Course Syllabus
Contact Information
Dr. Charles Nicholson actively engages in research encompassing various facets of system engineering and data science, focusing on enhancing community resilience in the face of disruptive events through analysis of complex interdependent networks, predictive modeling, and optimization to support decision-makers. His research spans a broad spectrum of applications for natural hazards, public health and supply chain networks impacting national security. Professor Nicholson is the Director of the Data Science and Analytics Institute at the University of Oklahoma.
Course Details
For a list of course activities, scroll to the bottom of this page.
Course Goals
By the end of this course, you should be able to:
- Define and frame analytics problems.
- Evaluate data quantity, quality, relevancy, and sufficiency.
- Select and use appropriate analytical tools.
- Discover and communicate analysis insights.
Course Materials
Textbook: Kelleher, J.D, Namee, B.M., & D’Arcy, A. (2020). Fundamentals of Machine for Predictive Data Analytics, 2nd Edition. The MIT Press.
Required software: R and RStudio
- R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. You can download R here: The R Project for Statistical Computing
- RStudio is an Integrated Development Environment (IDE) that is used to code in R. It is also a free resource and can be found here: RStudio Desktop
Grades
Breakdown
| Activity | Description | Points/Percentage |
|---|---|---|
| Quizzes | 7 quizzes | 16% |
| Project Preparation Deliverables |
3 deliverables for producing the Final Project:
|
25% |
| Team Reports | 4 Team reports | 6% |
| Peer Feedback | 1 Peer Feedback submission | 8% |
| Discussion Board | 4 Discussions | 10% |
| Final Project Written Report | Written report | 20% |
| Final Project Presentation | Oral presentation | 15% |
| Total | 100% |
Scale
| Percentage | Letter Grade |
|---|---|
| 90–100% | A |
| 80–89% | B |
| 70–79% | C |
| 60–69% | D |
| Below 60% | F |
Grade Revision Policy
A student can request a grade review/revision within 1 week of associated grade posting. Grade review petitions after the 1 week window will be denied. To have an assignment grade reviewed, please email both the TA and the Instructor and explain the details of your revision request (e.g., what problem number, why the grade was incorrect and/or why you deserve more credit for a solution submission).
Course Components
Course Project Preparation
There are three project preparation deliverables:
- Preparation 1: Course project proposal
- Preparation 2: Initial Data Analysis
- Preparation 3: Initial Draft
Course Project Final Written Report
A 12-15 page report with an executive summary, background of the problem, methodology, results, conclusion, and optional reference and appendix pages.
Course Project Final Presentation
A 10 minute presentation with slides summarizing the problem, methodology, results and conclusion of your course project.
Self-Check Quizzes
Seven untimed quizzes assessing your knowledge of the course content presented in the textbook and recorded lectures.
Live Synchronous Sessions
Synchronous meetings are every other week (weeks 2, 4, 6, and 8) from 7:00 - 8:30 pm (CT). These are active meetings to work on project deliverables, present ideas and provide feedback to class members, as well as address questions about course ideas.
Course Policies
Communication
Please contact Dr. Nicholson via email (either directly or via Canvas). In your email subject line, remember to include the course number (ELM 5213). The professor will try to respond to your email within 24 hours if it is received M-F before 5p. Otherwise, there may be a delay (e.g., he does not typically respond to email on the weekend).
Feedback
You should expect grades and/or feedback within 1 week of assignment due dates.
Late Policy
This is a graduate course that will be completed in 8 weeks. Due to this, there is a strict policy regarding late work. There is a 10 minute grace window for every homework/project assignment, outside of that window, late work is NOT accepted. All discussions and quizzes must be submitted on time as listed in Canvas (the 10 minute window does not apply).
Academic Honesty
Cheating, plagiarism, or any act of dishonesty will NOT be tolerated. This policy applies to all parties involved in the incident. Never take credit for anyone else’s intellectual property, be it on an exam or homework assignment. This includes, but is not limited to, copying from another student’s paper, copying from a paper from a previous semester, using forbidden information on exams, and copying from published writings. Additionally, you are strictly prohibited from using online services such as Chegg.com to complete homework, quizzes, or exams in this course. Students are responsible for knowing the policies, procedures and expectations of the University of Oklahoma's Academic Integrity Code.
Plagiarism is the most common form of academic misconduct at OU. There is no college-level assignment that can be satisfactorily completed by copying. OU's basic assumption about writing is that all written assignments show the student's own understanding in the student's own words. That means all writing assignments, in class or out, are assumed to be composed entirely of words generated (not simply found) by the student, except where words written by someone else are specifically marked as such with proper citation. Including other people's words in your paper is helpful when you do it honestly and correctly. When you don't, it's plagiarism.
University Academic Policies and Student Support
Course Summary:
| Date | Details | Due |
|---|---|---|