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

4. 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

5. LaTeX

• Basics
  • LaTeX.pdf (in slides)
  • example_paper.tex (See proj_todai-asahi repository)
  • example_beamer.tex (See proj_todai-asahi repository)