📖 Syllabus

Table of contents

1. About
   1. Acknowledgements
2. Course Meetings
   1. Office Hours
   2. Lecture
   3. Discussion
   4. Quizzes
3. Getting Started
   1. Technology
   2. Syllabus Check
   3. Welcome Survey
4. Communication
5. Readings
   1. Textbooks
   2. Course notes
6. Assignments
   1. Daily Checks
   2. Lab Assignments
   3. Homework Assignments
7. 📝 Assessments
   1. Exams
   2. Quizzes
8. 📆 Weekly Schedule
9. 🙋 Participation
10. 💯 Grades
    1. Letter Grades and Incompletes
11. Academic Integrity
    1. What do we mean by “cheat”?
    2. Generative AI Usage:
12. Accomodations
13. ❓ Waitlist FAQs

About

Welcome to DSC 152: Applied Statistical Data Analysis and Inference, at UC San Diego! This course is aimed at developing skills for the analysis of real data using exploratory tools and statistical inference methods. It covers basic concepts of statistical summaries and graphics, estimation, testing and regression, with an emphasis on real data analysis.

Prerequisites:

• DSC 80 or DSC 80R
• SE 125 or ECE 109 or ECON 120A or MAE 108 or MATH 180A or MATH 183 or MATH 186

Acknowledgements

I am grateful for multiple conversations with Drs. Justin Eldridge and Armin Schwartzman while preparing this course.

Course Meetings

Office Hours

We will hold the following weekly office hours this quarter:

• Mondays 2-3pm (Valerie)
• Mondays 3-4pm (Lucas)
• Mondays 4-5pm (Aditya)
• Thursdays 12:30-1:30pm (Peter)
• Fridays 12:30-1:30pm (Arunima)

During WEEK 1 ONLY, Lucas and Aditya’s office hours will be on Friday 4/3. All office hours will be in HDSI Rm 355 (tentative, still confirming).

The room is large enough that you are welcome to come there and work on DSC 152 assignments even if you do not have specific questions ahead of time, but want to be in the presence of staff for any help you may need as you work.

Lecture

There are two lecture sections:

• Section A: TTh 8:00-9:20am in HSS 1330.
• Section B: TTh 9:30-10:50am in HSS 1330.

In-person attendance is never required, but your regular attendance is STRONGLY encouraged, as this will give you the opportunity to ask questions, answer ungraded poll questions, and follow along with coding exercises interactively. Additionally, after each lecture, you will be required to turn in a “Daily Check” assignment (more details below). We will essentially cover the entire answers to these in class, so if you have attended lecture that day, you simply need to submit it; if you have to miss class, it will still be doable, but with a bit more effort.

You may always attend either lecture section regardless of which one you are registered for.

Lectures will be podcasted. Podcast recordings will be available online at podcast.ucsd.edu within a few hours.

Discussion

There are two discussion section times:

• Wednesdays 3:00-3:50PM in CENTR 216.
• Wednesdays 4:00-4:50PM in CENTR 216.

Discussion sections will occur each week at these times. For three of the weeks, a quiz will be administered (see below). Otherwise, discussion sections will consist of some structured material (particularly in the first few weeks), or review / extra office hours. See the schedule on the home page for more details.

With the exception of quiz days, you may attend either discussion section time of your choosing on any given week, without prior approval from us.

Discussion sections will not be podcasted.

Quizzes

On three Wednesdays throughout the quarter, we will have quizzes during the discussion section times:

• April 22nd (Week 4)
• May 13th (Week 7)
• June 3rd (Week 10)

These will take place during the discussion section times listed above. Based on your responses to the Welcome Survey, we will assign you to either the 3pm or 4pm section time for each quiz. On these weeks, you MUST attend the discussion section time that we assign you to take your quiz.

You will have the entire 50 minutes for each quiz. There will be no other material or activity during discussion sections on the day of a quiz.

Getting Started

Make sure to complete the four items listed below by Wednesday, April 1st at 11:59PM. If you join the course late, these items are due at 11:59PM the day after you join the class.

1. Join Campuswire (join code: 1733).
2. Check if you can access Gradescope. If not, make a post on Campuswire to “instructors & TAs” with your name, PID, and email address, then we can add you so you can submit assignments.
3. Read the syllabus and course website and complete the Syllabus Check on Canvas/Gradescope.
4. Fill out the Welcome Survey on Canvas/Gradescope.

Technology

If you do not have access to a computer, UCSD has a Laptop Lending Program which may be helpful, but you should also contact us if you have any concerns about access to technology. It is recommended that you bring a laptop to every class session you attend. All course content will be linked from this website, but there are a few additional platforms that you’ll need to access:

• Campuswire: We’ll be using Campuswire as our course message and discussion board. More details are in the Communication section below. If you didn’t already get an invitation, join here (join code: 1733).

• Gradescope: You will submit all assignments to Gradescope. This is where all of your scores will live as well, but they will also be synced to the gradebook in Canvas. You will be automatically added to Gradescope about 24 hours after enrolling in the course. If you need to submit assignments before then, please make a post on Campuswire to “instructors & TAs” with your name, PID, and email address.

• DataHub: Like in DSC 10, we will use DataHub (datahub.ucsd.edu), but for R instead of Python.

Make sure you can access all three sites.

Other than for gradebook syncing to Gradescope, we will not be using Canvas this quarter. Please do not contact the staff through Canvas – we won’t be able to read it! Use Campuswire for all course communications.

Syllabus Check

To demonstrate that you have read and understood the policies on the syllabus and course website, you’ll be asked to complete a Syllabus Check (on Canvas/Gradescope), which is a short quiz about the information contained on the syllabus and course website.

You must complete the Syllabus Check before the deadline with a score of 100% to earn credit. However, you may modify your answers after receiving your score, so please resubmit as necessary in order to receive the required score.

If you have questions about any course-related policies in the future, always refer to the syllabus and course website first!

Welcome Survey

Please fill out the short Welcome Survey on Canvas/Gradescope at the start of the quarter. This is required of all students.

Communication

This quarter, we’ll be using Campuswire (join code: 1733) as our course message board.

If you have a question about anything to do with the course — if you’re stuck on a problem, want clarification on the logistics, or just have a general question about statistics — you can make a post on Campuswire. If your post includes any part of your solution to a problem (e.g. code), please make your post private to “instructors & TAs”; otherwise, please make your post public so that other students can benefit from the interaction. You can also post anonymously if you prefer. Course staff will regularly check Campuswire and try to answer any questions that you have. You’re also encouraged to answer a question asked by another student if you feel that you know the answer – this is a great way to strengthen your understanding of the material (we will monitor this and modify any answers that are not quite correct!)

Please use Campuswire instead of email, as this helps us keep all course-related communication in one place. In particular, make your post public if you can, but if it contains code or personal information, make your post to “instructors & TAs” (do NOT send DMs to individual staff members as this will likely cause delays in getting a response).

Readings

Our readings will come from several free online sources. As this course has been created and tailored for the specific needs of our Data Science majors, there is not one single textbook that fits the course. As such, we will be utilizing excerpts from a variety of textbooks / course notes. The specific chapters/sections mapping to each lecture are linked on the main page, but here are each of the texts in their entirety. You are not responsible for anything that is in these texts that is not covered in lecture; this is here for your reference only.

Textbooks

• ModernDive - Chester Ismay, Albert Kim and Arturo Valdivia
• Handbook of Regression Modeling in People Analytics - Keith McNulty
• Time Series Analysis With R - Nicola Righetti
• R Cookbook - James (JD) Long, Paul Teetor

Course notes

• PSU Applied Statistics Course Notes - Penn State Department of Statistics
• Statistics and Predictive Analyics Course Notes - Jeff Webb, University of Utah
• Data Handling, Visualization and Statistics Course Notes - Owen R. Jones, University of Southern Denmark

Assignments

Daily Checks

After each lecture, a brief assignment will be due to Gradescope. These assignments will typically consist of short conceptual questions, coding, or a mix of both. They will be based directly on content from that day’s class, and should be very quick and straightforward if you were in class. If you miss class, it should still be possible to do the assignment, but it may take a bit more time and effort.

I recommend turning these in as soon after class as possible (or even during class), but they will be due at midnight on the day of class. They will be scored based on a “good-faith effort” out of 1 point each. Your lowest two scores will be dropped. Late submissions will not be accepted unless there are extenuating circumstances such as a serious prolonged illness; reach out to “instructors & TAs” on Campuswire if necessary and these will be handled on a case-by-case basis (do not email or even send direct messages on Campuswire to individual staff members; a response will likely be delayed, or even worse, your message may get completely buried, if you do this).

Lab Assignments

Lab assignments in this course will look a lot like DSC 10 labs, but here they are designed to teach you R rather than Python.

As in DSC 10, autograder tests for labs will be visible to you as you complete the lab, which check to make sure that your answers are correct. If you complete the assignment such that all the tests pass, you’ll get a perfect score!

To submit a lab, follow the instructions in the assignment to upload your notebook to Gradescope, which will run automated tests and assign your score. You should verify that all of your test cases pass on Gradescope before the deadline. Lab assignments will usually be due on Mondays at 11:59PM, though you should refer to the homepage of this website for the most up-to-date schedule. We will release lab assignments roughly a week before they’re due. Your lowest lab score is dropped from your grade calculation at the end of the quarter.

Labs must be completed and submitted individually, but we encourage you to discuss high-level approaches with others. See the Academic Integrity Policies section for more details.

Homework Assignments

There will be three homework assignments, designed to give you a more real world experience of statistical workflows using R Markdown. You will utilize the skills you have learned in approximately the 3 prior labs and 3 weeks of lecture, to carry forward a statistical workflow from start to finish on a real dataset. Each homework will be due on a Thursday.

Like labs, homeworks must be completed and submitted individually, but we encourage you to discuss high-level approaches with others. See the Academic Integrity Policies section for more details.

📝 Assessments

Exams

This class has one Final Exam (and no midterm exam):

• Saturday, June 6th from 3:00pm to 6:00pm, location TBD.

The Final Exam will cover the entire quarter, with emphasis on material after Week 5 of the quarter. It will be held in-person and on-paper. You’ll be allowed to use one 8.5 by 11 inch page of double-sided handwritten notes, but no calculators, computers, or other resources.

If you have a conflict with the exam, please let us know right away via the Welcome Survey (in Canvas/Gradescope) to see if accommodations can be made.

Quizzes

There are three quizzes throughout the quarter, administered during your assigned quiz time on the following dates:

• April 22nd (Week 4)
• May 13th (Week 7)
• June 3rd (Week 10)

You’ll be allowed to use one 8.5 by 11 inch page of double-sided handwritten notes, but no calculators, computers, or other resources.

Your lowest score will be dropped.

We will not offer makeup quizzes. If you are sick, traveling, or otherwise need to miss a quiz, the dropped quiz is intended to take care of this. If you have extenuating circumstances that require you to miss more than one quiz, please reach out to “instructors & TAs” on Campuswire and we will handle this on a case-by-case basis.

You must attend quizzes at your assigned time that we will establish and notify you of after the Welcome Survey responses are in.

📆 Weekly Schedule

To summarize all of the events and deadlines, refer to this general weekly schedule. Please refer to the homepage of this website for the most up-to-date schedule of deadlines.

🙋 Participation

There will be 3 participation points available:

• 1 point for doing the Welcome Survey.
• 1 point for getting all answers correct on the Syllabus Check.
• 1 point for completing SETs (Student Evaluations of Teaching).

Your participation grade will be the number of points earned out of 3.

💯 Grades

The table below shows how your mastery of class material will be assessed and how grades will be computed:

Note that in each category, all assignments in that category will be worth the same amount, regardless of the number of points they are graded out of.

Letter Grades and Incompletes

We will use a standard scale for assigning letter grades:

A+ grades are given at the instructor’s discretion. If you are taking the course P/NP, you will receive a grade of P if you meet the criteria for a C- grade, otherwise you will receive a grade of NP.

Grades may be curved at the end of the quarter if appropriate.

If you have extenuating circumstances that prohibit your completion of coursework, you may be eligible for an Incomplete grade. Please reach out as soon as possible if it seems like this might be the case.

Academic Integrity

All students are expected to uphold UCSD’s standards of Academic Integrity. Essentially this just means please do not cheat. If you do cheat, we will enforce the UCSD Policy on Integrity of Scholarship. This means you will likely fail the course and the Dean of your college will put you on probation or suspend or dismiss you from UCSD. Students agree that by taking this course, their assignments will be submitted to third party software to help detect plagiarism.

What do we mean by “cheat”?

• On Daily Checks, Labs, and Homeworks, you may use any resources at your disposal, including Generative AI (more on that below), and discussing amongst each other. However, you MUST write up your own assignments, in your own words. Any submissions that are found to be duplicates of each other, or with clear egregious AI usage, will be considered violations of academic integrity.

• On quizzes and exams:

  • You may have your 8.5 x 11 double-sided cheat sheet
  • No calculators or any other devices whatsoever
  • Refrain from looking at other students’ quiz/exam

Generative AI Usage:

Generative AI / LLM tools such as ChatGPT or Claude have incredible capabilities and you are welcome to use them in this class. But, you must use them wisely and with caution. The first thing I want to mention is that the AI boom has put tremendous strain on our natural resources, including (but not limited to) freshwater. While I do not demand an AI-ban because of this, I just want to simply encourage everyone to be mindful of your usage of AI, as you would when you run the faucet.

The second thing to be aware of is that, even in 2026, they often give responses that are not completely correct. Even when they are correct, they will frequently present responses that are obviously AI-generated. If you choose to use generative AI to help you with your assignments, you are responsible for ensuring that the information it gives you is correct, and for turning it into your own words.

You are even welcome to ask generative AI to give you R code as needed, but again you are responsible for making sure that it works as it should. In my experience, the code that an LLM gives will often look plausible, but there will be errors in it that make it incorrect (and sometimes drastically so). It is best used if you have a good idea of what needs to happen, give it your best shot in coding it on your own, but then ask AI once you are stuck. You will then be in a good position to fix whatever errors that AI gives you when you ask it.

All usage of generative AI should be credited in your work. This includes Daily Checks, Lab Assignments and Homework Assignments. Any work that is determined to be AI-generated and not credited will have to be reported as an incident of academic dishonesty (see above). Additionally, even when generative AI is credited:

• All text should be re-written in your own words.
• Any code that is AI-generated should be checked and fixed as needed.
  • If it actually works as is and you are as certain of it as you can be, then you may use it directly without modification; however, either way, you should add your own code comments to the code, in your own words.

Additionally, for your own learning, I HIGHLY recommend that you re-type any AI-generated code yourself as opposed to simply copy/pasting it. As tedious as this may be, I strongly believe that it will be helpful in actually learning how to code in R on your own, as re-typing it gives you a chance to internalize the code that was given. It also gives you a more obvious opportunity to check through the AI-generated code to make sure that it actually makes sense and is correct. And again, be sure to CREDIT the generative AI that produced the code.

One final note: this is hopefully obvious, but asking an LLM to do an entire assignment for you will be considered a violation of academic integrity and will have to be reported.

Accomodations

From the Office for Students with Disabilities (OSD):

OSD works with students with documented disabilities to review documentation and determine reasonable accommodations. Disabilities can occur in these areas: psychological, psychiatric, learning, attention, chronic health, physical, vision, hearing, and acquired brain injuries, and may occur at any time during a student’s college career. We encourage you to contact the OSD as soon as you become aware of a condition that is disabling so that we can work with you.

If you already have accommodations via OSD, make sure that we receive your Authorization for Accommodation (AFA) letter at the start of the quarter so that we can make arrangements for accommodations. The Data Science OSD Liaison can be reached at dscstudent@ucsd.edu.

❓ Waitlist FAQs

I am on the waitlist, but I really want to get into the course. Can you let me in?

Sorry, but instructors are not able to enroll students in classes. There is nothing we can do let you into the course.

I am on the waitlist, so how can I keep up with the course?

Waitlisted to students may attend lecture and discussion, space-permitting, and can also watch podcast recordings. You can (and should) still submit assignments if you are on the waitlist. If you get off the waitlist and are able to join the class, you will not get any extensions on past-due assignments.

Waitlisted students should have access to DataHub to work on assignments. You may need to add yourself to some course tools; see the Getting Started section of the syllabus. If you need access to Gradescope, send a private message to the instructional staff on Campuswire with your name, PID, and email address.

What are my chances of getting off the waitlist?

The instructional staff is not equipped to answer this question. Many questions about enrollment are answered here. Please direct your questions about enrollment to DSC advising. You can send an email to dscstudent@ucsd.edu, send a message through the Virtual Advising Center, or stop by drop-in advising hours. In short, seats in the class open up when students drop the class, which can be hard to predict.

I have been added to Gradescope, Campuswire, and other course tools. Does this mean I am off the waitlist?

No. Students on the waitlist were added to all course tools, so they can complete assignments while they are on the waitlist. Check WebReg if you are not sure of your enrollment status.