Data Science Study
  • Books with Jupyter

Get started

  • Overview
  • Build your book
  • Publish your book online
    • GitHub Pages and Actions
    • Publish with Netlify
  • Configure book settings
  • Table of contents structure
  • Types of content source files
    • Markdown files
    • Jupyter Notebook files
    • Notebooks written entirely in Markdown
    • reStructuredText files

Write book content

  • MyST Markdown overview
  • Special content blocks
  • References and citations
  • Math and equations
  • Images and figures
  • Control the page layout
  • Execute and cache your pages
  • Formatting code outputs

Make your book interactive

  • Launch buttons for interactivity
  • Hide or remove content
  • Interactive data visualizations
  • Commenting and annotating
    • Hypothesis
    • Utterances

Advanced and miscellaneous

  • PDFs for your book
  • Custom Sphinx configuration
  • How-to and FAQ
  • Contribute to Jupyter Book

Analysis of Time Series Data

  • 데이터 분석의 단계별 목적 이해하기 (분석 싸이클 이해)
  • 분석을 이해하고 공감하는 자세 및 방향
  • 분석 단계별 의사결정을 위한 수학/통계적 언어를 이해하기
  • 학습방향과 알고리즘(Learning Style and Algorithms)
  • 시계열 데이터/분석과 기계학습의 차이
  • (시계열) 회귀분석 요약
  • 시계열 분석
  • 기계학습(Machine Learning) 알고리즘
  • 시계열 알고리즘
  • 비선형 시계열 알고리즘
  • 다변량 시계열 알고리즘

Reference

  • Gallery of Jupyter Books
  • MyST cheat sheet
  • The command-line interface
  • Glossary
Powered by Jupyter Book

Index

A | B | C | E | G | J | M | S | T

A

  • A second term

B

  • Binder
  • BinderHub

C

  • CommonMark

E

  • ExecutableBookProject

G

  • Google Colab

J

  • Jupyter-Cache
  • JupyterHub

M

  • MyST
  • MyST-NB
  • MyST-Parser

S

  • Sphinx
  • Sphinx-Book-Theme

T

  • Term one

By Raphael Kim
© Copyright 2020.