Course Syllabus - Database Design & Implementation

Database Design & Implementation
New York University
Department of Computer Science

Course description

In this course, we introduce principles and applications of databases. We begin by studying issues related to data collection and discovery, and techniques for prepping and munging the raw data to shape it into a form that is ready for database storage. Studying the design of relational databases, we learn how to craft databases to model “real-world” data in a way that lends itself to the types of manipulation and analysis that we have in mind. Once we have a clear understanding of these issues, we look at more advanced topics in data, such as data analysis and visualization in Python, how NoSQL databases like MongoDB compare to relational systems, ethical and societal implications of the world of data, and how to build simple web apps that publish and save data stored in databases.

Credits

4 credits

Meeting pattern

Two class sessions per week, 1 hour and 15 minutes each session.

Prerequisites

Introduction to Computer Programming (either CSCI-UA.0002 or CSCI-UA.0003) with a grade of C or better. Students that have successfully completed Data Management and Analysis (CSCI-UA 479) are not eligible to take this course.

Learning objectives

Upon completing this course, students will be familiar with some of the most common database systems and practices, including:

Explorations of “data in the wild” - common sources of and issues related to working with real data sets
Textual representations of data - common formats for storing data as text, e.g. CSV, JSON, XML, HTML, and fixed-width columnar text files
Spreadsheets as databases - Using database features of standard spreadsheet software such as Microsoft Excel and Google Sheets
Using Python to prepare datafiles - scrubbing, munging, and massaging data to prepare it for analysis
Relational database programming with SQL - a deep dive into the SQL language
Python database integration - connecting Python programs to both SQL and NoSQL databases
Data analysis - contemporary data analysis techniques, including standard Python modules and the pandas library within a Jupyter Notebook environment
Visual display of data - using common data visualization libraries, such as pandas and matplotlib, to create charts and graphs to more easily spot and communicate trends in data
Constructing and evaluating data-based claims - using data to make and test hypotheses and evaluate claims about the world
Societal implications of data collection and use - evaluate the societal and ethical consequences of different forms of data collection and utilization
Relational database theory - the relational model, normalization, and Entity-Relationship Diagrams
MongoDB - comparing and contrasting MongoDB with relational systems and viewing it as an example of the trend towards NoSQL databases
Web application implementation - implementation of a Python-based database-driven web application using MongoDB, Flask, and pymongo
Time-permitting, we will cover blockchain technology in detail as a unique variety of database

To achieve mastery in these topics, students will take quizzes and exercises corresponding to each lecture topic as well as a midterm and final exam.

While one class session is usually dedicated to hands-on student work to start each assigned exercise, students are expected to work independently to complete the exercises for approximately 10-15 hours each week.

Instructor

Amos Bloomberg
WWH 424

Department

This course is offered by the Computer Science Department. For department-related questions or concerns, please see the department’s contact information.

Textbooks

All books are available online:

via Online Access through NYU Libraries / O'Reilly Safari (log in using your NYU Net ID email address; see notes on accessing via Safari apps)
free html / ebook versions supplied by author or publisher
database platform's documentation

Readings will be selected from the following books:

Getting help

Help resources available to you are listed in order of urgency of your problem:

Messaging

Our course will use a team messaging app (link to be distributed in class) as its main communication channel for announcements and discussion. This is a good place to ask questions that anyone - other students, graders, tutors, or the professor - can answer. This is a resource best used when the answer is not required urgently.

Tutoring

Tutors for this course are waiting to answer your questions, either on our message board or during dedicated tutoring hours (hours to be distributed in class). Use tutoring for more involved questions and when you prefer a more immediate answer.

Talk with the instructor

For any issues at all, contact the instructor:

see me before class
raise your hand or simply speak during class
see me after class
come to my open office hours (hours to be distributed in class)

Additional help resources

Additional academic support is also available through the University Learning Center.

Students who feel they could benefit from additional writing help are encouraged to utilize the NYU Writing Center.

Disability disclosure statement

Academic accommodations are available for students with disabilities. Please contact the Moses Center for Student Accessibility (212-998-4980 or mosescsd@nyu.edu) for further information. Students who are requesting academic accommodations are advised to reach out to the Moses Center as early as possible in the semester for assistance.

Student wellness

In a large, complex community like NYU, it’s vital to reach out to others, particularly those who are isolated or engaged in self-destructive activities. Student wellness is the responsibility of all of us.

The NYU Wellness Exchange is the constellation of NYU’s programs and services designed to address the overall health and mental health needs of its students. Students can access this service 24 hours a day, seven days a week - wellness.exchange@nyu.edu; (212) 443-9999. Students can call the Wellness Exchange hotline (212-443-9999) or the NYU Counseling Service (212-998-4780) to make an appointment for Single Session, Short-term, or Group counseling sessions.

Attendance & participation

Attendance is mandatory and absences may be penalized up to 10% of the total grade. Students who are present for only a small fraction of a class session will be marked absent. In-class and online message board participation is encouraged. Anecdotally, students who do not attend class regularly and who do not participate in discussions tend to do poorly.

Student and instructor interaction during class

Class sessions are a mixture of lecture, discussion, and project work. During any lecture or discussion, students are generally encouraged to participate with questions, comments, and constructive criticism of the material being covered. On days when students work on their assigned projects, students work individually and occasionally in small groups of typically 2-3 people to complete specific projects, with help and guidance provided by the instructor.

Required software and hardware

All students require access to a desktop or laptop computer on which they can write software using a specific set of applications.

i6 account

In addition to your NYU Home Account, we will be using a special computer account on a Unix Web server named i6.cims.nyu.edu which will be assigned to you automatically based on your enrollment. This is called an i6 account and we will use it to host our websites.

Connecting to the i6 server from “off campus” requires you to first be connected to NYU’s VPN service.
Common questions about i6 accounts are answered on this FAQ page.
If you forget your i6 password and would like to reset it, go to the i6 password reset page for instructions on how to do so.
If you do not receive notification that an account has been created for you, check your spam, and try to reset your password using the link above.

Third-party software

In this class, we will be using GitHub, Discord, and Figma - standard tools in industry. The use of this software is for educational purposes only. This is third-party software, which means that it is not an NYU-supported service that has data privacy, FERPA, and security protections in place. Assessments in this course (graded work, including any exercises, quizzes, exams, etc) are structured so that no highly sensitive personal information is needed to use these tools, and we encourage students to not disclose sensitive information to any of these tools. But note that we are subject to the terms of use and data privacy set by these platforms’ developers.

To raise your awareness of this issue, we ask that you complete a consent form, which contains an overview of this issue and links to the privacy policies for you to review. If you have any concerns about consenting to use these tools, please let us know as soon as possible.

Grading

You will receive a grade calculated mechanically on the following rubric:

15%: Quizzes
35%: Exercises
25%: Midterm exam
25%: Final exam

Attendance may be taken into account in the final grade.

Letter grades

The final class grade will be assigned as follows:

Grade Range	Letter Grade
93-100%	A
90-92%	A-
87-89%	B+
83-86%	B
80-82%	B-
77-79%	C+
70-76%	C
60-69%	D
0-59%	F

Notification of grades

Students will be sent their complete individual grades via email approximately once per week.

At any moment, students can request the latest copy of their grades be automatically sent to them by submitting an online form.

Quizzes

Quizzes are completed outside of class using Google Forms. You must be logged into Google with your NYU Net ID account in Google in order to view the Quizzes. Quizzes are submitted by submitting the Google Form, i.e. you click the Submit button.

If you see an error message indicating you do not have permission to view a Quiz, it is because you are not logged into the correct NYU Net ID account… log out and then log back in with the correct account.

Exercises

Exercises are usually begun in-class with the remainder completed outside of class.

All exercises are submitted by pushing code to GitHub.

we will cover how to push code to GitHub
unless you have good reason to do otherwise, follow best-practices for all basic file names and file extensions

Late policy

All assigned work is due before class on the due date indicated on the schedule

for every 24 hours that work is late, we apply a 10% penalty on the grade, up to a maximum penalty of 30%.
after 72 hours, we will no longer accept the work.

Extensions

Students are automatically granted 2 late assignment extensions of up to 3 days late each, with the exception that all assignments must be submitted before the last day of regular classes before the final exam period.

extensions must be used immediately upon submitting the work and cannot be retroactively applied later on.
when submitting an assignment for which you would like to use one of these automatic extensions, you must notify the grader that you are using the extension, otherwise your assignment will be rejected.
for any group work, each member of the group must use an extension (or lose points if none is available) for the entire group to submit work late.
No additional extensions will be given, except in the case of a documented medical emergency.

Regrade requests

If a student requests a regrade of any work, we will regrade the work in full, not just the part that the student believes has been mis-graded.

Academic Integrity

Working with others and leveraging all resources available to you is a prerequisite for success. This is different from copying, cheating, plagiarism, and mental laziness. All submitted work must be your own. There are very reliable systems we use to detect plagiarism in computer code, such as moss and compare50. If you submit any work that is not your own, you risk failure or worse.

Students are expected to adhere to the Computer Science department’s policy on academic integrity and the University-wide policy which supersedes it.

Generative AI

Students must adhere to the academic integrity policies outlined above regardless of whether they use generative AI tools or not. All restrictions on sharing, communicating, collaborating, or copying to/from other humans apply equally to generative AI tools, unless explicit permission is given.

Students are advised that, while useful, material produced by generative AI often involves inaccuracies, plagiarism, copyright infringement, and is often done in a way that does not follow the best practices required of student work in this course.

Students are welcome to use generative AI tools, such as ChatGPT, Gemini, GitHub Copilot, etc to assist with researching and understanding the material covered in this course. However, all work submitted for a grade must be a student’s own, written or otherwise fully produced and fully understood by them without direct assistance writing or producing that work from other people or interactive programs like generative AI tools.