h3yfinn / codecamp_r_data_analysis Goto Github PK

Use R and apply exploratory data analysis techniques to explore a selected data set for distributions, outliers, and anomalies.

This project is intended for the the udacity 'Data Analysis with R' course. See here for details (https://classroom.udacity.com/courses/ud651/lessons/3334308585/concepts/33605985620923#)

In this project I intend to look into the data provided by Free Code Camp's, "2016 New Coder Survey". My main aim is to find statistics that apply to me and in turn learn from my analysis of this data set. Being an ex-student doing a full time Online Degree in data analysis I'm especially interested in those students in this data set that have been to university, are studying at least 40hours per week and are going down a data analysis/science pathway. I'd also like to explore the comparison between those students looking to do data analysis and those looking to do Front end dev. This spurs from my disinterest in Front end dev and therefore interest in any similarities/differences between their stats and data science stats.

The "2016 New Coder Survey" dataset is made up of 113 variables. Most of these variables are answers to survey questions, although a few are computer-generated (e.g. respondent ID and survey start/end times). More than 15,000 observations exist.

The str function output is long so I won't print it here. Please consult Free Code Camp's survey data dictionary (https://github.com/FreeCodeCamp/2016-new-coder-survey/blob/master/survey-data-dictionary.md). Boolean, numeric, and categorical types are the majority.

Free Code Camp is an open source community that helps you learn to code. They offer self-paced coding challenges, non-profit projects, certificates. They also connect you with people in your city so you can code together. The "2016 New Coder Survey" was an anonymous survey, done in 2016, of thousands of people who started coding less than 5 years ago.