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Applied Data Science @ Columbia

STAT GR5243/GU4243 Fall 2021

Project 1 An R Notebook "Data Story" on the history of philosophy

The goal of this project is to write a data story on philosophy using the dataset for the Philosophy Data Project. Applying data mining, statistical analysis and visualization, students should derive interesting findings in this collection of philosophy texts and write a "data story" that can be shared with a general audience.

Datasets

Challenge

In this project you will carry out an exploratory data analysis (EDA) of philosophy texts and write a blog on interesting findings from your analysis (i.e., a data story).

You are tasked to explore the text corpus using tools from data mining, statistical analysis and visualization, etc, all available in R or Python and write a blog post using R or Python Notebook. Your blog should be in the form of a data story blog on interesting trends and patterns identified by your analysis of these philosophy texts.

Even though this is an individual project, you are encouraged to discuss with your classmates and exchange ideas.

Project organization

A link to initiate a GitHub starter codes repo will be posted on piazza for you to start your own project.

Suggested workflow

This is a relatively short project. We only have about two weeks of working time.

  1. [wk1] Week 1 is the data processing and mining week. Read data description, project requirement, browse data and study the R notebooks in the starter codes, and think about what to do and try out different tools you find related to this task.
  2. [wk1] Try out ideas on a subset of the data set to get a sense of computational burden of this project.
  3. [wk2] Explore data for interesting trends and start writing your data story.

Submission

You should produce an R or Python notebook (rmd and html files) in your GitHub project folder, where you should write a story or a blog post on the history of philosophy based on your data analysis. Your story, especially main takeways should be supported by your results and appropriate visualization.

Your story should NOT be a laundry list of all analyses you have tried on the data or how you solved a technical issue in your analysis, no matter how fascinating that might be.

Repository requirement

The final repo should be under our class github organization (TZStatsADS) and be organized according to the structure of the starter codes.

proj/
├──data/
├──doc/
├──figs/
├──lib/
├──output/
├── README
  • The data folder contains the raw data of this project. These data should NOT be processed inside this folder. Processed data should be saved to output folder. This is to ensure that the raw data will not be altered.
  • The doc folder should have documentations for this project, presentation files and other supporting materials.
  • The figs folder contains figure files produced during the project and running of the codes.
  • The lib folder (sometimes called dev) contain computation codes for your data analysis. Make sure your README.md is informative about what are the programs found in this folder.
  • The output folder is the holding place for intermediate and final computational results.

The root README.md should contain your name and an abstract of your findings.

Useful resources

R pakcages
Project tools
  • A brief guide to git.
  • Putting your project on GitHub.
Example
Tutorials

For this project we will give tutorials and give comments on:

  • GitHub
  • R notebook
  • Example on sentiment analysis and topic modeling