Syllabus
CSci 39579: Introduction to Data Visualization¶
Department of Computer Science, Hunter College, City University of New York
Spring 2026
Description¶
3 hours, 3 credits: This course will introduce you to the tools and techniques for analyzing and visualizing data. It emphasizes on how to complement computation and visualization to perform effective analysis. We will cover methods from both sides, and hybrid ones that combine the best of both worlds.
Prerequisites: CSci 235, and one of: Math 160 or Math 260.
Instructor: Ryan Vaz Office hour: 4.00PM - 5.00PM TuTh North 1008 or by appointment
Grading Policy¶
Course Format:
Synchronous in-person lectures are each Tuesdays and Thursday, 7:00pm-8:15pm at North 1001E
Midterm examination during lecture and final examination during the registrar-assigned time slot during finals week
Expectations:
Completing homework is an essential part of the learning experience. Students are expected to learn both the material covered in class and the material in the textbook and other assigned reading.
Honor Code:
You are encouraged to work together on the overall design of the programs and homework. However, for specific programs and homework assignments, all work must be your own. As a general rule, do your own typing. Submitting work of others, or not safeguarding your work from copying, are academic integrity violations. You are responsible for knowing and following Hunter College’s Academic Integrity Policy:
Hunter College regards acts of academic dishonesty (e.g., plagiarism, cheating on examinations, obtaining unfair advantage, and falsification of records and official documents) as serious offenses against the values of intellectual honesty. The College is committed to enforcing the CUNY Policy on Academic Integrity and will pursue cases of academic dishonesty according to the Hunter College Academic Integrity Procedures. All incidents of cheating will be reported to the Office of Student Conduct in the Vice President for Student Affairs and Dean of Students office.
Lecture Participation:
Participation in lecture is measured by collected classwork. If you miss or do poorly on a classwork, your grade on the final exam will replace the missing or low grade.
Programming Assignments:
Assignments are posted on the class website, usually two weeks before the due date. They reinforce concepts covered in lecture and lab and serve as building blocks for the classwork and the semester-long project.
To receive full credit for a program, the program must perform correctly, must include comments, be written in good style (following PEP 8, using Pylint), and be submitted via Gradescope.
While you may consult and discuss with others, this is an individual assignment, and all code must be written and typed by you. All programs are run through similarity review and copied code is reported to the Office of Student Conduct.
No late homework is accepted. Instead, we drop the lowest programming assignment grade when computing the final programming grade.
Midterm Examination:
The midterm covers material from the lecture notes, code demonstrations, classwork, and submitted programs. There is no make-up midterm examination. Instead, your score on the final exam will replace missing midterm grades (the final exam grade will also replace the midterm grade if you earn a higher grade on the final than the midterm).
Final Exam: There is a 2-part exam consisting of written and coding questions:
Coding exam will be given either during finals week or the last week of class. It is a case-study style exam where you will be given a dataset and asked to perform various data science tasks on it within 24 hours. Expectation and rubric will be provided closer to the exam date.
Writing exam: more details to come ...
Project: A final project is optional for this course. The grade for the project is a combination of grades earned on the milestones (e.g. deadlines during the semester to keep the projects on track) and the overall submitted program. If you choose not to complete the project, your final exam grade will replace its portion of the overall grade.
Grades:
Participation: 10%
Programming Assignments: 20%
Optional Project: 25% (if you choose not to do the project, the final exam replaces the grade)
Exams:
Midterm Exam: 20%
Final Exam: 25% (or 50% if you choose not to do the optional project)
Emergencies:
Drop the lowest programming grade
Replace low or missing grades on the midterm, classwork participation and project with your final exam grade
To respect your privacy, there is no need to provide documentation to take advantage of the dropping/replacing grades policies. It is done automatically. See individual sections above for details. If you are going to miss more than 2 weeks of class and associated work, contact us, so we can make arrangements for you to take the course in a future term.
Materials, Resources and Accommodating Disabilities¶
Technology:
This is a programming-intensive course in the Python programming language. See the resource page on Brightspace for obtaining Python and the packages used, links for submitting assignments and assessments. All software used is freely available.
Computer Access:
A computer (capable of running Python 3) is needed to complete the on-line assessments, and programming assignments and projects. Hunter College is committed to all students having the technology needed for their courses. If you are in need of technology, see Student Life’s Support & Resources Page.
Accommodating Disabilities:
In compliance with the American Disability Act of 1990 (ADA) and with Section 504 of the Rehabilitation Act of 1973, Hunter College is committed to ensuring educational parity and accommodations for all students with documented disabilities and/or medical conditions. It is recommended that all students with documented disabilities (Emotional, Medical, Physical, and/or Learning) consult the Office of AccessABILITY. For further information and assistance, see their contact page.
Hunter College Policy on Sexual Misconduct:
In compliance with the CUNY Policy on Sexual Misconduct, Hunter College reaffirms the prohibition of any sexual misconduct, which includes sexual violence, sexual harassment, and gender-based harassment retaliation against students, employees, or visitors, as well as certain intimate relationships. Students who have experienced any form of sexual violence on or off campus (including CUNY-sponsored trips and events) are entitled to the rights outlined in the Bill of Rights for Hunter College.
Sexual Violence: Students are strongly encouraged to immediately report the incident by calling 911, contacting NYPD Special Victims Division Hotline (646-610-7272) or their local police precinct, or contacting the College’s Public Safety Office (212-772-4444).
All Other Forms of Sexual Misconduct: Students are also encouraged to contact the College’s Title IX Campus Coordinator, Dean John Rose (jtrose@hunter
.cuny .edu or 212-650-3262) or Colleen Barry (colleen .barry@hunter .cuny .edu or 212-772-4534) and seek complimentary services through the Counseling and Wellness Services Office, Hunter East 1123.
See CUNY Policy on Sexual Misconduct Link.
Course Objectives¶
At the end of the course, students should be able to:
Learn visual and computation techniques and tools, for typical data types.
Learn how to complement each kind of methods
Gain a breadth of knowledge
Work on real datasets and problems
Learn practical know-how (useful for jobs, research) through significant hands-on programming assignments.
Important Notes:¶
The syllabus is subject to change; more details will be provided as they become available.