Julia's intuitive syntax makes for a lean learning curve. The optional typing and the wealth of shorthand constructors result in readable, noise-free code, while the large collection of third-party packages makes accessing, manipulating, visualizing, plotting, and saving data a breeze.
Just by learning Julia's basic data structures and a few related functions, coupled with its powerful data manipulation toolset, we were able to implement an efficient data analysis workflow and extract valuable insight from the Iris flowers dataset. That was all we needed in order to perform efficient exploratory data analysis with Julia.
In the next chapter, we'll continue our journey by learning how to build a web crawler. Web mining, the process of extracting information from the web, is an important part of data mining and a key component of data acquisition in general...
