이 과정에서는 데이터 과학자의 도구 상자에 있는 메인 도구와 아이디어를 소개합니다. 본 과정은 데이터 분석가와 데이터 과학자가 작업하는 데이터, 질문 및 도구의 개요에 대해 설명합니다. 이 과정에는 두 가지 구성 요소가 있습니다. 첫 번째는 데이터를 실행 가능한 지식으로 바꾸는 아이디어에 대한 개념적 소개입니다. 두 번째는 버전 관리, 마크다운, git, GitHub, R 및 RStudio와 같은 프로그램에서 사용할 도구에 대한 실용적인 소개입니다.
데이터 과학자의 도구 상자
Gain insight into a topic and learn the fundamentals.
18 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
R, R-Studio, Github 및 기타 유용한 도구 설정
데이터 분석가가 사용하는 데이터, 문제 및 도구 이해
필수 연구 설계 개념 설명
Github 리포지토리 생성
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
21 assignments
Taught in Korean
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There are 4 modules in this course
이 모듈에서는 데이터 과학과 데이터 자체를 소개하고 정의합니다. 또한 문제가 발생했을 때 데이터 과학자가 도움을 받기위해 사용하는 일부 리소스에 대해서도 살펴봅시다.
What's included
5 videos2 readings5 assignments5 plugins
이 모듈에서는 R 및 RStudio를 시작하고 실행하는 데 도움을 줍니다. 그 과정에서 두 가지 모두에 대한 몇 가지 기본 사항과 데이터 과학자가 이를 사용하는 이유를 학습합니다.
What's included
5 videos6 assignments5 plugins
이 모듈에서는 버전 관리와 데이터 과학자에게 버전 관리가 중요한 이유를 학습합니다. 또한 Git 및 GitHub를 사용하여 데이터 과학 프로젝트에서 버전 관리를 매니징하는 방법을 배우게 됩니다.
What's included
4 videos5 assignments4 plugins
이 마지막 모듈에서는 R Markdown을 사용하는 방법을 배우고 모든 성공적인 데이터 과학자에게 매우 중요한 세 가지 개념인 좋은 질문하기, 실험적 설계, 빅 데이터에 대해 소개합니다.
What's included
4 videos5 assignments1 peer review4 plugins
Offered by
Recommended if you're interested in Data Analysis
Google
Duke University
New York Institute of Finance
University of California San Diego
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