Instructors:
- 12:00 section: Peter Alonzi (lpa2a) and Mohini Gupta (rde6mn)
- 13:00 section: Gianluca Guadagni (gg5d) and Andres Castellanos (ppj8hm)
Office Hours:
- Peter Alonzi:
- Dates and times: Wednesdays 13:00-15:00pm
- Location: Data Science 344
- Mohini Gupta:
- Dates and times: Fridays 13:00-15:00
- Location: The Hub, 2nd Floor
- Gianluca Guadagni:
- Dates and times: Thursdays 17:00-18:00
- Location: 431
- Andres Castellanos:
- Dates and times: TBD
- Location: TBD
Subject Area and Catalog Number: Data Science, DS 4002
Term : Fall 2025
Meeting times:
- Section 001 - 12:00-12:50, MWF
- Section 002 - 13:00-13:50, MWF
Class Location: Data Science Room 306
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/)
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 meet a high level of expectation 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 and part of the grading scale. However that does not mean all work is done in class. 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).
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
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.
| Week | Date | Project # | Plan | Milestone Due | B Bundle | A Bundle |
|---|---|---|---|---|---|---|
| Week 1 | 8/27,29 | 0 | Course Philosophy | |||
| Week 2 | 9/1,3,5 | 0 | DS project fundamentals | |||
| Week 3 | 9/8,10,12 | 1 | Begin Project 1 | MI1 | ||
| Week 4 | 9/15,17,19 | 1 | MI2 | |||
| Week 5 | 9/22,24,26 | 1 | MI3 | |||
| Week 6 | 9/29,10/1,3 | 1 | Presentation Week | MI4 | CC1,CC2,ID1 due 10/6 | |
| Week 7 | 10/6,8,10 | 2 | Begin Project 2 | MI1 | Open CS 1, CS 2 | |
| Week 8 | 10/15,17 | 2 | no class 10/13 | MI2 | ||
| Week 9 | 10/20,22,24 | 2 | MI3 | |||
| Week 10 | 10/27,29,31 | 2 | Presentation Week | MI4 | CC1,CC2,ID1 due 11/3 | CS1,CS2 Due 10/31 |
| Week 11 | 11/3,5,7 | 3 | Begin Project 3 | MI1 | Open CS 3 | |
| Week 12 | 11/10,12,14 | 3 | MI2 | |||
| Week 13 | 11/17,19,21 | 3 | MI3 | |||
| Week 14 | 11/24 | 3 | Thanksgiving | |||
| Week 15 | 12/1,3,5 | 3 | Presentation Week | MI4 | CC1,CC2,ID1 due 12/8 | CS3 Due 12/5 |
| Week 16 | 12/8 | Make UP | ||||
| Exams | 12/11 | No Exam |
- N.B. There is no final exam for this class.
- Attendance credit is earned during the project cycles. I.e. from 9/8 through 12/3.
- Due dates are listed in canvas and submissions are made via canvas except for CS3. No assignments can be accepted after 12/11.
Attendance Policy: The majority of the project work for this class is done during class. As such attendance is part of the course grade. Details are in the grading details page Grading Policy.
There are many additional policies that this course inherits from the School of Data Science course policies, please see here