Syllabus

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Intro

Week 1: Course Intro

• Date: 20230302

• Class

  • Course Introduction PDF

  • Install R, RStudio, & Rtools

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Data Science Basic

Week 2: R Basic Syntax (1)

• Date: 20230309

• Pre-class: Basic syntax, Vector, Array

• Class: Hands-on practice PDF

  • Data in use: COV19_data

  • Process of creating the data above [Code]

Week 3: R Basic Syntax (2)

• Date: 20230316

• Pre-class: Data.frame, List

• Class: Hands-on practice PDF

  • Data in use: List_KMP

Week 4: R Basic Skillset (1)

• Date: 20230323

• Pre-class: Read, Write, Condition, Repetition

• Class: Hands-on practice [Code]

Week 5: R Basic Skillset (2)

• Date: 20230330

• Pre-class: Function, Missing values, Outliers

• Class: Hands-on practice (Review Week 1 to 5)

• 데이터사이언스를 활용한 미디어연구에 대한 고찰 [PDF]

  • KCI 논문 검색 dbpia

  • SSCI 논문 검색 google scholar

  • 미디어 분야에서 데이터사이언스 활용한 논문 목록 리스트 [XLSX]

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Data Pre-processing & Visualization

Week 6: Data manipulation

• Date: 20230406

• Pre-class: Data wrangling (Base R & Tidyverse)

• Class: Hands-on practice

  • Introducing tidyverse [click]

  • Go to Posit cheatsheets [click]

  • Class code [click]

• Recommended books for the further study

  • Statistical Inference via Data Science (Modern Dive) (written by Chester Ismay and Albert Y. Kim)

    • is a comprehensive textbook that provides an accessible and hands-on approach to learning the fundamental concepts of statistical inference and data analysis using the R programming language.

  • R for Data Science (written by Hadley Wickham and Garrett Grolemund)

    • is an excellent resource for learning data science using R, covering data manipulation, visualization, and modeling with R. The book is available as a free online resource.

Week 7: Data visualization

• Date: 20230413

• Pre-class: Data-visualization, ggplot2, and (ggplot practice, optional)

• Class: Hands-on practice

  • ggplot2 world [click]

  • ggplot2 extension gallery [click]

  • Class code for understanding ggplot2 [click]

  • Class code for titanic data visualization [click]

• Recommended books for the further study

  • R Graphic Cookbook (written by Winston Chang)

    • is a practical guide that provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems

  • ggplot2: elegant graphics for data analysis published by Springer

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Text Data Analysis

Week 8: Text mining (1)

• Date: 20230420

• 전처리, 빈도 분석, 형태소 분석

• Code for the class [here]

• Recommended books for the text mining in R

  • Text Mining with R (written by Julia Silge & David Robinson)

  • R로 하는 텍스트마이닝 (written by 안도현 교수님)

  • Do it 쉽게 배우는 R 텍스트마이닝 (written by 김영우)

Week 9: 휴강

• Date: 20230427

Week 10: Text mining (2)

• Date: 20230504

• TF-IDF, 감정 분석

• Code for the class [here]

Week 11: Text mining (3)

• Date: 20230511

• 동시출현 분석

• Code for the class [here]

Week 12: Text mining (4)

• Date: 20230518

• 토픽 모델, 총정리 및 실습

• Code for the class [here]

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Media & DS Application

Week 13: 데이터사이언스를 활용한 연구 기획을 위한 개인 미팅

• 20230525(목) 외부 출장으로 인해 아래 시간 중 찾아올 것.

• 개인 단위 연구도 가능하고 그룹 연구도 가능!

• Office Hour:

  • 20230523 (화) 13:00 ~ 17:00

  • 20230524 (수) 13:00 ~ 16:00

Week 14: 데이터사이언스를 활용한 프로포절

• Date: 20230601

• Class:

  • 데이터사이언스를 활용해서 풀고 싶은 내용 (5분 발표)

  • 참고한 데이터사이언스를 활용한 논문들도 소개해주면 Even better!

Week 15: 최종 과제 제출

• Date: 20230608

• 프로포절에서 받은 피드백을 바탕으로 ’자유 형식’으로 아래 구글폼을 통해 제출

• 자유 형식이지만 약간의 Guide를 준다면,

  • 연구 배경: 연구의 중요성

  • 문제 제기: 연구의 독창성

  • 연구 질문: 풀고자 하는 문제

  • 연구 방법: 정의된 문제를 어떻게 풀지에 대한 내용

  • 예상 결과, 추진 계획 등