Data is at the heart of modern commercial application development, and its use includes environments and domains where large amounts of data must be stored for efficient update, retrieval, and analysis. The purpose of this course is to provide a comprehensive introduction to the use of management systems for applications. Data 514 introduces database management systems and their use; data models, query languages, transactions, database tuning, and parallelism.
This course meets on Tuesdays, 5:00-8:50 pm, in the MSDS classroom. Students are expected to attend class in person whenever possible. Each class meeting will include lecture activities, and allow for discussion among class members. Students who must miss class are responsible for the material provided during lecture. In the case of extenuating circumstances contact the instructor directly for accommodations.
The grade breakdown is as follows:
There will be approximately one homework assignment per week. Homeworks assess a variety of skills, including coding, database theory, and societal impacts. Homeworks must be submitted individually; collaboration is permitted, subject to the guidelines below. Homeworks are due on Thursdays at 9pm.
Lectures have a variety of associated activities, which complement the day’s topics. In-class activities are intended to promote active learning and break up the tedium of lectures. These problems will usually be solved and discussed during our meetings. The in-class activities are provided on worksheets*, along with additional practice problems that dive deeply into the day’s topics.
Worksheets will be provided for each class meeting and are due Tuesdays at 5pm the following week (ie, before the next lecture). Worksheets will be graded based on reasonable effort.
*Occasionally additional participation may be through Gradescope, which will be noted clearly during the class lecture.
At the end of the quarter, you will work in a small group (2-3 students) to design a real-world application using the techniques you've learned. You will give a short presentation about your project during finals week.
There are no exams for this course.
In recognition of the vagaries of life, students are permitted up to 6 late days during the quarter. Using a late day permits a student to submit an assignment up to 24 hours past the due date (two late days permits 48 hours). Students may use up to two late days per assignment. (There will be no late days allowed for the final project presentation.) Late day tallies will be maintained on Canvas.
If you need further accommodation, or have truly exceptional circumstances, please contact Megan to arrange an appropriate strategy for completing your work.
If you have conceptual questions or need assistance debugging your assignments, we strongly recommend attending office hours. You may also schedule an appointment with Megan Hazen.
Canvas is the learning management system of record through which this course is administered. It will be used in conjunction with a number of other resources to allow a full-featured course.
We will use Ed for discussion and announcements. This includes both private "staff-only" discussion and public class-wide discussion.
We use Gradescope for homework assignments.
This course will refer to a webpage that publishes additional resources. This includes a course calendar with assignments.
If you have any concerns you may contact Megan directly at mh75@uw.edu. Alternatively, you can send anonymous feedback to Megan using this form, or email the MSDS staff at uwmsds@uw.edu.
The MSDS program believes that each individual student is unique and capable of making contributions to our cohorts and the field. Each students individual viewpoint and talents are essential to a thriving data science community. This instructor seeks to ensure all students are fully welcomed in each course, and strives to create an environment that reflects community and mutual caring. The expectation that all students and the course staff will work together to build this community. I encourage students with concerns about classroom or course climate to contact me directly, or use one of the other feedback methods listed above.
Your integrity is everything. You are expected to hold yourself to the highest standards of personal and academic integrity while participating in this course.
Any attempt to misrepresent the work you submit will be dealt with via the appropriate University mechanisms, and your instructor will make every attempt to ensure the harshest allowable penalty. The guidelines for this course and more information about academic integrity are in a separate document (including, but not limited to, the Allen School’s Academic Misconduct page and the College of Engineering’s Academic Misconduct Process). You are responsible for knowing the information in these documents.
All material you submit in the homeworks and quizzes must be written by you. Copying the work of another student or external documents is a violation of the above integrity policy. In summary:
Data scientists adhering to the highest standards of integrity will still collaborate with fellow data scientists. In this course collaboration is encouraged and expected. You may discuss and share the contents of this course as you wish, so long as you do not share specific solutions and code. You may work with other students and discuss solutions to problems, but you should not copy answers or code directly. You may use internet resources to look up references, but you may not copy any answers directly.
One way to determine whether you are collaborating, versus cheating, is to test how well you have learned material. If you are able to take a break, and then recreate solutions or generalize solutions (solve similar but slightly different problems) that is a good indication that you are truly learning the material.
There will be some opportunities to work on solutions together. These include in-class activities and the final project. During these times you should ensure that each member of the group is treated with respect and can make a contribution to the final solution.
Please refer to university policies regarding disability accommodations and religious accommodations. These policies have strict timelines associated with them, so we encourage you to read through and apply these policies at the start of the quarter if you believe they may apply to you.
More generally, we recognize that our students come from varied backgrounds and can have widely-varying circumstances. If you have any unforeseen or extenuating circumstances that arise during the course, please do not hesitate to contact Megan to discuss your situation. We will work together to find a resolution. Extenuating circumstances may include:
The University of Washington acknowledges the Coast Salish people of this land, which touches the shared waters of all the Duwamish, Suquamish, Tulalip and Muckleshoot nations. We also recognize and acknowledge that the very foundation of the United States of America was built on the free and forced labor of Black people of the African diaspora and Black people indigenous to this land. The historical and contemporary contributions of the Coast Salish people and the Black diaspora continue to shape American culture and benefit all inhabitants of this land.
More generally, this instructor would like to acknowledge that all of our work builds on the foundations of those who came before us. This course relies heavily on material developed by Dan Siciu, Ryan Maas, and Hannah Tang, as well as all previous instructors and TAs.