About the Course
See the syllabus for details. The course is divided into three rough parts,
- Core data science coding skills
- Modeling
- Advanced topics
Instructor: Jan Hannig
Instructional Assistants:
Lab Sections:
- 320.400 by Taylor: W 5:45 PM to 6:35 PM
- 320.401 by Pavlos: W 4:40 PM to 5:30 PM
- 320.402 by Sam: F 4:40 PM to 5:30 PM
Zoom Links:
- Lectures: TTH 2:00 PM to 3:15 PM
- 320.400 by Taylor: W 5:45 PM to 6:35 PM
- 320.401 by Pavlos: W 4:40 PM to 5:30 PM
- 320.402 by Sam: F 4:40 PM to 5:30 PM
- Instructor Office Hours: MW 2:00 PM to 3:00 PM
- Taylor Office Hours: M 10:00 AM to 11:00 AM and W 1:30 PM to 2:30 PM
- Pavlos Office Hours: M 10:00 AM to 11:00 AM and F 1:00 PM to 2:00 PM
- Sam Office Hours: TuTh 4:00 PM to 5:00 PM
Course Material
Date | Lecture | Slides | Tutorial |
---|---|---|---|
Jan. 19 | Welcome/Data Visualization | Slides | Tutorial 01 |
Jan. 21 | RMarkdown | Slides | Tutorial 02 (Final Plot) {Solution} |
Jan 26 | Data Transformation I | Slides | Tutorial 03 {Solution} |
Jan 28 | Data Transformation II | Slides | |
Feb 2 | Data Transformation III | Slides | Tutorial 04 {Solution} |
Feb 4 | Final Project Instructions | Slides | |
Feb 9 | Data Import | Slides | |
Feb 11 | Exploratory Data Analysis | Slides | |
Feb 16 | Wellness Day | ||
Feb 18 | CLASS CANCELLED - ice storm | ||
Feb 23 | Project Proposal Discussion | ||
Feb 25 | Tidy Data | Slides | Tutorial 05 |
Mar 2 | Web Scraping | Slides | Tutorial 06 |
Mar 4 | Joins | Slides | Tutorial 07 |
Mar 9 | Factors | Slides | |
Mar 11 | Wellness Day | ||
Mar 16 | Programming I | Slides | Tutorial 08 |
Mar 18 | Programming II | Slides | Tutorial 09 |
Mar 23 | Programming III | Slides | |
Mar 25 | Modeling I | Slides | |
Mar 30 | Group Work Day | ||
Apr 1 | Modeling II | Slides | Tutorial 10 |
Apr 6 | Modeling III | Slides | Tutorial 11 |
Apr 8 | Modeling IV | Slides | |
Apr 13 | Modeling V | Slides | |
Apr 15 | Modeling VI | Slides | Tutorial 12 |
Apr 20 | Modeling VII | Slides | Tutorial 13 |
Apr 22 | R Shiny | Slides | Tutorial 14 |
Apr 27 | Data Ethics | Slides | |
Apr 29 | Presentation | ||
May 4 | Presentation |
Homework Tracker
All homework assignments are to be submitted via Gradescope.
Date assigned | Instructions | Solutions | Due Date (Time) |
---|---|---|---|
Jan 21 | HW1 | Jan 28 | |
Jan 28 | HW2 | Feb 4 | |
Feb 4 | A1 | Feb 21 | |
Feb 18 | HW3 | Feb 25 | |
Feb 25 | HW4 | Mar 4 | |
Mar 4 | A2 | Mar 18 | |
Mar 18 | HW5 | Mar 25 | |
Mar 23 | A3 | Apr 8 | |
Apr 8 | HW6 | Apr 15 | |
Apr 15 | HW7 | Apr 22 | |
Apr 22 | A4 | May 4 |
Final Project Details
For the final project, each section of STOR 320 will be divided (ideally) into research groups of size 5. To ensure fairness, students will be assigned randomly based on the sample
function in R.
Research Group Assignments
To find your research group and your group’s in class presentation times, see file group.xlsx
in the Resources tab on Sakai.
Four Roles
Although everyone is responsible for the entire project, each member of the group will be assigned a specific role for accountability and consistency. These four specific roles are described as follows:
The Creator: Meet with Instructor to Propose Your Group’s Research Idea, Lead Designer in Slides
The Interpreter: Schedule and Meet with Instructor or Instructional Assistant to Share Findings from Exploratory Analysis, Evaluate Practice Presentation
The Orator(s): Give a Captivating 5-7 Minute Slideshow Presentation During the Last Two Lectures
The Deliverer: Deliver Your Group from Evil by Editing and Submitting the Exploratory Analysis and Final Report via Sakai Before the Deadline
Four Parts Including Point Values
This final project will be divided into four parts worth a total of 100 points. Each part will have a clear rubric as non-subjective as possible. The parts along with total point values are found below:
- P1: Project Proposal (10 Points)
- P2: Exploratory Data Analysis (20 Points)
- P3: Final Written Paper (40 Points)
- P4: Final Presentation (30 Points)
Due Dates of Individual Parts
Part | Description | Method of Submission | Due Date (Time) |
---|---|---|---|
P1 | Project Proposal | Gradescope | Feb 21 |
Proposal Meeting | In class | Feb 23 | |
P2 | Exploratory Data Analysis | Gradescope | April 6 |
EDA Meeting | In Lab | April 6-9 | |
P3 | Final Report | Gradescope | May 5 |
P4 | Presentation Slides | Gradescope | April 28 |
Final Presentation | In Class | April 29 and May 4 |
Above Average Final Projects from Previous Courses
Acknowledgements
Thanks to Dr. Mario, Dr Li and Dr. Characiejus for sharing their course materials.
Reading
R for Data Science (R4DS)
Text Mining with R (TMwR)
ModernDive (MD)
R Programming for Data Science (RP4DS)
The Art of R Programming (AoRP)
Additional resources
This page was last updated on 2021-04-27 10:19:20 Eastern Time.