DSC160: Data Science and the Arts
ABOUT THIS COURSE
Welcome to DSC160! This course addresses the intersection of data science and contemporary arts and culture, exploring four main themes of authorship, representation, visualization, and data provenance. The course is not solely an introducing to data science techniques, nor merely an arts practice course, but explores significant new possibilities for both fields arising from their intersection. Students will examine problems from complementary perspectives of artist-researchers and data scientists.
Prerequisites: DSC 80.
Authorship: How are traditional notions of authorship and creativity changing with the rise of data-driven and machine learning-based generative models? What new techniques are possible given the advent of ubiquitous machine perception, cloud computing, deep learning inference, and internet connected devices (IoT)?
Representation: In contemporary society, how are data used to represent people? For instance, how do technology corporations represent individuals through data? How do we choose to represent ourselves? What are the potentials and limitations of online self-representation as currently practiced?
Visualization: How do we meaningfully experience data analyses? For instance, what modes of comprehension, and perception do we engage (sonification, visualization, tangible media, temporal experience, interaction)
Data Provenance: How do we question data evaluating the truthfulness of digital media given the rise of realistic simulations (deep fakes), bot authorship (botnets/fake news), and masked or unknown sources (data provenance)?
You can see the weekly course schedule with topics and on the schedule page.
COURSE TIME & LOCATION
- Lecture: TuTh 2-3:20pm. (asynchronous, uploaded as video)
- Discussion: Wed 2-2:50pm.
- Final Project: due finals week.
Instructor: Robert Twomey (email@example.com)
Teaching Assistants: Balaji Balachandran, Subrato Chakravorty
Office Hours: online during lecture time TuTh 2-3:20pm.
This course is a mix of lecture and lab activity. In addition to a number of homework exercises, students will produce a midterm and final project in groups.
The course will be conducted primarily in python using free, open-source machine learning and scientific computing toolkits, running on the educational computing cluster (datahub). In addition to hands-on experience with DSC techniques, students will become familiar with cloud-based workflows, jupyter notebooks, and a variety of software tools applicable to the arts.
Architectures and topics covered include archival practices, web scraping, data cleaning, feature extraction, dimensional reduction, clustering, and generative methods in the arts including new ML techniques.
Students will be responsible for thinking creatively, identifying research questions within the arts, for technical implementation of state-of-the art data analysis and data-driven generative techniques, and for producing meaningful responses to course prompts.
Students will be evaluated based on the creative interest of their research questions, the appropriateness and implementation of their DSC techniques, and the ultimate technical and aesthetic success of their realized projects.
- Lectures will be pre-recorded videos that are shared before lecture time. You can watch these asynchronously on your own time. They will introduce tools, techniques, and concepts which you will explore through the class exercises and projects.
- Discussion section will be flexible according to student needs, coordinated by our TA.
- Office hours will be held via zoom / piazza.
REMEMBER THIS URL: dsc160.roberttwomey.com
- Course updates will be posted on this website: dsc160.roberttwomey.com and on canvas.
- Canvas will coordinate all homework and discussions: https://canvas.ucsd.edu/courses/12878
- Piazza for technical help: piazza.com/ucsd/spring2020/dsc160
- Code Examples and Exercises: https://github.com/roberttwomey/dsc160-code
- Zoom for office hours and lecture: see url posted to canvas
- Github Classroom: for project submission
COLLABORATION POLICY AND ACADEMIC INTEGRITY
The UCSD Policy on Integrity of Scholarship and this syllabus list some of the standards by which you are expected to complete your academic work, but your good ethical judgment (or asking us for advice) is also expected as we cannot list every behavior that is unethical or not in the spirit of academic integrity. Ignorance of the rules will not excuse you from any violations.
Cite your sources, including open source software repositories, research papers/literture, and artworks.
Remember, Academic Integrity is about doing your part to act with Honesty, Trust, Fairness, Respect, Responsibility and Courage.
The Office for Students with Disabilities (OSD), an Academic Affairs department, is responsible for the review of medical documentation and the determination of reasonable accommodations based on a disability. Authorization for Accommodation (AFA) letters are issued by the OSD and given to undergraduate, graduate, and Professional School students directly. If you have an AFA letter, meet with the CSE Student Affairs representative, and schedule an appointment with your instructor by the end of Week 2 to ensure that reasonable accommodations for the quarter can be arranged.
DIVERSITY AND INCLUSION
We are committed to fostering a learning environment for this course that supports a diversity of thoughts, perspectives and experiences, and respects your identities (including race, ethnicity, heritage, gender, sex, class, sexuality, religion, ability, age, educational background, etc.). Our goal is to create a diverse and inclusive learning environment where all students feel comfortable and can thrive.
Our instructional staff will make a concerted effort to be welcoming and inclusive to the wide diversity of students in this course. If there is a way we can make you feel more included please let one of the course staff know, either in person, via email/discussion board, or even in a note under the door. Our learning about diverse perspectives and identities is an ongoing process, and we welcome your perspectives and input.
We also expect that you, as a student in this course, will honor and respect your classmates, abiding by the UCSD Principles of Community (https://ucsd.edu/about/principles.html). Please understand that others’ backgrounds, perspectives and experiences may be different than your own, and help us to build an environment where everyone is respected and feels comfortable.If you experience any sort of harassment or discrimination, please contact the instructor as soon as possible. If you prefer to speak with someone outside of the course, please contact the Office of Prevention of Harassment and Discrimination: https://ophd.ucsd.edu/