326.212 Statistical Computing and Labs @ SNU
This is the course website for 326.212: “Statistical Computing and Labs” at Seoul National University in Fall 2021. Assignments, lecture notes, and open source code will all be available on this website.
Announcements
- 2021-11-16: Instruction for part 1, Q1 of the final project has been modified to cope with the changes after the first announcement of the project.
- 2021-11-11: Part 3 of the final project has been updated.
- 2021-10-18: There was a typo in Hint 3, Q2, Part 1. If you have downloaded the
project.Rmdfile prior to 2021-10-18, please download it again. - 2021-10-16: Final project has been uploaded.
- 2021-09-01: No lab session. Class video is for the introduction to the course.
- 2021-08-31: This is section where future announcement will be posted.
Instructor
Joong-Ho (Johann) Won
Email: wonj AT stats DOT snu DOT ac DOT kr
Class Time: Mondays 13:00 - 14:50 (lecture video upload); Wednesdays 13:00 - 14:50 (lab session video upload),
Office Hours: By appointment.
Textbook: R for Data Science by Hadley Wickham and Garret Grolemund
References: Advanced R (Korean translation); The Art of R Programming (Korean translation)
Syllabus: Link, in Korean
Course Objectives
By the end of this course, you will be able to
- acquire basic programming skills using the R programming language;
- proficiently wrangle, manipulate, and explore data using R;
- utilize contemporary R packages, especially the tidyverse;
- visualize, present, and communicate trends in a variety of data types;
- formulate data-driven hypotheses using exploratory data analysis and introductory model building techniques.
Course Overview
Assessment
The course will be graded based on the following components:
- Attendance (10%): Includes both the lecture and the lab sessions.
- Assignments (30%): You will be assigned a computational assignment to be completed using RStudio and the package knitr regularly throughout class.
- Quizzes (20%): Unscheduled quizzes will be frequently given throughout the class during the lab sessions.
- Final project (40%): The project will be a computational case study that brings together the techniques learned throughout the semester. The description of the project will be provided towards the midpoint of the semester.
Schedule
Overall, this course will be split into two main parts: (1) lecture sessions on the basics of how to code in R and (2) lab sessions for performing hands-on data analysis on real case studies and examples using R.
The following schedule is tentative, and is subject to change over the course.
| Week | Topic | Reading | Assignment | Due Date |
|---|---|---|---|---|
| 1 (9/1) | Introduction | Ch. 1 | Homework 1 | 2021-09-21 |
| 2 (9/8, 9/10) | Data Visualization I, II, Workflows, R Markdown | Chs. 2, 3, 28, 4, 6, 8, 27 | ||
| 3 (9/13, 9/15) | Data Transformation I, II | Ch. 5 | ||
| 4 (9/20, 9/22) | Data Transformation III, Exploratory Data Analysis I | Chs. 5, 7 | Homework 2 | 2021-10-12 |
| 5 (9/27, 9/29) | Exploratory Data Analysis II, III | Ch. 7 | ||
| 6 (10/4, 10/6) | Import and Tidy Data I, II | Chs. 10, 11, 12 | ||
| 7 (10/11, 10/13) | Import and Tidy Data III, Relational Data | Chs. 10, 11, 12, 13 | Final Project [Rmd] | 2021-12-10 |
| 8 (10/18, 10/20) | Strings I, II | Ch. 14 | Homework 3 | 2021-11-09 |
| 9 (10/24, 10/27) | Factors, Date and Times I, II | Chs. 15, 16 | ||
| 10 (11/1, 11/3) | Pipes, Functions I | Chs. 18, 19 | ||
| 11 (11/8, 11/10) | Functions II, Vectors I | Chs. 19, 20 | Homework 4 | 2021-11-30 |
| 12 (11/15, 11/17) | Vectors II, Iteration I | Chs. 20, 21 | ||
| 13 (11/22, 11/24) | Iteration II, Model Basics I | Chs. 21, 22, 23 | ||
| 14 (11/29, 12/1) | Model Basics II, Model Building I | Chs. 23, 24 | ||
| 15 (12/6, 12/8) | Model Building II, Final Project | Chs. 24, 25 | ||
| 16 (12/13) | Final Project |
Acknowledgment
Lecture notes for this course were arranged from the source code of the textbook available at https://github.com/hadley/r4ds.