Fall 2022 - ECON 334 D100
Data Visualization and Economic Analysis (3)
Class Number: 4067
Delivery Method: In Person
Overview
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Course Times + Location:
Sep 7 – Dec 6, 2022: Tue, 10:30 a.m.–12:20 p.m.
Burnaby -
Exam Times + Location:
Dec 13, 2022
Tue, 3:30–6:30 p.m.
Burnaby
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Instructor:
Simon Woodcock
swoodcoc@sfu.ca
1 778 782-4524
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Prerequisites:
ECON 233 or BUS 232 or STAT 270 or POL 201, with a minimum grade of C-; ECON 103 with a minimum grade of C-, or ECON 113 with a minimum grade of A-, or ECON 105 with a minimum grade of C-, or ECON 115 with a minimum grade of A-.
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:
How can we recognize and learn from patterns in data to add value to a firm or to design more effective economic and social policy? During this course, we will learn to use modern statistical software (R) to manage, visualize, and analyze data to answer real-world economic questions. While we will use econometric methods in our programming, the focus of this course is not on the underlying statistical foundations of econometric methods. Those foundations are covered in, among others, BUEC 333 and ECON 435. Assignments,
readings, etc. will be posted on SFU Canvas.
Upon satisfactory completion of the course, students will be able to
- Claim proficiency in R, a software environment for statistical computing and graphics (https://www.r-project.org/).
- Perform exploratory data analysis, including data visualization, using complex data downloaded from various sources.
- Analyse data using simple econometric/statistical models to provide answers to real-world economic questions.
- Prepare clear and informative reports that effectively communicate analysis of patterns in social and economic data.
Topics:
Weeks 1-8: Data wrangling, data visualization / exploratory data analysis, programming skills
This part of the course will introduce students to using R through RStudio and RMarkdown
and will focus on (1) obtaining real-world economic data from various sources; (2) loading,
combining and “tidying” the data; (3) exploratory data analysis through data visualization;
and (3) programming using iteration, maps, pipes, and functions in R.
Weeks 9-12: Economic analysis of data
The final part of the course will focus on using basic econometric methods to analyse
data and will discuss prediction. Students will be introduced to modern machine learning techniques used by data scientists and will apply these techniques to economic data (model selection, LASSO, regression trees, random forests).
Grading
- Weekly Assignments 70%
- In-Person Quizzes 15%
- Final Exam 15%
NOTES:
Your grade will be based on weekly assignments (70%); and in-person quizzes to ensure the development of basic programming skills (15%); and a final exam (15%). The quizzes will primarily function as a check to ensure that students are completing the weekly assignments themselves and will be graded pass/fail. Students must pass the majority of quizzes and score higher than 50% on the final exam to pass the course. Weekly assignments are to be completed using R, in RStudio, using RMarkdown and are required to be independent work. You will hand in your source code, output, and written answers in .Rmd and .html (this will be described in detail through a demonstration during the first lecture). Assignments will require clean and clear communication and a quarter of the score will be for effective communication. Late assignments are not accepted (0 points). For grading, we may randomly select one or two questions from the assignment. Your performance on that question determines your score for that assignment.
Materials
REQUIRED READING:
We will be using the textbook “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 will be assigned throughout the semester.
REQUIRED READING NOTES:
Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.
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
SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html