MS in Data Analytics and Business Computing
Data Visualization for Business Analytics

NYU Stern Policies

Please read the NYU Stern Policies for this course.

Course code: SHBI-GB 7140 B01

The course combines visual theory with intensive programmatic implementation. While it builds directly upon your foundational analytics tracks, it requires hands-on Python development to translate conceptual design choices into automated, boardroom-ready visualization pipelines.

Overview

Format: Half-Semester Course | 6 Sessions, 3 Hours each.

Program: MS in Data Analytics and Business Computing (NYU Stern)

The course builds core core competencies across three primary pillars:

1. Visual Psychology and Perception: Understanding how the human brain processes shapes, color axes, and positions before designing any technical chart assets.
2. Programmatic Optimization: Eliminating visual noise, calculating information densities, and automating clean, scalable visual pipelines via Python libraries.
3. Strategic Narrative Delivery: Learning how to transform exploratory internal models into explicit, persuasive, and highly clean data presentations targeted directly to executive leadership boards.

Prerequisites

• Foundational comfort with Python data operations (manipulating structures, arrays, and basic parameters).

Materials

The course curriculum relies systematically on four foundational literature texts:

• 1. Claus Wilke — Fundamentals of Data Visualization (2019) (Read first / Available Free Online)
• 2. Edward Tufte — The Visual Display of Quantitative Information (2nd Ed.)
• 3. Cole Nussbaumer Knaflic — Storytelling with Data (2015)
• 4. Alberto Cairo — How Charts Lie (2019)

Attendance and penalty for missing classes

Requiring attendance is necessary for several reasons. First, you incorrectly assume you can catch up on a missed class by watching a recording (if available). Videos do not engage your brain as much as a live class. Second, less than 20% of you watch the recording (if available). You are then lost in class, which provides the wrong signals to me as an instructor. Third, your absence hurts class discussions. Fourth, you miss out on feedback if you do not work through the questions I pose in class. Fifth, I lose the feedback since there are fewer questions.

The policy below will be in effect only after the add/drop period.

Without mandatory attendance, attendance is often below 50%. Therefore, though I dislike doing this, I penalize absences. If you anticipate being absent for good reasons, please email me well in advance. Please enter "Excused" on the attendance sheet described below to avoid the penalty if approved. If you miss a class due to emergencies and cannot tell me in advance, do not panic. Take care of the emergency first, and then email me. I will permit you to change the "Absent" to "Excused." But if you miss a class without a valid reason, there is a penalty, as stated below.

For sections meeting in 150-190 minute sessions, you will lose one grade (A to A-, A- to B+, B+ to B, B to B-, and so on) for EVERY missed session unless you were explicitly excused via email. Thus, if you miss two class sessions, you will lose two grades, and so on.

Please sit in the same seat in every class and display your name tags. For Zoom classes, you must keep your video on AT ALL TIMES. You must also have a good working headset or mic, as it is extremely rude to be inaudible and force me to ask you to repeat yourself. After entering the class, please mark yourself present on the OneDrive sheet within the first 20 minutes (link posted on Brightspace). You will be marked absent if you are more than 20 minutes late, unless it is due to factors beyond your control (traffic, subway delays, or interviews running late). You will also be marked absent if you leave the class early unless you have my permission or get it afterward. You will get an F in the course if you are caught cheating on the attendance sheet.

Exams and Grading

There are no midterms, in-class quizzes, or written final exams.

• Please read about the penalty for missing classes above.
• Programmatic Python Labs / Assignments: 50%
• Final Capstone Presentation & Slide Architecture: 50%

Help and Office

• Me: dgode@stern.nyu.edu, 212-998-0021, Office: KMC 10-86.
• Teaching assistant: Please check NYU Brightspace.

Curriculum Sessions