Data scientists like to work with tidy data because it makes the data easier to work with. Visualizations, data manipulation, and modeling are made much easier when working with tidy data. Common coding environments for data science, including R Studio, Pandas in Python, and related packages have been designed to work well with tidy data. The first critical step in investigating a dataset is tidying. We will take a look at each rule from R for Data Science and see how you can format a data frame for each donut that you, as a data scientist/baker can use to visualize, explore, or model your data.

Data Science sounds like something cool and awesome. It's pictured as something cool and awesome. It is a sexiest job of 21st century as we all know (I won't even add the link to that article:D). But all this is just a top of an iceberg. And today I would like to list all the methods and functions that can help us to clean and prepare the data.

Too often, Machine Learning is used synonymously with Data Science. Before I knew what both of these terms were, I simply thought that Data Science was just some new faddish word for Machine Learning. Over time though, I've come to appreciate the real differences in these terms. I've always wondered how misconceptions like these endure for so long -- my current working hypothesis: people are deathly afraid of looking stupid. Too afraid of asking someone "what is machine learning?