Class: TR, 955a and 1135a, in Amos B010
Recitation: TBA
My TA and I will be available during our office hours to address your course content questions. I am happy to meet with you by appointment if you are unable to visit during my office hours.
| Contact | Web | Hours | |
|---|---|---|---|
| Chris Cornwell | cornwl.github.io | cornwl@uga.edu | W, 230-330p |
| Dawson Lane | dawson.lane@uga.edu | TBA |
My office is Ivester E353. You will find Dawson in Amos B362. Dawson 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 5000 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.
The BUSN 5000 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 during class and studying course content outside of class. We strongly recommend that you purchase a copy and bring it to class each day. Viewing the slides on your laptop would be beside the point.
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 “Gábors” text, as it has the most comprehensive treatment of the topics covered in BUSN 5000. Anyone considering continuing on from BUSN 5000 to Terry’s MSBA or a similar program should buy it.
Beginner
Intermediate
Next Level
There are many other good online resources for statistics coverage with R programming tossed in. Check out R for Data Science and Data Science in R: A Gentle Introduction. Finally, there is R for Excel Users for those wanting some guidance in transitioning from spreadsheets to a proper scripting language.
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. If you haven’t already, first download and install R; then download and install RStudio. Follow these instructions.
TAL is a resource supported by the Ivester Institute for Business Analytics and Insights (IIBAI) for all Terry students enrolled in business analytics courses. It is located in Orkin D207 and directed by Dr. Katie Ireland (katherine.ireland@uga.edu). Her team provides free tutoring in course concepts and R coding. You should regard TAL as the first stop for help with course content and assignments. TAL has its own eLC course page, where its operating hours and learning resources are posted. All BUSN 5000 students will be subscribed.
Your performance will be evaluated on the basis of Dailies, homework assignments, a course project, and in-class tests weighted as follows:
| Assessment | Number | Total |
|---|---|---|
| Dailies1 | 25 | 10% |
| Homework2 | 10 | 30% |
| Project | 1 | 20% |
| Test 1 | 1 | 20% |
| Test 2 | 1 | 20% |
1 There are 28 class periods. The Week-0 Dailies do not count toward your grade, and there is no Daily on the Part I exam day.
2 We no longer grade the homework assignments because the variance in homework scores has mostly vanished over the last few semesters, with most students recording perfect scores. Now we bake in the perfect scores up front, assigning each student a homework average score of 100 in the overall course average calculation. This policy will keep overall course average distributions aligned with the past and provide some cushion against potentially greater variance in Dailies scores, where the accountability for the homework assignments has shifted.
A Daily comprises up to 5 short active-learning exercises, drawn in part from the week’s homework assignment, that we conduct each class period. You will earn Dailies points by participating and providing correct responses: 75% is allocated to your participation; 25% is allocated to your responses.
Your overall Dailies score will be the percentage of total potential Dailies points you earned. If you are absent from class you cannot participate in the Daily. (This should go without saying, but just in case: participating in Dailies on behalf of students who are absent from class constitutes an academic honesty violation and will be prosecuted as such.) We will drop the 4 lowest Dailies scores. Entering your 81# incorrectly in the Daily form will result in a “lost submission” and 0 credit for that Daily.
The policy of dropping the 4 lowest Daily scores is to accommodate unforeseen events that prevent you from participating in a particular Daily. We don’t distinguish between excused and unexcused drops, so there is no reason to email anyone on the teaching team to document the reason for non-participation, 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.
Homework assignments are formative tutorials that guide you through the key concepts in each course topic and include an empirical component involving R. They are designed to build mastery and prepare you for the tests. Taking them seriously is essential for success. The course schedule includes dates by which each assignment should be completed. You should commit to that cadence. To encourage your commitment, there is weekly accountability through the Dailies.
The 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.
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” it all together in a slide deck. You may consult TAL staff, the TA, or the instructor for assistance, and you may use AI, but your deliverable must be completed and submitted by you.
The 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 Progress Check submission is complete and correct. Even if you do not score the bonus points, participating in the Progress Check will provide valuable feedback for completing the Project successfully.
The course schedule lists all Project-related deadlines. Mark these dates in your calendar. Late submissions will not be accepted, period.
The tests are summative assessments of the key concepts covered in each section of the course. We believe real mastery of the course material is demonstrated by high-level performance on both formative and summative assessments.
Exam I is scheduled for Thu, Sep 24. Exam II will be held at the date and time determined for each section by UGA’s final exam schedule. Mark these dates on your calendars too. We expect all students to take the tests during their prescribed time. If you know now that you will not be able to take either test during the period it is scheduled, you should drop this class.
Attendance is not required, but missing class means missing a Daily. Make no mistake, the data on Dailies participation show that regular class attendance predicts success in BUSN 5000.
You will be ranked relative to other students in the class according to your overall performance and grades will be awarded based on your class rank, with the class median performance assigned to the B+/A- range. We will use the plus/minus system to make distinctions within grade categories. We do not “round up”.
BUSN 5000 has a strong no-electronic-device policy. That means no phones, earbuds, tablets, or laptops. The one exception will be on Data Fundamentals day, when we will get you set up to start the Project.
Phones and earbuds are a clear distraction, but why tablets and laptops? Because the data (here, here, and here) clearly indicate that their use in class harms learning, and learning is what we care about. If you have an Accessibility and Testing accommodation for in-class laptop or tablet use, please see me.
All devices should be stowed away and phones set to silent mode before class begins.
If you are cited for the unauthorized use of an electronic device, you will not be permitted to submit the Daily for the day, resulting in a grade of 0 for that Daily. Repeat violations of the electronic device policy risk more severe sanctions.
By now, everyone has their take on AI. Mine is that it represents the most important technological shift in my lifetime and will shape the future in profound, exciting, and unpredictable ways. You should absolutely learn to use it, by which I mean use it. There is no manual.
There are principles, though, that can guide your use. What is the question you want to answer, problem you want to solve, or project you want to conduct? Now you have (for a small monthly fee) a team of disembodied workers you can deploy to find the answer, solution, or deliverable. How you organize and direct them is a management problem, not an AI engineering task. Ethan Mollick is a person who shares this view. Follow his Substack if you would like to learn more about this perspective.
One thing Mollick (and others) are saying is that the days of the chatbot are behind us. We are now fully in the agentic age and operating well up the exponential, with every indication we are not near the top.
Okay, so what is my AI policy for this class? Like I said, you should use it. Bring your AI team to the homework assignments and the project, but do it wisely. Your AI team can help you become a far more productive learner or substitute for learning entirely. You should aim for the former or you might find yourself on the wrong end of a story like this. Which is to say, there will be no AI on the in-class exams, so like I said, be wise.
The Project Guide provides some guidance for using AI in coding, as well as links to other AI resources.
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 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 Dawson or me.
Course administration questions should first be directed to Dawson. If he 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.
“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.