Data Science

  • 데이터 사이언스는 데이터로 부터 새로운 통찰력과 지식을 얻기 위한 학문이자 실무분야

  • 4차 산업혁명의 근간에 데이터가 있으며, 이 분야는 컴퓨터 관련 전공자 외에도 모든 분야의 연구자들에게 기반지식으로 부상


Data Science Workflow

Hadley Wickham은 data science workflow를 다음과 같이 정의:

Source: R for Data Science by Garrett Grolemund and Hadley Wickham.

Source: R for Data Science by Garrett Grolemund and Hadley Wickham.


Import

  • CSV, XML, HTML, Json 등 다양한 형태의 데이터를 Web crawling 등 다양한 방식으로 수집하거나 R로 import 하는 방법 습득


Tidy

  • Tidy data와 이를 위한 Tidyverse 방법론 습득

  • SDMX, 표준의 중요성


Transform

  • DB를 활용한 대용량 데이터 핸들링 방법

  • high performance computation


Visualize

  • ggplot2


Model

  • Leanear Regression

  • Repeated Sales Methods

  • tibble과 List columns을 활용한 모델링 및 분석결과 단순화 방법론


Communicate

  • R markdown

  • shiny



Importance of Programming


Benefits to programming vs. GUI software

  1. Automation
  2. High performance
  3. Advanced analysis
  4. Reproducibility



Replicatiom vs Reproducible Research


Replication

Previously researchers focused on replication - can the results be duplicated in a new study with different data? In science it is difficult to replicate articles and research, in part because authors don’t provide enough information to easily replicate experiments and studies. Institutional biases also exist against replication - no one wants to publish it, and authors don’t like to have their results challenged.


Reproducibility

Reproducibility is “the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them.”