 5 points depending on the quality of your answers. You may receive less than 4 points if you do not complete all the questions.
Late submission within a week (regardless of how late you were within the week) will receive a 25% deduction on the points you receive. Late submission beyond a week will receive no points.
Each pre-lecture reading assignment will be weighted equally. The lowest 2 pre-lecture reading assignment will be dropped.
Each pre-lecture reading assignment should take about 1-2 hours to complete.
Homework (Labs)#
A lab component will be included in each class. These labs involve hands-on practices on the topics being covered. Students will have time in class to start working on those labs and to ask questions. However, given the limited class time, in most cases you may not have enough time to fully complete the labs.
The homework assignments of this course will simply be completing those labs, and there won’t be additional homework assignments.
Each homework assignment (lab) will be due in one week at noon. That is, a lab from a Monday class will be due at noon on the following Monday after the class. The lab will be announced in class and be available on the course website (this site). Once you complete the lab (which is the homework assignment), you should submit it on Canvas.
Each homework assignment will be graded on a 5-point scale, with 4 points on completeness, and 1 point on quality. In other words, if you complete the entire lab, you will receive at least 4 points (even if you made mistakes), and you can receive up to 5 points depending on the quality of your answers. You may receive less than 4 points if you do not complete the entire lab.
Late submission within a week (regardless of how late you were within the week) will receive a 25% deduction on the points you receive. Late submission beyond a week will receive no points.
Each homework assignment (lab) will be weighted equally. The lowest 2 homework assignments will be dropped.
Each homework assignment (lab) should take about 1-2 hours to complete.
In-class Participation and Discussion#
(Updated Jan 10th)
The participation in the discussion and lecture will constitute a significant part (40%) of your final grade. For each meeting, your participation will be graded on a 3-point scale with the following rubrics:
1 point for timely and full attendance (fractional points may be given for partial attendance)
1 point for fully engaging in the discussion / class activities (fractional points may be given for partial engagement)
1 point for the quality of engagement (see below for details)
Under these rubrics, if you show up in class (for the full period) and are fully engaged, you will get a minimum of 2 points, and up to 3 points, for each class.
The semester will be divided into 4 “grading periods”, with each grading period contributes 10% of the of your final grade. Excluding mock midterm and final presentations, each grading period will have 5 meetings (except for the first grading period, which has 6). You can earn a maximum of 10 points in each grading period. Here are the list of grading periods and their meeting dates:
Period 1: Jan 10, 17, 22, 24, 29, 31 (6 meetings)
Period 2: Feb 5, 7, 12, 14, 26 (5 meetings)
Period 3: Mar 11, 13, 18, 25, 27 (5 meetings)
Period 4: Apr 1, 3, 8, 10, 22 (5 meetings)
For example, consider a grading period that has 5 meetings. If you showed up and fully engaged in all 5 meetings, but did not get any quality of engagement points at all, you would still earn the full 10 points (2*5) for that grading period. If instead you got 2.5 points for 4 of the 5 meetings but missed the last meeting, you would still earn the full 10 points (2.5*4) for that grading period.
Under this scheme, no meetings will be “dropped”. You will just collect points until you reach 10 points in each grading period.
Quality of Engagement#
You will earn a higher score on the quality of engagement when you come prepared! For example, spending time thinking about the questions in the pre-lecture reading assignments can help you engage better. It’s important to note that the quality of engagement is not graded based on how much you spoke in class or how correct you were. It is graded based on how much you contribute to the learning experiences of yourself, other students, and the instructor (yes, the instructor learns from you all too!). High quality engagement usually prompts further reflection, thinking, and discussion.
Mock Exam and Presentation#
The mock exam will a timed, open-book exam that will take place in class. It is a “mock” exam because it will be graded on attempt only. After the mock exam, students will be split into groups, and each group will be assigned one question to solve (open book, open discussion). On the group presentation day, each group will present their solutions to the class.
After the presentation, each member of the group will submit a brief (<300 words) contribution statement that (1) summarizes their contribution in the group, and (2) comment on how their group mates participate.
The mock exam and presentation will be graded with the following rubric, with a total of 10 points:
2 points for attempting the mock exam
2 points for showing up and giving a complete presentation on the assigned problem
2 points for clearly laying out how you approach the problem
1 point for solving the assigned problem(s) correctly
1 point for validating, interpreting, and discussing the solutions you reached
1 point for engaging in the Q&A
1 point based on the contribution statements
Final Project Presentation#
Each student will choose a specific problem, preferably a problem they encounter in their research work, and apply a statistical or computational method to tackle the chosen problem. The chosen method does not need to be a method that this course has specifically covered, but should be closely connected to the material covered in this course.
Each student will do a 5-minute “pre-presentation” to describe the chosen problem and the method they plan to use. In the final presentation week, each student will do a 10-minute presentation on their results.
Your presentation will be evaluated as follows – with a total of 10 points:
2 points for giving a complete pre-presentation
1 point for clearly describing the chosen problem
2 points for giving a complete final presentation
2 points for clearly describing why and how you implement your chosen method to the chosen problem
1 point for summarizing the results you obtained
1 point for validating, interpreting, and discussing the results you obtained
1 point for engaging in the Q&A
Policies on Collaboration and the Use of AI Tools and Other Resources#
With the exception of the exam part of the mock midterm exam, you can discuss and collaborate with other students in this class. However, each of you must write your own answers/code independently. For example, you can discuss how to implement something, but you must carry out the implementation separately.
If you have an extensive discussion with other students on a problem, to the extent that your answers will likely be similar even when you implement separately, you must specify in your submission that your answer comes from a collaboration with [student names].
If you consult or have discussion with any other person outside the class on a problem, and the consultation or discussion influences your answer, you should always specify so in your submission.
You can also use online resources. Generally you should cite the resources you consulted. If you are using or modifying the code example from the official documentation of a package for the purpose of using that package, it is ok to omit the citation. When in doubt, cite your sources.
If you use any online resources that are not publicly available (for example, contents behind a paywall or requiring login), you must provide a copy of the used resources in addition to citing them.
If you use any generative artificial intelligence (AI) tools, such as ChatGPT, Brad, etc, you must specify in your submission: the tool you used, the prompt you used, and the original response the AI tools replied (in addition to your final submission, which is likely a modification of the original response).
University Policies#
Please refer to the Canvas page (login required) for university policies.