Syllabus

Introduction

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. Some of the topics covered are the following: data models (relational, json, etc), query languages (SQL, datalog, etc), transactions, parallel data processing, and NoSQL.

Course Components and Late Policy

Grading

The grade breakdown is as follows:

Homeworks: 40%
Course Project: 10%
Lecture Activities: 15%
Exams (Quizzes + Final): 35%

Assignments: Homeworks and Course Project

We have homeworks with due dates approximately every week. They 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. They are always due at 10pm.

We have a single large course project during the middle of the quarter, which synthesizes your understanding of many concepts, including SQL queries, schema design, concurrency, and Java programming. To help with time management, we've broken this project into 3 "milestones", due each week. As with homeworks, each milestone must be submitted individually and is due at 10pm.

Exams: Quizzes and Final

You will have 3 take-home quizzes, administered via Gradescope. These quizzes are "open book": you can use any available resource, including personal notes, lecture recordings, current classmates, former 344 students, and the internet. Although you must submit your answers individually, you may collaborate with a group of any size, subject to our collaboration policy; however, we recommend that you keep your group to <=5 students, as large groups have historically struggled with this format. Quizzes are always due at 10pm and late days cannot be used for them.

The final "exam" will be a summative take-home assignment that is due at our regularly scheduled exam time (10:20am on Wednesday of finals week). We will try to make it 2h long so that you can complete it during the scheduled 2h block, if desired.

Lecture Activities

Lectures have a variety of before- and in-class activities designed to enhance lecture topics and provide an opportunity to interact with classmates. These are intended to be low-pressure and lenient.

These activities are administered via PollEverywhere and can be submitted asynchronously (eg, if you are re-watching lectures after class). If you choose to submit your activities asynchronously, you will have approximately ~36 hours to do so; late submissions will not be accepted.

Due to our desire for low-pressure activities, grading primarily reflects participation. You will receive credit for attempting the activity; notably, your answer can be incorrect as long as you are actively participating in lecture. Furthermore, our "90% is 100%" policy means that you will receive full credit for this category as long as you attempt 90% of the offered activities. For example, you will receive full points in this category if you watch lecture and complete 72 out of the ~80 questions asked during the quarter. There is no bonus or extra credit for exceeding 90% (eg, if you answer 75 of the ~80 questions).

In-person participation can be tricky to assess equitably, so we do not attempt to do so.

Late Policy for Assignments

There are absolutely no late submissions accepted for exams or lecture activities.

We expect assignments (homeworks + project milestones) to be submitted on time; however, we realize that our deadlines may occasionally overlap with another class's, or that work may schedule you for an unexpected shift, or that you may contract a routine illness. In recognition of this fact, you are allowed a total of 8 late day tokens, which are defined as the 24-hour period after a due date: num_late_days = ceil(hours_late/24). Unused tokens have no impact on your grade at the end of the quarter.

Typically, you may spend up to 2 tokens per assignment, though some assignments may adjust this threshold. Regardless of how many late days you have remaining, no assignment will be accepted after 2 days have elapsed.

For each late day you use that exceeds your token allowance, you will lose 20% off your score on one of the late assignments.

Inclusion

Our code and our projects are made better by considering a variety of viewpoints. Your course staff is committed to the values outlined in the Allen School’s inclusiveness statement, and you are expected to uphold a supportive and inclusive learning environment.

If, at any point, you are made to feel uncomfortable, disrespected, or excluded by a staff member or fellow student, please report the incident so that we may address the issue and maintain a supportive & inclusive learning environment. Should you feel uncomfortable bringing up an issue with a staff member directly, consider submitting anonymous feedback or contacting the Office of the Ombud.

Resources

Support Hours and One-on-Ones

If you have conceptual questions or need assistance debugging your assignments, we strongly recommend attending support hours.

We recognize that not every question is appropriate for support hours, however. For example, you may wish to discuss your extenuating circumstances, need support/advice for a personal issue, or have followup questions from a previous support hour. In these circumstances, we recommend requesting a one-on-one.

Ed

We are using Ed for discussion and announcements. This includes both private "staff-only" discussion and public class-wide discussion.

Gradescope and Canvas

We are using Gradescope for assignments and exams. We will use Canvas only for Panopto recordings. Gradescope is the authoritative copy of your scores; we will not copy scores into Canvas.

PollEverywhere

We are using PollEverywhere to administer before- and in-class lecture activities.

Legible Code and Style Guides

We have a few suggestions for how to write code which your course staff will be able to grade.

Textbooks

We will be using Database Systems: The Complete Book (2nd Edition) (errata here)

If you need an alternative explanation for a particular topic, the library has the following:

Fundamentals of Database Systems, by Elsmasri and Navathe
Database Management Systems, by Raghu Ramakrishnan and Johannes Gehrke
Foundations of Database Systems, by Abiteboul, Hull and Vianu

Anonymous Feedback

You can send anonymous feedback to Hannah via this form. Note that she cannot respond to you directly.

Cheating and Collaboration Policy

Learning these ideas is challenging. We encourage you to discuss course activities with your friends and classmates as you are working on them, because you will definitely learn more in this class if you work with others than if you do not. Ask questions, answer questions, and share ideas liberally; we want a class that is open, welcoming, and collaborative, where we can help each other build the highest possible understanding of the course material.

Learning collaboratively is different from sharing answers. Ultimately, the difference between collaboration and sharing answers boils down to "whose brain generated the submitted material?". A good rule-of-thumb for distinguishing between these two is: Can I/my collaborator successfully answer an immaterially-different question (eg, calculating the max instead of the average), by myself/themself? If the answer is “yes”, then someone learned from your collaboration; that’s great! Please do more of it! Go you!

However, if the answer is “no”, then you were sharing answers; please don't do that. In particular:

Do not use any collaboration-generated materials (eg, code, hand-written notes, screenshots) when you are working on the materials that you will submit for grading. You MAY NOT ALLOW your collaborators to take away materials, either.
Cite the names of all your collaborators, regardless of whether they are currently taking CSE 344.
Take a 30 minute break (eg, go for a run, watch a TV show, wash the dishes) before writing up your solution (eg, writing code, drawing a parse tree).
Treat coding assistants and generative AIs (eg, Github Copilot, OpenAI ChatGPT) as if they were another human; you may "discuss" class problems with these tools (eg, ask them to write an additional example or to explain code that you wrote), but your submitted work must comply with the above (ie, be written independently and without referencing any output from that tool). When citing your "collaborator", please also provide the prompts you used.

Ultimately, YOU are responsible for understanding and applying Allen School's Academic Misconduct Policy, which applies to students who provide answers in addition to those receiving them. If you are in doubt about what might constitute misconduct, contact the course staff describing the situation and we will be happy to clarify it for you. When it comes to the potential for sharing answers, it is better to ask permission than to beg forgiveness.

Accommodations and Extenuating Circumstances

Please refer to university policies regarding disability accommodations and religious accomodations. 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 circumstance that arise during the course, please do not hesitate to contact the staff to discuss your situation. The sooner we are made aware, the more easily these situations can be resolved. Extenuating circumstances may include:

Work-school balance
Familial responsibilities
Military duties
Unexpected travel
Acute health issues (notably: recall that mental health is still health)
Concerns for personal safety
... or anything else beyond your control which may negatively impact your performance in the class

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