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:
Grader:
- Jake Mareno: jmareno@unc.edu
Class:
- Lectures: TTH 8:00AM-9:15AM, Hanes 120
- Lab 320.400 by Morgan: W 3:30PM – 4:20PM, HN107
- Lab 320.401 by Morgan: W 5:00PM – 5:50PM, CH104
- Lab 320.402 by Yuhao: F 10:10AM – 11:00AM, HN107
- Lab 320.403 by Yuhao: F 3:30PM – 4:20PM, DE203
Office Hours:
- Instructor Office Hours: W 2:00PM-4:00PM, Office: Hanes 334
- Morgan Office Hours: T 10:00AM-11:00AM and TH 3:00PM-4:00PM Office: Zoom
- Yuhao Office Hours: M 10:00AM-11:00AM and TH 2:00PM-3:00PM, Office: Hanes B50
Course Material
| Date | Lecture | Slides | Tutorial |
|---|---|---|---|
| Aug. 20 | Welcome | Slides | |
| Aug. 22 | Data Visualization | Slides | Tutorial 01(.rmd) |
| Aug. 27 | RMarkdown | Slides | Lecture 03 |
| Tutorial | Tutorial 02(.zip) | ||
| Aug. 29 | Data Transformation I | Slides | Lecture 04 |
| Sep. 3 | Well-Being Day (No Class) | ||
| Sep. 5 | Data Transformation I | Tutorial 03(.zip) | |
| Sep. 10 | Data Transformation II | Slides | Tutorial 04(.zip) |
| Sep. 12 | Final Project Instruction | Slides | |
| Data Import | Slides | ||
| Sep. 17 | Project Proposal Discussion | ||
| Sep. 19 | Project Proposal Discussion | ||
| Sep. 24 | Exploratory Data Analysis | Slides | Tutorial 05(.zip) |
| Sep. 26 | Tidy Data | Slides | Tutorial 06(.zip) |
| Oct. 1 | Joins | Slides | Lecture 10 |
| Oct. 3 | Factors | Slides | |
| Oct. 8 | Programming I | Slides | Tutorial 09(.zip) |
| Tutorial 10(.zip) | |||
| Oct. 10 | Programming II | Slides | Lecture 13 |
| Oct. 15 | Final Project Working Day (No Class) | ||
| Oct. 17 | Fall Break (No Class) | ||
| Oct. 22 | Programming III | Slides | |
| Oct. 24 | Modeling I | Slides | Guest Presentation |
| Oct. 29 | Modeling II | Slides | Tutorial 11(.zip) |
| Oct. 31 | Modeling III | Tutorial 12(.zip) | |
| Nov. 5 | Modeling IV | Slides | Tutorial 13(.zip) |
| Nov. 7 | Modeling V | Slides | |
| Nov. 12 | Modeling VI | Slides | |
| Nov. 14 | Modeling VII | Slides | Tutorial 14(.zip) |
| Nov. 19 | Modeling VIII | Slides | Tutorial 15(.zip) |
| Nov. 21 | Final Presentation | ||
| Nov. 26 | Final Presentation | ||
| Nov. 28 | Thanksgiving | ||
| Dec. 3 | Final Presentation |
Homework Tracker
All homework assignments are to be submitted via Canvas.
| Date assigned | Instructions | Due Date (Time) |
|---|---|---|
| Aug 20 | HW1(.rmd) | Aug 30 (11:55 PM) |
| Aug 30 | HW2(.rmd) | Sep 8 (11:55 PM) |
| Sep 8 | A1(.zip) | Sep 22 (11:55 PM) |
| Sep 22 | HW3(.rmd) | Oct 1 (11:55 PM) |
| Sep 29 | HW4(.rmd) | Oct 8 (11:55 PM) |
| Oct 6 | A2(.zip) | Oct 20 (11:55 PM) |
| Oct 20 | HW5(.rmd) | Oct 27(11:55 PM) |
| Oct 27 | A3(.zip) | Nov 10 (11:55 PM) |
| Nov 10 | HW6(.rmd) | Nov 17 (11:55 PM) |
| Nov 17 | HW7(.rmd) | Nov 24 (11:55 PM) |
| Nov 24 | A4(.zip) | Dec 4 (11:55 PM) |
Lab Tracker
Attendance to all labs is mandatory. Each week, your lab instructor will take attendance. During the lab session, students will be required to complete a lab assignment, which must be submitted no later than 30 minutes after the lab ends. All lab assignments are to be submitted via Canvas.
| Date | Lab | Materials |
|---|---|---|
| Aug. 21 or Aug. 23 | Getting to Know Each Other | Lab 1(.rmd) |
| Aug. 28 or Aug. 30 | Data Visualization | Lab 2(.rmd) |
| Sep. 04 or Sep. 06 | No Lab (Well-Being Day) | |
| Sep. 11 or Sep. 13 | Final Project Proposal | Lab 3 |
| Sep. 18 or Sep. 20 | Basic Data Transformation | Lab 4(.rmd) |
| Sep. 25 or Sep. 27 | Exploratory Data Analysis | Lab 5(.rmd) |
| Oct. 02 or Oct. 04 | Tidy Data Case Study | Lab 6(.rmd) |
| Oct. 09 or Oct. 11 | Relational Data | Lab 7(.rmd) |
| Oct. 16 or Oct. 18 | No Lab (Fall Break) | |
| Oct. 23 or Oct. 25 | Control Structures | Lab 8(.rmd) |
| Oct. 30 or Nov. 01 | Programming | Lab 9(.rmd) |
| Nov. 06 or Nov. 08 | EDA Report Discussion | |
| Nov. 13 or Nov. 15 | Modeling Basics I | Lab 10 |
| Nov. 20 or Nov. 22 | Modeling Basics II | Lab 11 |
| Nov. 27 or Nov. 29 | No Lab (Thanksgiving) |
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 16 (11:55PM) |
| Proposal Meeting | Class | Sep 17 / Sep 19 (8:00AM-10:00AM) | |
| P2 | Exploratory Data Analysis | Canvas | Nov 3 (11:55PM) |
| EDA Meeting | Lab | Week of Nov 4 to Nov 8 | |
| P3 | Final Report | Canvas | Dec 6 (11:55PM) |
| P4 | Presentation Slides | Canvas | Nov 20 /Nov 25 / Dec 2 (11:55PM) |
| Final Presentation | Class | Nov 21 /Nov 26 / Dec 3 (8:00AM-9:15AM) | |
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 2024-11-13 11:09:35.495014 Eastern Time.