Note: It’s past the start date of Sept. 23, 2013. But you’re free to follow the notes and guides on this site as you wish. Also, check out Chrys Wu’s list of other Fall 2013 data journalism classes.

Table of contents


Note: This is subject to change depending on class overall technical level and availability of classroom computer lab resources.

Sept. 23 - Introduction to Data Journalism

What makes data journalism different or not from traditional journalism? And as an introduction to how information is structured in our day to day lives, we learn how to use the Web inspector.

Read the class outline here.

Sept. 30 - Introduction to Structured Data

The joy of spreadsheets, including the concept of delimiters, pivot tables, and crowdsourcing data-gathering.

Read the class outline here

Oct. 7 - Data Mashing

Single-source data is as unreliable as a news story with just one perspective. We’ll learn about the concepts of foreign keys and how to merge different data sets together to find interesting insights.

Read the class outline here

Oct. 21 - Visualization and Analysis

Keep things simple. Read the class outline

Workshop: Charting and Mapping via Spreadsheets and Google Fusion tables One of the easiest ways to build data-heavy visualizations (step-by-step guide to come)

Oct. 28: Killer Visualizations

Course Description

Name: WRIT1-CE9787, through NYU-SCPS, and part of the Certificate in Digital Journalism.

Date: September 23, 2013 - October 21, 2013

Time: Monday nights, 6:45PM - 9:15PM

Instructor: Dan Nguyen

NYU Class Homepage: Sign-up here.

The rise of personal computing and the Internet has transformed data journalism, long a niche of the newsroom, into one of the most visible fields in today’s media landscape. But while new technologies and techniques have made data journalism more accessible to audiences, they have also made data journalism increasingly intimidating to those who wish to practice it.

This course aims to examine data journalism at its core, through case studies and hands-on practice of modern data skills. Because the course is only five weeks, we focus on the practical concepts and tools needed to explore unfamiliar datasets to find worthwhile stories.

By the end of the course, students will be expected to have the experience and intuition to ask (and, to some degree, answer) these questions:

  • Why should anyone care about this data?
  • How can I explore, analyze, and compare this data?
  • How can I tell the story of this data?

This class will be taught in a small group setting with a focus on hands-on instruction.

Note: I won’t be teaching any programming, but if you already how to code, you’ll learn how to apply it in data journalism. And if you can’t code yet, you’ll learn how it can be useful – which is always good to have in mind before learning to code mid-career.


(We’ll go through the installation processes in class, or I’ll have written guides for them.)

Text and Resources

There is no textbook for this class. Here’s a list of recommended online articles and collections relevant to the coursework.

Policies and Academics

Academic Integrity

Plagiarism involves borrowing or using information from other sources without proper and full credit. Students are expected to demonstrate how what they have learned incorporates an understanding of the research and expertise of scholars and other appropriate experts; and thus recognizing others’ published work or teachings – whether that of authors, lecturers, or one’s peers – is a required practice in all academic projects.”

The full policy is located at:

It will be strictly enforced.

Students with disabilities

New York University is committed to providing equal educational opportunity and participation for students with disabilities. Any student who needs a reasonable accommodation based on a qualified disability is required to register with The Henry and Lucy Moses Center for Students with Disabilities (CSD) for assistance.

Grading Criteria

It’s assumed that students will have a wide range of technical skills and this will greatly impact the scope of work that they can produce for this class. For example, a student experienced in SQL will be able to analyze and chart a dataset involving millions of rows.

But this class will be graded less on technical expertise and more on critical thinking and deductive reasoning. If a student has trouble working with software but can explain in detail how he/she would inspect a real-world dataset, the process for joining other datasets to it, the expected insights from standard analytical methods, and the anticipated pitfalls and inherent limitations of the data, that student would meet this course’s standards.

Grading Components

  • 25% – Class Participation/Attendance
  • 25% – Quiz/Homework
  • 25% – Project
  • 25% – Final

Grading components as a pie chart

Extra credit: Name five things wrong with the above pie-chart

Other Data Journalism classes

Some of the curriculum and material for this class was inspired or taken from other data journalism classes that have also posted their syllabuses online:

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