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Data Management

This is the repository for the 2018 data management course at Osaka University.

Contents

  • Lecture slides
  • Jupyter Notebook files
    • python_basics.ipynb (written in Python)
    • scrape_speech.ipynb (written in Python)
    • R_basics.ipynb (written in R)
    • text_analysis_R.ipynb (written in R)
  • Data
    • cars.csv
    • cars.txt
    • syuuin_speech_tpp2017.csv
  • Class assignments

Prerequisite

  • You need to install Jupyter Notebook before opening ".ipynb" files. See Python.pdf for more details.
  • You need to install Python before running Python code in Jupyter Notebook. See Python.pdf for more details.
  • You need to install R and IRKernel before running R code in Jupyter Notebook. See R.pdf for more details.

Procedure (how to go through the material)

  1. Intro.pdf (in slides)
  2. Version control.pdf (in slides)
  3. Python
  • Basics
    • Python.pdf (in slides)
    • python_basics.ipynb
  • Data cleaning and analysis
    • Directory and Data Structure.pdf (in slides)
    • clean_data_py.ipynb (See data_todai-asahi repository)
    • analyze_py.ipynb (See proj_todai-asahi repository)
  • Application (web scraping)
    • syuuin_speech.ipynb
  1. R
  • Basics
    • R.pdf (in slides)
    • R_basics.ipynb
  • Data cleaning and analysis
    • clean_data_R.ipynb (See data_todai-asahi repository)
    • analyze_R.ipynb (See proj_todai-asahi repository)
  • Application (text analysis)
    • text_analysis_R.ipynb
  1. LaTeX
  • Basics
    • LaTeX.pdf (in slides)
    • example_paper.tex (See proj_todai-asahi repository)
    • example_beamer.tex (See proj_todai-asahi repository)