Exploratory Data Analysis/visual · Statistics
analyze data by looking at different type of graphs using ggplot in R, best practices for data visualization, D3 for interactive plots, web scrapping using R, and Git
Best practices in data visualization, beautiful ways to present a story with data.
Some tools that I learned about are: 1. static: R (base graphics / ggplot2) 2. interactive: Plotly, htmlwidgets, Shiny, D3 + ... 3. version control: Git / GitHub 4. communication: Rmarkdown +
Different kinds of visualizations and where to use them.
A lot of different types of showing data. Including interactive plots.
R coding in general and many useful data visualization guidelines and skills
Visualization strategies, Data exploratory analysis
tidyverse, data visualization, r studio. github, D3, CSS
The course helps get to know more people in the cohort during the times when everyone is remote and working. The course is oriented towards exploring and being hands-on with a lot of practical scenarios of graphs.
I learned a lot! Specifically, I learned about new kinds of data visualization, how to build them, and became more fluent in the programming languages needed for building. I have a full-time job and these analytical skills were immediately applicable to my day-to-day work. I also built relationships with multiple classmates. These were the first people I actually met from the masters program, and I really, really, really appreciated Professor Robbins making this relationship-building a priority!