Data analysis, science, & wrangling resources
Published in
2 min readMar 28, 2016
… from NICAR 2016; (Part 2 of a 2 part series; Intro to coding (Python, Ruby, & JS) resources)
The Computer Assisted Reporting Conference is held every year for students, editors, and data journalists. Why should you care? Because they have a lot of awesome talks for beginners.
This blog post highlights resources that relate to a better understanding and practice of data analysis and data handling in R and Python and Node, as well as data visualization. While this list is edited, you can see the whole list, which is curated by Chrys Wu.
Data analysis — non-coding
- A walkthrough of approaches through data handling and mapping — Working with big geodata (without messing up)
- What’s the story with algorithms?
- Reproducible Research and Analysis Sharing
- How to avoid rookie mistakes [in data organization & analysis]
- Evaluating data quality
Data — Python
- How to install Jupyter Notebook & Agate (step-by-step instructions)
- Python for data wrangling tutorial
- PyCAR Bootcamp materials: An extended intro to data analysis in Python
- Advanced data cleaning with Python: Machine learning techniques
- Pandas, hands-on
- Data visualization and analysis with Python
- Hands on with Machine Learning
Data — R
Communicating Finding — Visuals
- Information design for the human brain: Which, chart should I use, and why?
- Data visualization and analysis with Python
- Command line graphics
- Leaflet maps intro
- Intro to D3 with a simple Scatterplot!
- How to build VR interactives using DEM data and Three.js (tutorial)
- Examples of charts made from JQuery — Practical data visualization in JavaScript
Data — Other
- csvkit and other command line tools
- Data cleaning with Regular Expressions
- Building Twitterbots in Node
- Using the t and csvkit to quickly collect and analyze tweets from the command-line
This is part 2 of a 2 part series; part 1 was titled Intro to coding (Python, Ruby, & JS) resources.
