BUSN 5000E is the online, asynchronous version of BUSN 5000 and is every bit as rigorous. The description, learning objectives, and topical outline are the same. The assignments have been adapted to the online format and the shorter calendar, but they are designed to assess the same thing. The message here is that you should not confuse the “E” with “easier”. If anything, the challenge will be greater because you will not have the disciplining forces associated with regular in-person class meetings and recitations.
Your success in BUSN 5000E will depend heavily on your motivation, commitment and organization. You should start by doing two things ASAP:
This course follows the schedule of the Summer Thru Term. All required course materials are available through eLC.
My TA, Abbi Cormier, and I will be available to guide you and address your questions as they arise. You should see us as facilitators, tutors and coaches.
| Contact | Web | Hours | |
|---|---|---|---|
| Chris Cornwell | cornwl.github.io | cornwl@uga.edu | W, 2–4p |
| Abbi Cormier | abbicormier.github.io | abigail.cormier@uga.edu | T, 1–3p |
Abbi is the primary contact for questions about course grades and administration.
The modern world is awash in a seemingly unlimited amount of data. Harnessing these data for decision-making begins with acquiring the raw information and ends with communicating the results of analysis. Along the way, the data are transformed for analysis and the analyst matches statistical methods to the task at hand. BUSN 5000E covers the data science skills necessary at every stage of the value chain, including data transformation; descriptive, explanatory and predictive analyses; and professional communication.
After completing this course, you should understand how to
The topical outlines for parts I and II of the course are provided below.
A detailed course schedule is posted on eLC and here. You should plan to follow it to a “T”. Each of the twelve topics corresponds to a module on the schedule.
The BUSN 5000E slide decks are written to be read rather than presented. You should regard them more like a textbook. To make them more useful, we have arranged with Bel-Jean’s to offer printed copies in a notes format (3 slides per page with space for note-taking). The packets are available in black and white (cheaper) or color (more expensive). The purpose of the packet is to provide structure for taking notes as you study course content. We strongly recommend that you purchase a copy and use it in this way. Viewing them on your laptop would be beside the point.
Most of the technical content in the slide deck packet is supported by instructional videos. They are generally 5–10 minutes long and designed to capture the steps I would take in class, working at the whiteboard, to explain a particular concept. Each is mapped to a corresponding slide or slides by its title.
There are no required texts for this course (other than the slides packet), but many useful ones. Here is a curated list where you can find the course content covered at a “skill-appropriate” level. If we were to recommend just one to buy, it would probably be the “Gabors” text, as it has the most comprehensive treatment of the topics covered in BUSN 5000E. Anyone considering continuing on from BUSN 5000E to Terry’s MSBA or a similar program should buy it.
Beginner
Intermediate
Next Level
†The schedule shows the relevant sections from the Gabors text, as well as those from Bueno de Mesquita and Fowler, and from Angrist and Pischke.
If you need a statistics refresher, you might start with Computational and Inferential Thinking: The Foundations of Data Science. It is the text for Berkeley’s Data 8 course, which is a modern take on the topics covered in introductory statistics courses. It includes basic instruction in Python as well.
There are many other good online resources for statistics coverage with R programming tossed in. Check out R for Data Science and Learning Statistics with R. Finally, there is R for Excel Users for those wanting some guidance in transitioning from spreadsheets to a proper scripting language.
In addition to the text recommendations, each module is associated with some IRL (“in real life”) content — articles, podcasts, and videos — that ties the course material to real-world settings. With the exception of the Uber story in module 2 and the field experiment in module 9, there is no direct accountability for the IRLs. They are offered for your edification.
Data analysis in this class is done in R, a free and open-source language for statistical computing and graphics. RStudio is a popular integrated development environment (IDE) for R that will greatly enhance your R experience. First, download and install R; then download and install RStudio. Follow these instructions.
Your performance will be evaluated on the basis of homework assignments, a project, and a test weighted as follows:
| Assessment | Number | Total |
|---|---|---|
| Homework | 10 | 30% |
| Project | 1 | 20% |
| Exam | 1 | 50% |
Homework assignments are formative graded tutorials that guide you through the key concepts in each course topic and include an empirical component involving R. We will drop the two lowest homework scores.
Homework assignments are delivered as Shiny apps running in the cloud. Links to each assignment are posted on eLC. We provide access to Shiny’s cloud service at no cost to you and you do not need an account to access it.
We have posted a video explaining the process of submission on eLC. Watch it and then watch it again. Submissions that do not comply with the process will not be accepted. To promote compliance, you will begin with “Homework 0”, which gives you the experience of completing and submitting the 10 substantive Homework assignments that follow. Homework 0 counts toward your grade, so failure to complete it will not only leave you unprepared to submit the others, but it will also exhaust one of your drops. Entering your 81# incorrectly in the Homework form will result in a “lost submission” and 0 credit for that homework assignment.
The Project is a summative assignment in which you draw on key course concepts to learn about an empirical relationship and document what you learn. You will use R and R Markdown to conduct the analysis and report your findings, “knitting” the two together in a slide deck. You may consult Terry Analytics Lab staff, the TA, or the instructor for assistance — but your deliverable must represent your work and be completed and submitted by you.
Our comprehensive Project Guide will be your companion from start to finish. You should read it carefully and refer to it often. It includes detailed instructions for completing the entire Project, including the required Pre-project Exercise and optional Project Progress Check. It also provides resources for learning about R and using AI.
The Pre-project Exercise takes you through the steps of establishing a workflow, knitting an R Markdown slide deck, and creating a PDF version for submission. It accounts for 5% of your Project grade and must be completed by the deadline indicated on the course schedule. Trust us, this hard line is for your benefit. The optional Project Progress Check covers about 60% of the project tables and figures. We will award 5 bonus points to your project grade if your Project Progress Check submission is complete and correct.
The exam is a summative assessment of the key concepts covered in each module of the course and will be accessible from 600a–800p EDT on Fri, Jul 31. If you are traveling outside the EDT zone on Jul 31, make sure you do your time-zone arithmetic correctly. There will be no sympathy for timing errors. Once you begin the exam, you will have 150 minutes to complete and submit it. Our comprehensive Exam Guide provides the instructions for completing and submitting it, as well as advice for performing well.
The exam accounts for half (50%) of your total grade. Why such a high-stakes assessment? We believe real mastery of the course material is indicated by a strong performance on a timed, summative assessment like a cumulative final exam. Placing a large weight on the final is intended to promote effort toward preparation. The homework assignments are low-stakes but serve as the training ground for the final exam. Because the homework assignments are untimed and open-book, they also provide opportunities to earn good scores. In a similar fashion, the project is set up for every student to successfully complete it and earn a good score. Together, the homework assignments and project comprise the other half of your course grade, balancing out the high stakes of the final.
We are serious about Homework deadlines, which are indicated in the course schedule. Late homework submissions will not be accepted.
The policy of dropping the 2 lowest Homework scores should accommodate unforeseen events that prevent you from submitting a particular Homework assignment by its deadline. We don’t distinguish between excused and unexcused drops, so there is no reason to email anyone on the teaching team to document your reason for dropping an assignment, whether it be for illness, an interview, car trouble, or whatever. Just take the drop and move on. Think of the policy as insurance. The drops are your allotted claims. Use them wisely because when they’re gone, they’re gone.
We are likewise serious about Project deadlines and Exam dates. Late Pre-project Exercise, Progress Check, and final Project submissions will not be accepted, and all students are required to take the exam during the prescribed window. If you know now that you will not be able to take the exam during the period it is scheduled, you should drop this class.
You will be ranked relative to other students in the class according to your overall performance and grades will be assigned based on your class rank. We will use the plus/minus system to make distinctions within grade categories. We do not “round up”.
Our communications to the class will generally come through the eLC Announcements tool, which functions like an instant messaging system. You should set your notification preferences to receive Announcements postings in the manner that suits you. We strongly encourage the SMS option. Regardless, you are responsible for information conveyed in the announcements.
Please address content and assignment-related questions to TAL staff first. If TAL staff members cannot solve the problem, please reach out to Abbi or me.
Course administration questions should first be directed to Abbi. If she is unable to resolve the issue, I will be happy to intervene.
When you write to any member of the teaching team, your email must have the following components:
.Rmd file and paste the
error message in the body of your email. Do not send
screenshots or photos.Omission of any of these features may cause your message to be rejected.
The in-person version of BUSN 5000 has a strong no-electronic-device policy. Why am I mentioning this here? Because we’re convinced that phones (and screens more generally) are a clear distraction to learning. The data (here, here, and here) strongly indicate their use in class harms learning and learning is what we care about. We’re pretty confident this is true outside of class as well, so we strongly recommend you put your phone away when working on this course.
My take is that you should view AI systems, like ChatGPT, Claude, and Gemini, as productive collaborators with whom you should learn to work, much like you would with a human. Unfortunately, there are no secret prompting strategies or special step-by-step manuals (also like with a human). For some practical guidance along these lines, I recommend that you follow Ethan Mollick’s substack.
As you probably have discovered, one place AI really helps is in coding. You can get coding help from the chat interface but you may prefer using Cursor or integrating GitHub Co-pilot with your favorite IDE. Here is how to get free access to GitHub Co-pilot as a student. Here are instructions for integration into RStudio. Alternatively, you may find you can get by just vibe coding with Claude Code or ChatGPT’s Codex.
“I will be academically honest in all of my academic work and will not tolerate academic dishonesty of others.” A Culture of Honesty, the University’s policy and procedures for handling cases of suspected dishonesty, can be found at honesty.uga.edu.
UGA Well-being Resources promote student success by cultivating a culture that supports a more active, healthy, and engaged student community.
Anyone needing assistance is encouraged to contact Student Care & Outreach (SCO) in the Division of Student Affairs at 706-542-8479 or visit https://sco.uga.edu. Student Care & Outreach helps students navigate difficult circumstances by connecting them with the most appropriate resources or services. They also administer the Embark@UGA program which supports students experiencing, or who have experienced, homelessness, foster care, or housing insecurity.
UGA provides both clinical and non-clinical options to support student well-being and mental health, any time, any place. Whether on campus, or studying from home or abroad, UGA Well-being Resources are here to help:
Additional information, including free digital well-being resources, can be accessed through the UGA app or by visiting https://well-being.uga.edu.
The Terry College of Business is committed to promoting an inclusive learning and working environment among its students, faculty, and staff. This class welcomes the open exchange of ideas and values freedom of thought and expression and provides a professional environment that recognizes the inherent worth of every person. It aims to foster dignity, understanding, and mutual respect among all individuals in the class.
The course syllabus is a general plan for the course; deviations announced to the class by the instructor may be necessary.