Main Focus
This course focuses on how technological advances associated with data science and artificial intelligence could enhance social studies education.
Students are expected:
to understand possibilities and limitations of those technologies for social studies education,
to develop their abilities to design teaching-learning contents by fusing the technologies and particular subject matters, and
to get to possess an introductory level of computer programming for the technologies.
Class Setting
LMS (Learning Management System)
eTL at Seoul National University (SNU eTL)
- Lecture notes, materials, references, and etc.
Class homepage: https://sangillee.snu.ac.kr/classes/2024_1_AI_Edu.html
Class Design
Classes are conducted in a combination of lectures and lab exercises.
Lectures include various topics such as:
Concepts and Principles of Data Science
Population Topics for Contents Development
Exploratory Data Analysis
Data Science and Communication
Data Science and Education
Web Scraping as Data Collection
Statistical Analysis and Machine Learning
Each lecture is accompanied by an R exercise.
During the final two weeks, each student is expected to give a presentation about designing social studies class contents for AI-Integrated education.
Student Responsibilities
Presentation
Each student should give a presentation on designing social studies class contents for AI-Integrated education.
Subject: one or two population Issues (low birth rate, aging, population cliff, regional extinction, school-age population)
Data: KOSIS or World Population Prospects 2022
Form: 20-minute-long presentation
Term Paper
- Each student should submit a term paper about the presentation above at the end of the semester.
Grading Method
Attendance and Participation (20%)
- Penalty will be given for each absence/lateness.
- Active participation might be awarded.
Presentation (50%)
- Overall performance will be evaluated.
Term Paper (30%)
- The format of the term paper can be freely chosen.
- Due date: 6/18/2024 (Tuesday)
References
Data Science
- Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund, 2023, R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 2nd edition, Boston: O’Reilly. (김설기ㆍ최혜민 옮김, 2019, R을 활용한 데이터 과학: 데이터 불러오기, 정리하기, 변형하기, 시각화하기, 모델링하기, 1판, 서울: 인사이트.)
- Bonnell, Jerry, and Mitsunori Ogihara. 2023. Exploring Data Science with R and the Tidyverse. 1st edition. Chapman; Hall/CRC.
- Irizarry, Rafael A. 2020. Introduction to Data Science: Data Analysis and Prediction Algorithms with R. Boca Raton: CRC Press, Taylor & Francis Group.
- Sarafian, Ron. Introduction to Data Science. https://bookdown.org/ronsarafian/IntrotoDS/.
- Estrellado, Ryan A., Emily A. Freer, Joshua M. Rosenberg, and Isabella C. Velásquez. 2024. Data Science in Education Using R. 2nd ed. https://datascienceineducation.com/.
- 백영민. 2023. R 기반 데이터 과학: 타이디버스(tidyverse) 접근. 서울: 한나래아카데미.
- 차영준ㆍ박진표, 2022, 데이터과학 입문을 위한 R과 타이디버스, 파주: 자유아카데미.
- 양윤석ㆍ오일석ㆍ강래형, 2019, R로 배우는 데이터 과학: 분석에서 예측을 위한 모델링까지, 서울: 한빛아카데미.
Data Visualization
Allchin, Carl. 2021. Communicating with Data. O’Reilly Media. (이한호 옮김. 2022. 데이터로 전문가처럼 말하기: 효율적 의사 전달을 위한 데이터 시각화와 비즈니스 스토리텔링의 기술. 서울: 한빛미디어.)
Dougherty, Jack, and Ilya Ilyankou, 2021, Hands-on Data Visualization: Interactive Storytelling from Spreadsheets to Code, Boston: O’Reilly. (김태헌 옮김, 2022, 핸즈온 데이터 시각화: 효과적인 데이터 시각화 전략부터 20가지 시각화 도구 사용법까지, 한빛미디어)
Kabacoff, Robert, 2024, Modern Data Visualization with R, Boca Raton: CRC Press.
Knaflic, Cole Nussbaumer. 2015. Storytelling with Data: A Data Visualization Guide for Business Professionals. New York: John Wiley & Sons. (정사범. 2016. 데이터 스토리텔링: 설득력 있는 프리젠테이션을 위한 데이터 시각화 기법. 서울: 에이콘.)
Knaflic, Cole Nussbaumer. 2019. Storytelling with Data: Let’s Practice! New York: John Wiley & Sons. (변혜정. 2019. 데이터 스토리텔링 연습: 연습 문제와 다양한 사례로 익히는 데이터 시각화 기법. 서울: 에이콘.)
Wilke, C., 2019, Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures, Sebastopol, CA: O’Reilly. (권혜정, 2020, 데이터 시각화 교과서: 데이터 분석의 본질을 살리는 그래프와 차트 제작의 기본 원리와 응용, 의왕: 책만.)
Wickham, Hadley. 2016. ggplot2: Elegant Graphics for Data Analysis. 2nd ed. Switzerland: Springer. (박진수 옮김, 2017. R로 분석한 데이터를 멋진 그래픽으로 ggplot2. 부천: 프리렉.)
Statistical Foundation
Tipton, Elizabeth, Arend M. Kuyper, and Kaitlyn G. Fitzgerald. 2022. Introduction to Statistics and Data Science. https://nustat.github.io/intro-stat-ds/.
Ismay, Chester, and Albert Y. Kim. 2019. Statistical Inference via Data Science: A ModernDive into R and the Tidyverse: A ModernDive into R and the Tidyverse. 1st edition. Boca Raton: Chapman; Hall/CRC. (양승훈 옮김, 2023, 데이터 과학을 활용한 통계, 파주: 자유아카데미.)
Bruce, Peter C., Andrew Bruce, and Peter Gedeck, 2020, Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, Second edition, Sebastopol, CA: O’Reilly. (이준용 옮김, 2021, 데이터 과학을 위한 통계, 서울: 한빛미디어.)
오진호, 2022, 기초통계학과 데이터 사이언스: R 활용, 파주: 자유아카데미.
Web Scraping
Pittard, Steve. 2022. Web Scraping with r. https://steviep42.github.io/webscraping/book/.
곽기영, 2022, R을 이용한 웹스크레이핑과 데이터분석, 서울: 청람.
Text Analysis
Hvitfeldt, Emil, and Julia Sigle, 2022, Supervised Machine Learning for Text Analysis in R, Boca Raton: CRC Press.
Silge, Julia, and David Robinson. 2017. Text Mining with R: A Tidy Approach. 1st edition. Beijing Boston Farnham Sebastopol Tokyo: O’Reilly Media.
한국R사용자회ㆍ안도현, 2022, R 텍스트마이닝, 오픈전자책.
김영우, 2021, 쉽게 배우는 R 텍스트 마이닝, 서울: 이지스퍼블리싱.
백영민, 2020, R을 이용한 텍스트 마이닝, 파주: 한울엠플러스.
Web Application Development
Granjon, David, 2022, Outstanding User Interfaces with Shiny, Boaca Raton: CRC Press.
Fay, Colin, Vincent Guyader, Sebastien Rochette, and Cervan Girard. 2022, Engineering Production-Grade Shiny Apps, Boca Raton: CRC Press.
Wickham, Hadley, 2021, Mastering Shiny: Build Interactive Apps, Reports, and Dashboards Power by R, Sebastopol, CA: O’REILLY. (이영록 옮김, 2022, R 사용자를 위한 Shiny 마스터 가이드, 서울: 인사이트.)
Machine Learning and Deep Learning
Lantz, Brett, 2023, Machine Learning with R: Learn Techniques for Building and Improving Machine Learning Models, from Data Preparation to Model Tuning, Evaluation, and Working with Big Data, Fourth edition, Birmingham Mumbai: Packt. (이병욱, 2024, R을 활용한 머신 러닝, 4판, 서울: 에이콘출판사.
Chollet, François, Tomasz Kalinowski, and J. J. Allaire, 2022, Deep Learning with R, Second edition, Shelter Island, NY: Manning. (박진수 옮김, 2019, 케라스 창시자의 딥러닝 with R, 1판, 파주: 제이펍.)
Lesmeister, Cory, 2019, Mastering Machine Learning with R: Advanced Machine Learning Techniques for Building Smart Applications with R, Third edition, Birmingham, UK: Packt Publishing. (김종원ㆍ김태영ㆍ류성희ㆍ이호. 2018, R로 마스터하는 머신 러닝: 업무에 활용할 수 있는 선형모델에서 딥러닝까지, 2판, 서울: 에이콘.)
장용식ㆍ최진호, 2020, 머신러닝을 활용한 R 데이터 분석, 파주: 생능출판.
Kuhn, Max, and Julia Silge. 2022. Tidy Modeling with R: A Framework for Modeling in the Tidyverse. 1st edition. Sebastopol, CA: O’Reilly Media.
Boehmke, Brad, and Brandon M. Greenwell. 2019. Hands-on Machine Learning with R. 1st edition. Boca Raton London New York: Chapman; Hall/CRC.
Introduction to R
RStudio Education Team, Finding Your Way To R.
김기환, 2023, R 프로그래밍(개정판), 웹북
곽기영, 2022, R 기초와 활용, 서울: 청람.
강전희ㆍ엄동란, 2018, 처음 시작하는 R 데이터 분석, 서울: 한빛미디어.
김영우, 2017, 쉽게 배우는 R 데이터 분석, 서울: 이지스퍼블리싱.
Calendar
| Week | Date | Contents |
|---|---|---|
| 1 | 3/5 | Orientation |
| 2 | 3/12 | Concepts and Principles of Data Science 1 |
| 3 | 3/19 | Concepts and Principles of Data Science 2 |
| 4 | 3/26 | Population Topics for Contents Development 1 |
| 5 | 4/2 | Population Topics for Contents Development 2 |
| 6 | 4/9 | Population Topics for Contents Development 2 |
| 7 | 4/16 | Idle |
| 8 | 4/23 | Exploratory Data Analysis |
| 9 | 4/30 | Data Science and Communication |
| 10 | 5/7 | Data Science and Education |
| 11 | 5/14 | Web Scraping |
| 12 | 5/21 | Statistical Analysis and Machine Learning |
| 13 | 5/28 | Idle |
| 14 | 6/4 | Designing Class Contents for AI-Integrated Education 1 |
| 15 | 6/11 | Designing Class Contents for AI-Integrated Education 2 |