Syllabus AREC 330 Data-Driven Decision Making
When and Where
Lecture: Nutrien 135, Wed 1:00 - 1:50 PM
Lab: Nutrien 103, Fri 1:00 - 2:40 PM
Course Webpages
Who
Lauren Chenarides (Instructor), lauren.chenarides@colostate.edu, 970-491-2480, Nutrien 201
Office Hours: F 3-4PM & by appt https://usemotion.com/meet/lauren-chenarides/meeting
Jude Bayham (Instructor), jbayham@colostate.edu, 970-491-2836, Nutrien 203
Office Hours: W 2-3PM & by appt https://go.oncehub.com/jude_bayham
Zarif Rasul (TA), Zarif.Rasul@colostate.edu
Office Hours: Th 12-1PM Nutrien 165
Course Description
The world generates 2.5 quintillion bytes of data each day. One of the key modern business challenges is transforming that data into actionable insights that improve decision making (i.e., data-driven decision making). Transforming data into information is not magic. It requires understanding the decision problem, organizing and processing data, analyzing data, and finally presenting information. This course will introduce you to this process of transforming data into actionable insights and build a core set of competencies with two commonly used software: R and Tableau. Since we only have 16 weeks, the most important lesson you will learn in this class is how to teach yourself what you need to know in the future. You will learn these skills by completing the three group projects over the semester.
Course Objectives
Recognize how to efficiently acquire data from a variety of sources
Implement best practices for coding
Demonstrate data literacy
Use R and Tableau to read, manipulate, and organize data in a variety of structures and formats
Examine real-world business and policy questions using appropriate data, analytical techniques, and visualizations
Support business and policy recommendations by conveying findings as a comprehensive story in a manner appropriate for business settings and policy makers
Construct appropriate summary statistics and analyses to inform business and policy decisions
Design interactive data visualizations to inform business and policy decisions
Throughout the semester, course materials will indicate which of these 8 learning objectives the materials are contributing to by referencing the course objective (CO) number.
Course Learning Outcomes
This course will contribute to several DARE Learning Outcomes:
You will develop Technical Competence and Problem-solving Skills while studying Ag Business and Environmental and Resource Economics issues, not to mention, learning to code in R and developing visualizations in Tableau.
You will develop Communication Skills and Leadership skills as you work in teams on problem-driven projects and communicate the results of your analyses to the instructors and your peers.
Course Materials
All course materials are freely available on the course website. There is no single textbook that you need to purchase. Course deliverables will be submitted on Canvas unless otherwise noted.
The course will require the use of R (statistical computing software) and Tableau (business intelligence software). R is an open source platform free to anyone. Tableau is freely available to you while you are a student at CSU. You will need to use these tools outside of class, so we recommend installing them on your own computer. Otherwise, it will be your responsibility to use the computer labs outside of class to complete assignments.
Another option for accessing R is to use the DARE compute server. You can access RStudio on the server here: http://darecompute-01.aggie.colostate.edu:8787/. This link should simply work while you are on campus. While you are off campus, you need to active the virtual private network (VPN). You can find instructions for setting up the VPN here: https://it.colostate.edu/cybersecurity/globalprotect-vpn/.
We will use iClicker software to take attendance at lecture and assess understanding of the material. If you have not already joined the course, you can do so using this link: https://join.iclicker.com/BIDY
Course Structure
Projects The course is organized into four projects intended to develop different analytical skills. The projects will be completed in teams. The project in each unit will be completed in parts. The parts will be submitted as weekly assignments. However, the final project deliverable will be worth the majority of points. You will present each of your final projects to the class. You will record the group presentation on video for the instructors and classmates to view. You will review each others presentations highlighting strengths and weaknesses. One lab session will be devoted to a question and answer session where the instructors and students will ask questions of the presenters.
Teams Many careers require teamwork in some form. In this course, you will work in teams to complete your assignments and present your work. Team projects will train students to collaborate with peers as they likely will do in the workplace. Group work can also lead to freeriding so, students will evaluate themselves and each other after each project. The evaluation will provide the students the opportunity to explain their contribution and those of their peers. The instructors will be available to resolve disputes within a team. However, the most productive arrangement for this class (and in your future workplace) is to communicate expectations with your colleagues and do your part of the project.
Website Students will create a website that will serve as a repository for projects/assignments. If this is your first time creating a website, we recommend google sites - a free and easy to use website editing and hosting tool. It will also serve as a record of their work in a presentable format that can be shared within or outside of CSU. There are many other tools for building and hosting websites. Please come talk to us if you have another option in mind or would like learn a more advanced option.
Quizzes There will be quizzes approximately every two weeks. The quizzes will be administered on Canvas and must be taken in class on the indicated lab day. Quizzes will cover topics ranging from data processing to graphical presentation of information.
Attendance and Participation There will be iClicker questions incorporated into most lecture materials. These questions are intended to test understanding of lecture materials and provide the instructors with real-time feedback on comprehension. Responses will account for a small portion of the overall course grade.
Course Expectations and Policies
We will communicate with you through your CSU email (@rams.colostate.edu) and through Canvas. Email is the best way to reach us. When many students have the same kind of question, we may reply to the entire class or post documents on the class website.
During class, please turn off your cell phones and put them away. Be respectful of each other and your instructors/TA. Much of the course content will require the use of computers. Please use laptops and lab computers appropriately (do not watch media in class).
Course Outline
Project 00
What is Data-Driven Decision Making (D\(^3\)M)?
Introduction to data processing with R
Introduction to data visualization with Tableau
Developing a workflow; Breaking a project into parts
Project 01: Time Series Data and Forecasting
How price forecasting can influence business decisions?
Time series analysis
Forecasting
Telling a story with your data analysis
Project 02: Cross-Sectional and Spatial Data
How does a firm study customers to improve marketing and sales?
Exploratory Data Analysis
Introduction to spatial data processing
Maps in Tableau
How to present a statistical model
Project 03: Panel Data and Causal Inference
How do firms use data to understand whether a policy or intervention worked as intended?
Basics of causal inference
Dynamic plots in Tableau
Grading
Quizzes: There will be approximately 8 quizzes worth 20% of your final grade.
Projects: There will be four projects in this course. The first (introductory) project will be worth 15% of your final grade. The remaining three projects will each by worth approximately 20% of your final grade (75% total for projects).
Attendance and Participation: There will be iClicker questions incorporated into most lecture materials. Completing iClicker polls will account for 5% of your final grade.
Letter grades will be assigned based on the conventional CSU grading scale (93% or more = A, 90% - 92% = A-, 88% - 89% = B+, 83% - 87% = B, 80% - 82% = B-, 78% - 79% = C+, 73% - 77% = C, 70% - 72% = C-, 60% - 69% = D, less than 60% = F)
If you have made it this far reading the syllabus, kudos to you! Please send the TA (Zarif Rasul) an email with the subject “arec 330 extra credit” and tell him your favorite vegetable. You will receive 5 points of extra credit for completing this before class on Friday, Jan 20, 2023.
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