Course Syllabus - Introduction to Computer Programming

Intro to Computer Programming
New York University
Department of Computer Science

Course description

An introduction to the fundamentals of computer programming, which is the foundation of computer science. Students design, write, and debug computer programs. No knowledge of programming is assumed.

Credits

4 credits

Meeting pattern

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

Prerequisites

None.

Fully online modality

This course is fully online. Students will attend lecture and tutoring via Zoom and participate in discussions between classes using an online chat messaging platform. See the University’s helpful tips for using Zoom.

Links to the Zoom call for class lecture will be distributed via Brightspace. All other links and contact details will be distributed and discussed in class.

Learning objectives

Upon completing this course, students will be familiar with the fundamentals of computer programming in Python, a high-level programming language. Specific topics include:

Introduction to Programming Languages - familiarity with the general concepts of programming, shared across many high-level programming languages
Python basics - specific syntax and core features of the Python programming language
Working with variables and operations - manipulating the data generated and stored in the memory of a program
Control structures - using conditional statements and logic to control the flow of a program
Repetition structures - using loops to repeat blocks of code
Working with text - manipulating text using common string operations
Functions and modules - how to make code more modular and reusable, with less repetition
Lists - an aggregated data structure that groups together values and allows a programmer to perform batch operations on the group
File input and output - writing and reading data to/from simple text files stored on the computer’s drive
Dictionaries - mastery of another aggregate data structure that stores multiple key->value pairs
Introduction to Object Oriented Programming - a light intro to the concepts of an object-orientated approach to programming

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

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

Optional

We do not teach directly from any textbook. If you do purchase a textbook, this is a recommended textbook for this course:

Starting Out with Python (5th Edition), by Tony Gaddis. ISBN-13: 9780136912330

Additional helpful texts

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. Use tutoring for more involved questions and when you prefer a more immediate answer.

Tutoring hours (all times in Eastern Time):

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.

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.

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.

20%: Assignments
20%: Quizzes
30%: Exam #1
30%: Exam #2

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 completed outside of class. In general, there is one exercises for each main topic of lecture.

Most exercises include a set of automated tests that can be run when the student wishes to evaluate the correctness of their own work.

We will cover how to set up and run these tests in class.

All exercises are submitted by pushing code to GitHub.

we will cover how to use GitHub for this course, but the official GitHub Quickstart guide may be helpful.
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 granted.

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.

How to study

Everyone has their own style and way of learning. The following is a general study plan that is probably pretty good for most people but probably not perfect for anyone.

Come to class

You can’t realistically expect to do well in a course if you don’t attend and know what is discussed.

Plan to spend significant time alone doing work for the course

Do any required or suggested readings or video viewing, starting from the beginning
Do all of the homework exercises yourself, starting from the beginning
Complete 10 or more practice exercises at the end of each chapter of any of the recommended textbooks

Don’t move ahead until you’ve covered your behind:

work progressively through the material
only move forward once you have mastered the previous material
get help from tutors or the instructor with specific problems you can’t solve or questions you can't answer
try not to go to the tutors or the instructor before you have even tried to solve the problem yourself

Come to class and pay attention:

Print out the class notes, if available, and bring them to class
Write your own notes on this paper.
Turn off your phone in class and when studying
Turn off your computer in class and when studying
Get out of the habit of Googling everything. Try to liberate yourself. Use the knowledge you have from lectures and required or suggested readings or videos to solve problems.

Review anything and everything:

class notes
any examples the instructor has supplied
required or suggested readings or videos
exercises
quizzes

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.