Course Syllabus

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.

There is no required textbook for this course. Access to required reading, labs, and other course materials will be posted on the course website at yymao.github.io/phys7730. 

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. 

Your grade will be determined from all of the components, with the following weights:

5-point Grading Scale

All the course components will be graded on a 5-point scale, based on the following standards. 

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. 

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, and you should submit your answers on Canvas.

Late submission within a week (regardless of how late you were within the week) will receive a 1-point deduction (on the 5-point scale). Late submission beyond a week will receive no points.

Each pre-lecture reading assignment will be weighted equally. The lowest 3 pre-lecture reading assignment will be dropped.

Each pre-lecture reading assignment should take about 1-2 hours to complete. 

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. Once you complete the lab (which is the homework assignment), you should submit it on Canvas.

Late submission within a week (regardless of how late you were within the week) will receive a 1-point deduction (on the 5-point scale). Late submission beyond a week will receive no points.

Each homework assignment (lab) will be weighted equally. The lowest 3 homework assignments will be dropped.

Each homework assignment (lab) should take about 2-3 hours to complete. 

During the class, there will be several group discussions and activities. You will be in a group of 2-3 people to discuss assigned questions or work on certain problems. Your participation in these discussions and activities will constitute a significant part of your final grade. For each meeting, your participation will be graded based on 5-point scale mentioned above, which depends on your level of engagement and the quality of engagement.

It's important to note that the quality of engagement is not graded based on how much you spoke in class nor on the correctness your contribution was. 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.

At the end of each meeting, you can also submit your in-class discussion notes to Canvas (or to me directly). These notes are optional, but they can be useful when you have written ideas or perspectives that you did not have a chance to share in class. They will be considered when I grade the in-class participation. 

The lowest 3 scores you have will be dropped. 

I will record each class in case a student has to miss the class and wish to watch the recording later. However, I will only post the recordings if requested (because processing the recordings takes quite some time). Please also understand that, as the class is designed to be very interactive, with many discussions and only intermittent lectures, the recordings will not reproduce the in-person learning experience. If you miss a class but request and watch the recording, you will receive 2 points for that class.

Engaging in discussions and class activities is important for reaching our learning objectives. However, every person has a different level of comfort when it comes to engaging in these group activities. If you find it uncomfortable or difficult to engage in in-class discussions, please contact the instructors so that we can discuss possible accommodations. 

Discussion Guidelines

When we have group discussions in class, please follow these guidelines: 

• Listen respectfully and actively, with an aim to understand others’ views.
• Allow everyone the chance to speak.
• Point out and compare the differences in the views and ideas that have been shared.
• Do not criticize individuals. Avoid blame, speculation, and inflammatory language.
• Understand that intent does not equal impact, and we are accountable for our words and their impact.
• Use writing and drawing to facilitate the conversation.

In addition, watch out for the group dynamics and how you are making progress to answer the questions. Here's some useful tips:

• If all group members find the discussion questions straightforward, that's great. Go beyond what was asked and come up with your own follow-up questions! 
• If all group members find it difficult to answer the discussion questions, start by collecting what questions or confusions you have, and then raise your hand to ask Yao for help! 
• If each group member appears to have different levels of understanding, that's OK too. If you feel you may be a bit slower, ask your group partners to explain things to you -- this really helps everyone learn better! If you feel you may have a better understanding, be willing to teach and to be challenged -- you will learn something new too!

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 the presentation on the mock exam questions will be graded with the following aspects. Each aspect will be graded on the 5-point scale mentioned above. All four aspects have equal weights, and in total they account for 10% of your course final score. 

• 10% - attempting the mock exam
• 25% - clearly laying out how you approach the problem(s)
• 25% - solving the assigned problem(s)
• 20% - validating, interpreting, and discussing the solutions you reached
• 20% - engaging in the Q&A (the presenter will not be evaluated based on whether they can provide accurate answers)

Up to 10% of the presentation total score may be adjusted based on the contribution statements. 

Each student will do a 5-minute presentation, with their choice of one of the following topics:

• Find a statement from a scientific paper or a news article that involves a statistical test or estimation. Describe, examine, and comment on the method used. (You can find science news articles on phys.org, sciencenews.org, sciencedaily.com)
• Describe a problem in their research work that involves a statistical test or estimation. Lay out a potential method that can be used for the said problem.
• Pick one exercise question from a section we have read in Wall and Jenkins, “Practical Statistics for Astronomers”, and answer it. 

Prepare to speak for about 4 minutes, and no more than 5 minutes. You can prepare one slide to aid your presentation. 

This presentation will be graded based on 5-point scale mentioned above. 

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, Copilot, Gemini, etc, you must document your use of AI in your submission.  In particular, you need to specify which answers were assisted by AI. You are also responsible for validation the AI output, and you should document your validation effort as part of your submission. You also need to be able to explain your answers without relying on AI.

If you are considering using AI, you are encouraged to first take a look at the U's Student Guide to Generative Tools.