Logistics

• Lectures: Tuesdays 1:30-3:50PM. We're in-person this year in CSE2 371.
• Attendance: We expect all students to attend and participate in all class sessions.
• Public resources: The lecture slides and assignments will be posted online as the course progresses. We are happy for anyone to use these resources with acknowledgement, but we cannot grade the work of any students who are not officially enrolled in the class.
• Canvas We will be accepting homework submissions through the course's Canvas.
• Office hours: Office hours will be held thrice weekly.
  Professor Althoff: Tuesdays immediately following the end of lecture.
  Mike: Thursdays 9-10am in-person at CSE2 Room 150
  Jina: Mondays 9:30-10:30am virtual on Zoom.
  
• Contact: Students should ask all course-related questions in the EdDiscussion forum, where you will also find announcements. For external enquiries, personal matters, or in emergencies, you can email us at cse481ds-instructors@cs.washington.edu. Please do not use other emails (such as the TAs' UW email IDs) for questions about the class, as these may not be answered in a timely manner.

Content

What is this course about?

Data analysis is a central activity for scientific research and is increasingly a critical part of decision making in government and business. However, producing reliable data analysis outcomes is challenging since the decisions made throughout the analysis process can dramatically affect the eventual outcome. This Data Science Capstone focuses on the complete end-to-end process of data analysis performed with code: the iterative, and often exploratory, steps that analysts go through to turn data into results. Our focus is not limited to statistical modeling or machine learning, but rather the complete process, including transformation, exploration, modeling, and evaluation choices.

Students will work in groups of four on a single project that will tie together and apply previous experiences from CSE 312, 332, 446, 442, 344, and other classes. Students are expected to already possess knowledge of appropriate machine learning, visualization and database methods, and will focus on independently applying those methods in the context of your project. There will therefore be limited lecture material in this course. Course staff will instead work closely with students to critique and advise on their group project. Students will experience the end-to-end data analysis process from transformation and exploration of data to modeling and evaluation. Your group will brainstorm on a project during the first week, before collaboratively exploring the data and implementing a complete data analysis workflow. This capstone course gives hands-on experience with selecting a data science question, and with crafting and evaluating a data science process to answer that question. question.

Prerequisites

Students should have completed CSE 332 and CSE 312, and at least one of CSE 446, CSE 442, or CSE 344. There are no other requirements for participating in this capstone class.