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: Yao Li
Instructional Assistants:
- Jack McPherson
- Email: jackrymc@unc.edu
Grader:
- TBA:
Class:
- Lectures: MWF 1:25PM-2:15PM, Hanes 120
- Lab 320.404: T 12:30PM – 1:20PM, HN107
- Lab 320.405: TH 12:30PM – 1:20PM, HN107
- Lab 320.406: T 2:00PM – 2:50PM, HN107
- Lab 320.407: TH 2:00PM – 2:50PM, HN107
Office Hours:
- Instructor Office Hours: W 9:00AM-11:00AM, Office: Hanes 334
- TA Office Hours: TTH 11:00AM-12:00PM, Office: Hanes B30
Course Material
Date | Lecture | Slides | Tutorial |
---|---|---|---|
Aug. 18 | Welcome | ||
Aug. 20 | Data Visualization | ||
Aug. 22 | Data Visualization | ||
Aug. 25 | RMarkdown | ||
Aug. 27 | Data Transformation I | ||
Aug. 29 | Data Transformation II | ||
Sep. 1 | Labor Day (No Class) | ||
Sep. 3 | Data Transformation III | ||
Sep. 5 | Final Project Instruction | ||
Sep. 8 | Project Proposal Discussion | ||
Sep. 10 | Project Proposal Discussion | ||
Sep. 12 | Project Proposal Discussion | ||
Sep. 15 | Well-Being Day (No Class) | ||
Sep. 17 | Data Import | ||
Sep. 19 | Exploratory Data Analysis | ||
Sep. 22 | Exploratory Data Analysis | ||
Sep. 24 | Tidy Data | ||
Sep. 26 | Tidy Data | ||
Sep. 29 | Joins | ||
Oct. 1 | Joins | ||
Oct. 3 | Factors | ||
Oct. 6 | Factors | ||
Oct. 8 | Programming I | ||
Oct. 10 | Programming II | ||
Oct. 13 | Programming III | ||
Oct. 15 | Programming IV | ||
Oct. 17 | Fall Break | ||
Oct. 20 | Programming V | ||
Oct. 22 | Modeling I | ||
Oct. 24 | Modeling II | ||
Oct. 27 | Modeling III | ||
Oct. 29 | Modeling IV | ||
Oct. 31 | Modeling V | ||
Nov. 3 | Modeling VI | ||
Nov. 5 | Modeling VII | ||
Nov. 7 | Modeling VIII | ||
Nov. 10 | Modeling IX | ||
Nov. 12 | Modeling X | ||
Nov. 14 | Modeling XI | ||
Nov. 17 | Modeling XII | ||
Nov. 19 | R Shiny | ||
Nov. 21 | Final Presentation | ||
Nov. 24 | Final Presentation | ||
Nov. 26 | Thanksgiving | ||
Nov. 28 | Thanksgiving | ||
Dec. 1 | Final Presentation | ||
Dec. 3 | Final Presentation |
Homework Tracker
All homework assignments are to be submitted via Canvas.
Date assigned | Instructions | Due Date (Time) |
---|---|---|
Aug 18 | HW1(.rmd) | Aug 31 (11:55 PM) |
Aug 31 | HW2 | Sep 7 (11:55 PM) |
Sep 7 | A1([.zip]) | Sep 21 (11:55 PM) |
Sep 21 | HW3 | Sep 28 (11:55 PM) |
Sep 28 | HW4 | Oct 5 (11:55 PM) |
Oct 5 | A2([.zip]) | Oct 19 (11:55 PM) |
Oct 19 | HW5 | Oct 26 (11:55 PM) |
Oct 26 | A13([.zip]) | Nov 9 (11:55 PM) |
Nov 9 | HW6 | Nov 16 (11:55 PM) |
Nov 16 | HW7 | Nov 23 (11:55 PM) |
Nov 23 | A4([.zip]) | Dec 3 (11:55 PM) |
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, see the spreadsheet sponsored by Google.
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 Three Lectures
The Deliverer: Deliver Your Group from Evil by Editing and Submitting the Exploratory Analysis and Final Report via Canvas 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 | Canvas | Sep 7 (11:55PM) |
Proposal Meeting | Class | Sep 8 / Sep 10 / Sep 12 (Lecture time) | |
P2 | Exploratory Data Analysis | Canvas | Nov 2 (11:55PM) |
EDA Meeting | Lab | Week of Nov 3 to Nov 7 | |
P3 | Final Report | Canvas | Dec 5 (11:55PM) |
P4 | Presentation Slides | Canvas | Nov 20 /Nov 23 /Nov 30 /Dec 2 (11:55PM) |
Final Presentation | Class | Nov 21 /Nov 24 /Dec 1 /Dec 3 (Lecture time) | |
Above Average Final Projects from Previous Courses
Class Participation Record
To find your class participation record, see the spreadsheet sponsored by Google.
Acknowledgements
Thanks to Dr. Mario and Dr. Characiejus for sharing their course materials.
Reading
R for Data Science (R4DS)
R Programming for Data Science (RP4DS)
Text Mining with R (TMwR)
The Art of R Programming (AoRP)
ModernDive (MD)
Additional resources
This page was last updated on 2025-08-11 10:51:07.3205 Eastern Time.