Course Syllabus
Contact Information
My interest has from an early age been in things mathematical - Physics and Math. In later life, I turned to Ancient Greek and statistics and earned a Ph.D. in Bayesian statistics at the University of Auckland New Zealand. These days I teach and develop classical and Bayesian statistics courses as well as innovate using the R workspace.
Course Details
For a list of course activities, scroll to the bottom of this page.
Course Prerequisites
This course has no prerequisites.
Course Materials
If you have previously taken MATH 5743, you should already have the textbook for this course.
- Statistics for Engineering and the Sciences 6th Edition by Mendenhall and Sincich - Chapters 8-10
- BookDown books: R for Data Science, R Packages (2e), Advanced R
- Please have on your machine the data for the course
Grades
Breakdown
| Activity | Description | Percentage |
|---|---|---|
| Labs |
At the end of most modules, you will complete a lab in which you will create & submit an R package. |
20% |
| Quizzes | Each week will contain an intensive open-book quiz on the resources. | 10% |
| Projects | There will be 2 major projects in the course. | 20% |
| Midterm | You will have a one-hour cumulative midterm exam. | 20% |
| Final | You will have a two-hour cumulative final exam. | 30% |
| Total | 100% |
Scale
| Percentage | Letter Grade |
|---|---|
| 90–100% | A |
| 80–89% | B |
| 70–79% | C |
| 60–69% | D |
| Below 60% | F |
Course Components
Module Overviews
For each module, you will have an introduction page that will provide an overview of the topic and prime you for what you are about to learn. It will also offer specific learning objectives and a to-do list to help you plan your week.
Quizzes
The quizzes will help to ensure that you have the foundational knowledge and/or component skills required for the course and will help inform you for the labs, where you will be asked to apply or extend your learning. This auto-graded quiz holds you accountable for the resources and will help to check your understanding of key concepts. These are intensive quizzes. Although they are open-book, they are very challenging.
Labs
Most weeks, you will submit a lab at the end of the week showing application of the concepts presented. These will typically involve the creation and submission of R packages. This will allow you and the instructor to assess your progress on the material and give you any necessary feedback that might help you to improve your performance on projects, quizzes, exams, and future labs. These will be graded based on their level of completeness. Perfection is not required, but you should show an appropriate level of effort to complete every part of the lab. Be sure to check the feedback comments even if you receive full credit, as you still may need to improve your process prior to attempting the projects or exams.
Projects
You will submit two major projects during the course. You will combine and apply knowledge to solve a real-world problem; through this, you will show your mastery over multiple concepts and applications presented cumulatively.
Resources
Each week, you will be given resources (textbook readings, pre-recorded videos, etc.) to help you in the application of the concepts. These resources will help you gain the foundational knowledge needed to do well on the weekly assessments and successfully participate in the Live Sessions.
Live Sessions
Every other week, you will attend or watch a recorded Live Session. The instructor will introduce the week’s project and offer good and bad examples of approaches to similar problems. Students will discuss problems as a group and do group activities to scaffold each other’s knowledge of the topic at hand.
Midterm & Final
Twice during the semester, you will have comprehensive exams that will allow the instructor to assess your mastery over the course content. You will have one hour to complete each exam.
Course Policies
Communication
You may contact me through the help board on Canvas or by email if you have urgent and/or personal needs. My email address is wayne.s.stewart@ou.edu
The course help boards will be checked each day and you should get a response in less than 24 hours, Monday-Friday.
Feedback
Grading should be accomplished in less than a week and you should get feedback and an explanation for the grade you obtained.
Late Policy
Late work will not be accepted except for exceptional circumstances - family bereavement, sickness etc. All work will be given a zero if not submitted on time.
Cheating
I expect students to help one another, especially in the early stages of an assignment. However, the mass and bulk of the work submitted should be the student's own work.
University Academic Policies and Student Support
Course Summary:
| Date | Details | Due |
|---|---|---|