IDC 6940 - Capstone Projects in Data Science
Syllabus
This is a generic syllabus, please refer to Canvas or your semester instructor for more details.
Course Information
- Instructor: Instructor information
- Email policy: Please put IDC6940 in the email subject
- Class Meetings: Online Learning
- Course Materials: Canvas
- Office Hours: Email your instructors
Course Description
This course will provide you with an opportunity to apply the knowledge and skills that you have gained in the program. The capstone project will allow student to gain experience in data wrangling, data visualization, statistical modeling, machine learning, reporting, and presenting the results. Upon the completion of this course, you will have a data product (paper and slides) to show to potential hiring managers.
Student Learning Outcomes
At the completion of this course, students will be able to:
- Describe a research or business problem of interest
- Apply and compare statistical and machine learning methods
- Acquire, organize, summarize, and visualize data
- Communicate and formulate statistical analysis to an audience
- Collaborate with others
Course Materials
There is no required textbooks for this course. Course Materials are posted on Canvas. Lectures will be recorded and posted on Canvas.
Grading and Evaluation
The course grade will be determined as follows:
- Quizzes Project Progress (#6): 60%
- Oral Presentation - Slides (#1): 20%
- Final Paper Project (#1): 20%
Grade Distribution
Final course grades will be determined according to the following scale.
| Letter Grade | Weighted Score |
|---|---|
| A | 93%–100% |
| A- | 90%–92% |
| B+ | 87%–89% |
| B | 83%–86% |
| B- | 80%–82% |
| C+ | 77%–79% |
| C | 73%–76% |
| C- | 70%–72% |
| D+ | 67%–69% |
| D | 60%–66% |
| F | < 60% |
University Statements and Policies
This link includes additional syllabus statements that can benefit all UWF students: University Statements and Policies
Textbooks (not required)
R for Data Science, 2017, Hadley Wickham and Garrett Grolemond. Free acess: https://r4ds.had.co.nz/
Python for Data Science: https://aeturrell.github.io/python4DS/welcome.html
Foundations of Linear and Generalized Linear Models, Edition (2015). Author: Alan Agresti; ISBN-13: 978-1118730034. Free access with UWF account
Generalized Linear Models With Examples in R, Edition (2015). Author: Peter K. Dunn and Gordon K. Smyth ; ISBN-13: 978-1441901170.
Introduction to Data Science - Data Analysis and Prediction Algorithms with R, (2020). Author: Rafael A. Irizarry.