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📓 Syllabus


Table of contents

  1. Overview
  2. Quarter 1 (DSC 180A, Fall 2023)
    1. Getting Started
    2. Course Components
      1. Lecture (Methodology)
      2. Discussion (Domain)
      3. Office Hours
      4. Remark on How the Course is Split
    3. Assignments and Grades
      1. General Rubric
      2. Letter Grades
    4. Collaboration Policy and Academic Integrity

Overview

Welcome to the capstone program! The capstone program is a two-quarter sequence (Fall 2023 and Winter 2024) in which you will be mentored by a faculty or industry expert in their domain of expertise. By the end of Quarter 2, you will design and execute a project from that domain in teams of 2-4. You can see the projects from last year here, and from all prior years here.

At a high level, here’s how the capstone program is organized:

  • In Quarter 1 (DSC 180A), you gain background information in your mentor’s domain by working on a guided project (e.g. a paper replication). By the end of Quarter 1, not only will you have you completed a guided project (your “Quarter 1 Project”), but you will write a proposal for a more independent project (known as the “Quarter 2 Project”, or the capstone project).
  • In Quarter 2 (DSC 180B), you execute the Quarter 2 Project you proposed at the end of Quarter 1.

Throughout Quarter 1, there is a “methodology” component that supplements your knowledge of your domain with best practices in software engineering, project management, and effective communication.


Quarter 1 (DSC 180A, Fall 2023)

Getting Started

Before the quarter begins:

  • Confirm the date and time of your discussion section on the Enrollment, as it may have been updated since you last checked.
    • Note that sections begin the week of Monday, October 2nd (Week 1). You must attend your discussion section each week.
    • If your section is being held on Zoom, your mentor should reach out to you before your first section with the link.
  • Note that there is only one live lecture this quarter, on Monday, October 2nd. All other methodology instruction will be provided asynchronously.
  • Make sure you can access the following sites; email Suraj if you can’t:
    • Gradescope, where all assignments will be submitted.
    • Ed, the discussion forum we will use for methodology. All class-wide announcements will be sent here!

Note that we will not be using Canvas at all this quarter.


Course Components

As mentioned in the Overview, the primary goal of Quarter 1 is to get you acquainted with your mentor’s domain. The main deliverable in Quarter 1 is the “Quarter 1 Project”, which you will work on gradually throughout the quarter by completing the tasks that your mentor assigns you. The Quarter 1 Project is due at the end of the quarter, but a checkpoint is due on Monday, November 5th. to make sure you’re on track.

You will complete your Quarter 1 Project either individually or in groups, depending on your mentor’s preferences. Your Quarter 1 Project will serve as a foundation for your Quarter 2 Project Proposal, which you will submit on Monday, December 11th. The Quarter 2 Project will be completed in groups of 2-4 throughout Quarter 2.

Note: You may not get to choose who your “partners” are, as that may be up to your mentor; like in industry or academia, groups will be formed using a variety of factors, including academic background, mutual interests, and perhaps a little randomness.

The subsections below describe how the course operates.

Lecture (Methodology)

Lectures are focused on methodological skills that can apply to all domains. In lectures, we will cover best practices with software engineering for data science and project management (see the course homepage for a full listing in Quarter 1).

Based on feedback from prior iterations of the capstone, we’ve decided to deliver methodology lectures as lecture notes that you read outside of class and discuss with course staff during synchronous sessions. Specifically:

  • Each Tuesday, we will post a “lesson” on the course homepage. A lesson will contain all of the methodology content you need to learn for the week. Read each lesson on your own. (We will make an announcement on Ed when we release new lessons).
  • Many methodology lessons will have an accompanying “methodology assignment”, due the following Monday. See the Assignments section for more details.
  • Suraj and the methodology TAs will hold office hours throughout the week to answer any questions with methodology content (or even your domain work, for that matter).

Note that on Monday, October 2nd, we will hold a “traditional” introductory lecture during the scheduled lecture slots, and you should plan to attend. The first methodology lesson will be released on Tuesday, October 3rd.

We will not be using the “lab” component of the course that appears on WebReg, nor will we be using the “lecture” component after Monday, October 2nd.

Discussion (Domain)

Each week, you will meet with your domain mentor for an hour in discussion section. You can see the time and location of your discussion meeting on the Enrollment page. Attendance in discussion section is mandatory, and you must notify your mentor in advance if you can’t make it in a particular week. (If you have a permanent time conflict with your discussion section, you should switch to another domain.)

Each week, your domain mentor will assign you a combination of readings and tasks to complete, along with “participation” questions to answer to ensure that you’ve engaged with the material. You must complete these participation questions 24 hours before discussion, as your responses to them will drive the class discussion. Your mentor may provide you with specific participation questions to answer; if not, you should answer these “default” participation questions. You should complete the other tasks they assign you before discussion as well, though you may not have to submit them anywhere. Later in the quarter, you will brainstorm project proposals in discussion as well.

Note that discussion section will consist of discussion, not lecture. As such, if you do not ask questions in discussion section, no discussion will occur. To get the most out of the capstone program, you should actively participate in discussion section. In the workplace, you will often need to communicate with your coworkers and ask questions when you don’t understand things, and the same is true here.

Office Hours

There are two flavors of office hours:

  • Methodology office hours, held by the methodology (DSC 180A) course staff. Come to these office hours with questions on methodology lectures and assignments, and on how to apply methodology concepts to your domain work. See the Office Hours page on the course website for the schedule.
    • Note that different TAs have expertise in different areas; see the Staff page for a listing on each TA’s area of expertise.
  • Domain office hours, held by your domain mentor. Come to these office hours with questions on the readings or tasks your mentor assigned you or on your projects. Your mentor will tell you when these are.
    • You will be required to attend your mentor’s office hours at least three times throughout the quarter.

Remark on How the Course is Split

As is common in data science, you will likely find yourself as a bridge between domain specialists and (computing) methodology specialists. In this course, it is totally normal if your domain mentors do not know specifics of your code (or even know the language you are coding in!). You will have access to help from both methodology experts (in office hours and EdStem) and your domain mentor (in discussion section and office hours). As such, it is up to you to formulate your questions for the appropriate audience (methodology expert or domain expert) so that you can adequately communicate with them to solve the problems you are facing.


Assignments and Grades

The table below summarizes all the ways you will be assessed in Quarter 1.

ComponentDescriptionDueWeightGrading Notes
Methodology assignmentsAssignments that develop your software development and project management skills. Submitted individually.There will be at least 5. If there are more than 5, only your highest 5 will be counted in your methodology grade.10%2% each
Participation questions (default questions or mentor-provided)Weekly questions to keep you engaged with the material and to inform your mentor of class progress. Submitted individually.Weekly, 24 hours before discussion5%Graded for completeness by methodology TAs
Overall participationEngaging in conversation in discussion section is important for success in the capstone; as such, your mentor will assign you an overall participation grade at the end of the quarter.N/A5%Graded by mentors
Quarter 1 ProjectA chance to put together everything you’ve produced while learning about your domain. Submitted individually or in groups (up to mentor).Week 5 (checkpoint), Week 10 (final submission)65%50%: reports, graded by mentors (checkpoint + final)

15%: code, graded by methodology TAs (checkpoint + final; graded to ensure best practices are followed)
Quarter 2 Project ProposalProposal for final capstone project. Submitted in groups.Week 915%Graded by both methodology TAs (elevator pitch) and domain mentors (schedule + write-up)

Note that the table contains links to assignment descriptions; the Quarter 1 Project and Quarter 2 Project Proposal details are tentative, and won’t be finalized until they are officially released. We will make Ed announcements when these components are finalized.

General Rubric

In order to ensure consistent grading across such a diverse array of domains, we will utilize a coarse grading scheme with a clear rubric. This scheme will reflect broad checkpoints that you meet, and should help maintain focus on large, impactful things that you can improve on while reducing grading disagreements.

The grading scheme we will use for all assignments (other than for participation questions, which are pass/fail, and methodology assignments, which may have numerical scores) follows an A/B/C/F scale (without plus/minus), developed by Shannon Ellis:

GradeCriteria
A (4.0)Accomplishes the task accurately, completely, and clearly. Code is clear, effective, and efficient. Written component is concise, at the appropriate level, and correct. Oral component (when applicable) is effective and within the time limit.
B (3.0)Accomplishes the task well, but lacks some completeness or clarity. Code runs but lacks some aspect of clarity, effectiveness, and or efficiency. Written component is logical and generally correct, but lacks either clarity or accuracy. Oral component (when applicable) is moderately effective and/or slightly outside the time window.
C (2.0)The task is somewhat accomplished, but lacks significantly when it comes to completeness and clarity. Code present but does not accomplish the task up to the standards of a data science graduating senior. Written component lacks substantial clarity/correctness. Oral component (when applicable) significantly lacks effectiveness/clarity.
F (0.0)The task largely remains unaccomplished. Code lacks completeness, structure, and is unclear. Written component lacks required information to understand the work done. Oral component (when applicable) is nonsensical/unclear.

Letter Grades

Individual assignments will be graded on the A/B/C/F scale above, and your overall course grade will be determined by using the proportions listed at the start of this section. For the purposes of computing your course grade, A, B, C, and F map to 4, 3, 2, and 0. So for instance, if you earn:

  • full credit (A) on methodology assignments,
  • an A on participation questions,
  • a B on participation (as graded by your mentor),
  • an A on your Quarter 1 Project, and
  • a B on your Quarter 2 Project Proposal,

your “numerical” grade would be \(0.05 \cdot 4 + 0.05 \cdot 4 + 0.05 \cdot 3 + 0.7 \cdot 4 + 0.15 \cdot 3 = 3.8\).

You are guaranteed to earn at least the letter grade that your numerical grade converts to. For instance, a 3.7 is guaranteed to learn at least an A-, and a 2.0 is guaranteed to earn at least a C. When your numerical grade is between two letter grades, you are guaranteed to earn at least the lower letter grade; for instance, 3.8 is between 3.7 (A-) and 4.0 (A), so a 3.8 is guaranteed to learn at least an A-.

Note that at the end of Fall 2023 you will receive a grade in DSC 180A, and at the end of Winter 2024 you will receive a grade in DSC 180B; these are two separate courses, each worth 4 units.

With all of that said, in this course, you should not worry about your letter grade. The grades you receive on individual components of the course are meant to provide you with feedback on how to improve future submissions. To be successful in this course, you should strive to have engaging interactions with your domain mentor and to produce work that you are proud of. Nobody will remember whether you got an A- or a B in the capstone, but they will remember if you produce a stellar final project.


Collaboration Policy and Academic Integrity

In DSC 180, we expect you to work hard and engage with material that originates outside the academic walls. All ideas and work must be your own, that of your approved group, or properly cited. Act with integrity and don’t cheat.

In DSC 180 you are encouraged to use outside resources to help with your work. However, you must properly cite any concepts, writing, or code that originates from other sources. If you are unsure of whether something needs a citation, it’s best to:

  • consult the domain expert for your section,
  • follow the examples in course readings, and
  • place citations with relevant links in comments.

The following activities are considered cheating and ARE NOT ALLOWED in DSC 180 (this is not an exhaustive list):

  • Using or submitting either writing or code acquired from other students (except your group, where allowed).
  • Not properly citing ideas, writing, or code acquired from outside sources. (Citations are a good thing!)
  • Having any other student complete any part of an assignment on your behalf.
  • Completing an assignment on behalf of someone else.

The following activities are examples of appropriate collaboration and ARE ALLOWED in DSC 180:

  • Discussing the general approach to understanding or solving a problem.
  • Talking about debugging/cleaning strategies or issues you ran into and how you solved them.
  • Using outside material with proper citations (including StackOverflow code!).

Generative Artificial Intellience tools should not be used on any methodology assignments. As for your domain work (e.g. your weekly tasks, Quarter 1 Project, and Quarter 2 Project Proposal), whether and how you can use Generative AI is up to your mentor. Ask them!