Instructor: Claudio Silva (email@example.com); webpage
Section Leader: Erin McGowan (firstname.lastname@example.org)
Teaching Fellow: Vitoria Guardieiro (email@example.com)
Grader: Rithvik Guruprasad (firstname.lastname@example.org)
Instruction Mode: In-Person
DS-GA 3001.001 (Lecture) Thursdays 6:45pm-8:25pm Classroom: 31 Washington Pl (Silver Ctr) Room 520
DS-GA 3001.002 (Lab) Fridays 10:15am-11:05am Classroom: 31 Washington Pl (Silver Ctr) Room 520
Class Discord: https://discord.gg/6pFj8dMK.
All our communications happen here.
Anything related to assignments, grading, etc., send a direct message to Grader(s).
Anything related to labs / coding contact Erin or Vitoria.
Feel free to direct message for anything else.
Solid programming expertise.
We will also expect students to be able to program in Python.
Basic knowlege of machine learning.
Our course is based on foundations of visual analytics, which is an area of data visualization that is concerned with improving the human analytic process.
Visual analytics is concerned with combining automated processes with human-driven processes that are built around data visualization: visual representations of data, and ways to interact with data.
This course is designed to sharpen a student’s knowledge of visualization.
We aim to make the student a more effective data scientist.
The course has a major project, which will help the student develop research skills.
The course include lectures and labs. We will strive to have hands-on sessions to complement theoretical materials.
Starts with a short primer on visualization.
Then we will cover techniques for visualizing model assessment, white-box and black-box machine learning explainers, and dimensionality reduction.
Second half of the course has more advanced topics, including Topology Data Analysis and techniques for visualizing deep neural networks.
There is no textbook for the course.
We will have suggested reading materials for each class.
Supplemental readings to be used as reference material:
Course includes a substantial research project.
Projects are expected to be pursued in groups of 2-3.
Once the group is finalized, students cannot change or separate their groups throughout the semester.
Late submissions of assignments will be penalized as follows:
A standard deduction rate of 20% per day. It means that after 5 days of being late, your assignment will have a maximum grade of 0 (zero).
You will have a one-time exception for submitting assignments late (up to 5 days late).
All students are expected to do their own work.
See detailed policy on class syllabus.
Also, here is a link to the GSAS statement on Academic Integrity.
You can use generative AI tools to do the assignments in this class.
If you use an AI to guide you in completing an assignment, you have to disclose which parts were generated by the AI.
This course does not have a final exam, but there will be a final project presentation.
Also, please pay attention to notable dates such as Add/Drop, Withdrawal, etc.