update faqs

faq.md

@@ -3,23 +3,73 @@ layout: page
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 title: FAQs
 description: >-
-    Frequently Asked Questions
+  Frequently Asked Questions
 ---
 
 # Frequently Asked Questions
 
-**Q: What is the required knowledge for this course?**
+## Course Requirements and Prerequisites
 
-**A:** This course assumes prerequisite knowledge as in the DSC 202 (Data Management for Data Science)
+**Q: What are the prerequisites for this course?**
 
-<!-- 
+**A:** You need to have completed either:
+
+An ML algorithms course (like CSE 151) AND
+Either a database systems internals course (like CSE 132C) OR an operating systems course (like CSE 120)
+Alternatively, DSC 102 satisfies both prerequisite requirements. Industry experience may be considered as a substitute with instructor approval.
+
+**Q: I have relevant industry experience but haven't taken the prerequisite courses. Can I still enroll?**
+
+**A:** Yes, substantial project or industrial experience can be considered as a substitute for prerequisites, but this requires the instructor's explicit consent. Email the instructor directly to discuss your background and eligibility.
+
+## Course Content and Structure
+
+**Q: What is the main focus of this course?**
+
+**A:** This is a research-based course that explores data systems for machine learning, combining elements of ML/AI, data management, and systems. It focuses on systems that power modern data science applications like enterprise analytics, recommendation systems, social media analytics, and generative AI.
+
+**Q: Who is this course designed for?**
+
+**A:** The course is primarily tailored for:
+
+MS students
+PhD students
+Advanced undergraduate students
+who are interested in systems for scalable data science and ML engineering.
+
+## Course Support and Communication
+
+**Q: What is the best way to get help in this course?**
+
+**A:** Your best avenues are to go to office hours held by the course staff, or to ask questions on Ed. Course staff will be monitoring Ed frequently and will try to answer your question quickly and thoroughly.
+
+## Grading and Assessment
+
+**Q: Where will our grades for assignments be displayed for the course?**
+
+**A:** Grades will be displayed on Gradescope for the written and autograded portions for all assignments (homeworks, labs, projects, and exams). For homeworks and projects, your total grade is the sum of the autograded portion and the written portion.
+
+**Q: I passed all the tests when doing my homework/project, but still got points off on the autograded portion of the assignment. Why is this happening?**
+
+**A:** The homeworks and projects have hidden tests that are not visible to students while they do the assignment. In order to pass these hidden tests, you must test your code yourself and make sure your answer is correct. Our tests are not always comprehensive.
+
+**Q: I noticed a mistake in the grading of the written portion of my homework. How can I get this fixed?**
+
+**A:** To get this fixed, you must submit a regrade request via Gradescope before the regrade deadline. This is known as the regrade request window. We unfortunately will not accept any regrades after the window has closed. All regrade deadline dates are posted on the same Ed post that releases the assignment grades and solutions.
+
+
+<!-- **Q: What is the required knowledge for this course?**
+
+**A:** This course assumes prerequisite knowledge as in the DSC 202 (Data Management for Data Science) -->
+
+<!--
 **Q: I don't know much about computer systems. Is that okay?**
 
-**A:** The intended audience for the course are data scientists, and not systems engineers! It is completely fine to not have a background in computer systems. The course material will quickly brush up on computer systems fundamentals and then dive into different aspects of data systems and scalable data processing. The assignments will test your ability to _apply_ scalable data programming principles as a _data scientist_, and not on the internals of, say, MapReduce or task scheduling. 
+**A:** The intended audience for the course are data scientists, and not systems engineers! It is completely fine to not have a background in computer systems. The course material will quickly brush up on computer systems fundamentals and then dive into different aspects of data systems and scalable data processing. The assignments will test your ability to _apply_ scalable data programming principles as a _data scientist_, and not on the internals of, say, MapReduce or task scheduling.
 
 **Q: This is my first time being a scribe. I'm not too sure what my role is.**
 
-**A:**  First off, we're glad that you're taking some time to understand our expectations! The expectations from a scribe are simple: the scribe should faithfully capture the professor's explanations in the lecture. We expect the notes to have the same structure/main sections as the slides, and many similar wordings as well. Additionally, the notes should also have helpful examples given by the professor in the class, additional context for different topics when needed, etc. The TAs will assess your notes based on the contents in the slides.  
+**A:**  First off, we're glad that you're taking some time to understand our expectations! The expectations from a scribe are simple: the scribe should faithfully capture the professor's explanations in the lecture. We expect the notes to have the same structure/main sections as the slides, and many similar wordings as well. Additionally, the notes should also have helpful examples given by the professor in the class, additional context for different topics when needed, etc. The TAs will assess your notes based on the contents in the slides.
 
 You should not, however, try to rearrange the content as you see fit, or add in material from the additional readings provided. The scribe notes do not have to be a self-contained document on the topic - we're only looking for you to capture what was taught in class.