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Syllabus DS 4002: Prototyping

Instructors: Peter Alonzi (lpa2a) and Ethan Wang (jqf8qm)

Office Hours:

  • Peter Alonzi:
    • Dates and times: Wednesdays 14:00-16:00pm
    • Location: Data Science 344
  • Ethan Wang:
    • Dates and times*: Monday 13:30 - 15:30pm
    • Location: Data Science 304, or you can find me at the closest cubicle in front of 304 conference room.

Subject Area and Catalog Number: Data Science, DS 4002

Term : Spring 2025

Meeting times: 11:00-12:15, Tuesday/Thursday

Class Location: Data Science Room 205

Class Title: Prototyping (in SIS: Data Science Project Course)

Level: Undergraduate

Credit Type: Grade (A-F) using Specifications Grading

Learning Management System: This course uses UVACanvas (https://canvas.virginia.edu/)

About the course

The objective of this course is for students to gain experience working on data-driven problems while developing the ability to critique their own work and the work of others productively. The key element that separates this course from traditional courses is that it is driven by student-led projects. Students will be asked to set the expectations for the course, contribute to its tactical objectives, and provide critical feedback to peers. Aside from providing datasets and infrastructure support, setting high-level project goals, and offering mentorship and technical guidance, the instructors will serve primarily in advisory capacities for the student teams.

The class meets in-person; attendance is compulsory. If circumstances arise that preclude attendance at a particular session, students are to notify group members and the instructor as soon as possible so that appropriate accommodations can be arranged (for example a death in the family). Group work outside of class is expected (on average project teams meet once a week outside of class). Students will be assigned to small groups to work together on three projects throughout the semester. Groups will be assigned after the first two weeks of class (aka the fundamental training period)

What you will learn

Data Science is a broad, elusive, and dynamic field best learned via hands-on experience. Topics in this course are selected to reinforce this perspective and help students understand the field’s core ideas and what is demanded from practicing data scientists. An ideal outcome would be for students to gain a perspective on the breadth of the field of data science, understand important tools and environments, and develop skills necessary to contribute to the community. Learning objectives include:

  • Cognitive
    • research ideas in a domain and create a testable hypothesis/model
    • develop a project plan based on the scientific method principles
    • establish data sets relevant to your hypothesis/model
    • create a functioning data science pipeline
    • prepare findings for presentation to your peers
    • and more objectives that you want to add (e.g. training neural networks)
  • Social
    • Collaborate with peers to implement your project plan
    • Provide constructive criticism
    • Demonstrate appropriate behavior in group settings
  • Psychomotor
    • Present results of a team project and field audience questions

How You’ll Know You Are Learning (Assessments)

This class uses the Specifications grading system. Like a more traditional system the course is broken up into assignments but they are all graded on a single-level rubric system. For many students it is an unfamiliar system and requires a little adjustment but once you get used to it you will really enjoy the benefits.

Details are here: Grading Policy. Alignment to the learning objectives is shown in the individual rubrics.

Schedule of Topics

Week Date Project # Project Cycle Plan Milestone
Week 1 1/14,16 0 Course Philosophy
Week 2 1/21,23 0 DS project fundamentals
Week 3 1/28,30 1 1 Begin Project 1 MI1
Week 4 2/4,6 1 2 MI2
Week 5 2/11,13 1 3 MI3
Week 6 2/18,20 1 4 Presentation Week MI4
Week 7 2/25,25 2 1 Begin Project 2 MI1
Week 8 3/4,6 2 2 MI2
Week 9 3/11,13 2 * Spring Break
Week 10 3/18,20 2 3 MI3
Week 11 3/25,27 2 4 Presentation Week MI4
Week 12 4/1,3 3 1 Begin Project 3 MI1
Week 13 4/8,10 3 2 MI2
Week 14 4/15,17 3 3 MI3
Week 15 4/22,24 3 4 Presentation Week MI4
Week 16 4/29 *
Exams 5/8 No Exam
  • N.B. There is no final exam for this class

A few Policies that will Govern the Class

Grading Policies: This course uses the specifications grading system as explained in Specifications Grading By Linda Nilson and under the guidance of the UVA CTE. For details see this page.

Attendance Policy: The majority of work done in this class is group work. Everyone is accountable to their group. Attendence is expected not only during class time but also during time outside of class when the group decides. Attendance is essential during presentation week.

University of Virginia Honor System: All work should be pledged in the spirit of the Honor System at the University of Virginia. The instructor will indicate which assignments and activities are to be done individually and which permit collaboration. The following pledge should be written out at the end of all quizzes, examinations, individual assignments, and papers: “I pledge that I have neither given nor received help on this examination (quiz, assignment, etc.)”. The pledge must be signed by the student. For more information, visit www.virginia.edu/honor.

Special Needs: The University of Virginia accommodates students with disabilities. Any SCPS student with a disability who needs accommodation (e.g., in arrangements for seating, extended time for examinations, or note-taking, etc.), should contact the Student Disability Access Center (SDAC) and provide them with appropriate medical or psychological documentation of his/her condition. Once accommodations are approved, just follow up with me concerning any logistics and implementation of accommodations. Please try to make accommodations for test-taking at least 14 business days in advance of the date of the test(s). Students with disabilities are encouraged to contact the SDAC: 434-243-5180/Voice, 434-465-6579/Video Phone, 434-243-5188/Fax. Further policies and statements are available at www.virginia.edu/studenthealth/sdac/sdac.html

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