Instructors: Javier Rasero (mnr3kn) and Harsh Anand (yyf8rj)
Office Hours:
- Javier Rasero:
- Dates and times: Wednesdays and Fridays, 2-4pm. Also by appointment or just by dropping by my office and checking if I am available.
- Location: Elson 198 or via zoom (check out Canvas for the link)
- Harsh Anand: tbd
Subject Area and Catalog Number: Data Science, DS 4002
Term : Spring 2024
Meeting times: 12:30-13:45, Tuesdays and Thursdays
Class Location: Dell 1, Room 105
Class Title: 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 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)
This was super cool to me because before this project, I had never worked with image data before and didn’t realize it was so readily available and so easy to work with.
I am very nervous about the workforce, what if I am not good enough or don’t know enough? Gaining more knowledge on different types of models is helping me ease that anxiety.
There was a lot of communication faults from all of us. When we are in the industry, these issues don’t just slide. In a classroom, where we are here to learn, This is the best time to work on teamwork. Although this is can be uncomfortable let’s not worry make mistakes and focus on growing from those mistakes.
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 Summary. Alignment to the learning objectives is shown in the individual rubrics.
There are 4 assignment categories:
- Project Milestones - These assignments describe milestones in the project cycle and help guide the student through the process of working a project from start to finish.
- Critiques - These assignments are about providing critique to others and to yourself.
- Documentation - These assignments serve two purposes. The first is to practice communicating the technical details of your work by documenting your code. The second is to highlight the contributions you made to each project cycle.
- Case Studies - These assignments are about reproducing, reviewing and producing projects.
| Week | Date | Project # | Project Cycle | Plan | Milestone |
|---|---|---|---|---|---|
| Week 1 | 1/18 | 0 | Course Philosophy | ||
| Week 2 | 1/23, 1/25 | 0 | DS project fundamentals | ||
| Week 3 | 1/30, 2/1 | 1 | 1 | Begin Project 1 | MI1 |
| Week 4 | 2/6, 4/6 | 1 | 2 | MI2 | |
| Week 5 | 2/13, 2/15 | 1 | 3 | MI3 | |
| Week 6 | 2/20, 2/22 | 1 | 4 | Presentation Week | MI4 |
| Week 7 | 2/27, 2/29 | 2 | 1 | Begin Project 2 | MI1 |
| Week 8 | 3/5, 3/7 | 2 | Spring Break | ||
| Week 9 | 3/12, 3/14 | 2 | 2 | MI2 | |
| Week 10 | 3/19, 3/21 | 2 | 3 | MI3 | |
| Week 11 | 3/26, 3/28 | 2 | 4 | Presentation Week | MI4 |
| Week 12 | 4/2, 4/4 | 3 | 1 | Begin Project 3 | MI1 |
| Week 13 | 4/9, 4/11 | 3 | 2 | MI2 | |
| Week 14 | 4/16, 4/18 | 3 | 3 | MI3 | |
| Week 15 | 4/23, 4/25 | 3 | 4 | Presentation Week | MI4 |
| Week 16 | 4/30, 5/2 | Case studies | |||
| Examinations | 5/9 | Last day to submit |
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.
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