Simon Fraser University
Engaging the World

Course Outlines

Fall 2026 - ECON 334 D100

Data Visualization and Economic Analysis (3)

Class Number: 1992

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 9 – Dec 6, 2026: Thu, 10:30 a.m.–12:20 p.m.
    Burnaby

  • Prerequisites:

    ECON 233, BUS 232, STAT 203, STAT 270, STAT 271 or POL 201, with a minimum grade of C-; and three units of ECON or SDA 100, with a minimum grade of C-.

Description

CALENDAR DESCRIPTION:

Explores how to recognize and learn from patterns in data using modern statistical software for the purpose of economic analysis. Introduces students to techniques for managing, visualizing, and analyzing data to answer real-world economic questions. Students with credit for POL 390, STAT 341, or STAT 452 may not take this course for further credit. Students with credit for ECON 387 under the title "Applied Data Analysis" may not take this course for further credit.

COURSE DETAILS:

Description

We live in a world of data. Economists and other social scientists are often called upon to help make sense of those data, and to use those data to make sense of the world around us. A key step in that process is data visualization: using data to create visual aids (charts, graphs, infographics) that tell a story and inform their viewer.

This course will introduce you to data visualization tools and principles. You will learn to use modern statistical software (R) to manage, visualize, and analyze data to answer real-world economic questions. To do that, you will learn to code in R. You will also learn some principles of good design in graphical representations of quantitative information, and lots of general-purpose programming skills that are useful outside of R. You will also be introduced to some of the statistical and econometric methods that applied researchers use in data analysis, but the focus of this course will not be the underlying statistical foundations of econometric methods. Those foundations are developed more fully in ECON 333, ECON 335, and ECON 435.

 

You will complete weekly assignments in R that will give you LOTS of practice applying the tools and methods that you learn. The key to doing well in this course is taking those assignments seriously and spending the time required to do them well. Assignments, readings, etc. will be posted on SFU Canvas.

This course requires no prior programming experience. We'll start from the basics. Upon satisfactory completion of the course, you will be able to:

  • Claim proficiency in R, a widely-used software environment for statistical computing and graphics.
  • Perform exploratory data analysis, including data visualization, using complex data from various sources.
  • Analyse data using simple econometric/statistical models to answer real-world economic questions.
  • Prepare clear and informative reports that effectively communicate patterns in social and economic data.

Topics

Topics will include an introduction to R, RStudio, and Quarto, obtaining real-world economic data from various sources; loading, tidying, and wrangling data; exploratory data analysis through data visualization; working with spatial data; an introduction to modeling and prediction; and the essentials of good programming including iteration, loops, pipes, and functions.

Grading

NOTES:

Your grade will be based on weekly individual assignments (30%); a group assignment (10%); in-class coding quizzes (30%); a final in-class assignment (10%); a final written exam (10%); and tutorial participation (10%).

The quizzes will primarily function as a check to ensure that students are completing the weekly assignments themselves and are learning basic coding skills. Students must pass the majority of quizzes and score higher than 50% on the final in-class assignment and written exam to pass the course.

Weekly assignments are to be completed using R, in RStudio, using Quarto. You are expected to work independently on the assignments. Failure to do so, or cheating of any other kind, will be prosecuted to the full extent of SFU's Student Academic Integrity Policy.

You will turn in your source code, output, and written answers in .qmd and .html (this will be described in detail through a demonstration during the Week 2 lecture). Assignments will require clean and clear communication and a quarter of the score will be for effective communication.

Please note that we will not accept or grade late assignments. So please be sure to submit your assignment on time. If you haven't finished your assignment by the due date/time, just submit your incomplete assignment before the due date/time for part marks.

For grading, we may randomly select one or two questions from the assignment. Your performance on the selected question(s) will determine your score for the assignment.

Materials

REQUIRED READING:

We will be using two free online textbooks: “R for Data Science” by Grolemund and Wickham (available free online https://r4ds.had.co.nz) and “Data Visualization” by Kieran Healy (available free online https://socviz.co). Other required reading/resources may be assigned throughout the semester.


Department Undergraduate Notes:

Please note that, as per Policy T20.01, the course requirements (and grading scheme) outlined here are subject to change up until the end of the first week of classes.

Final exam schedules will be released during the second month of classes. If your course has a final exam, please ensure that you are available during the entire final exam period until you receive confirmation of your exam dates. 

Students requiring accommodations as a result of a disability must contact the Centre for Accessible Learning (CAL) at 778-782-3112 or caladmin@sfu.ca.

***NO TUTORIALS DURING THE FIRST WEEK OF CLASSES***

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.

To learn more about the academic disciplinary process and relevant academic supports, visit: 


RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.