Why should we use Julia for data science?

Now, you are all set up to learn and experience Julia for data science.

Data Science is simply doing science with data. It applies to a surprisingly wide range of domains, such as engineering, business, marketing, and automotive, owing to the availability of a large amount of data in all these industries from which valuable insights can be extracted and understood.

With the growth of industries, the speed, volume, and variety of the data being produced are drastically increasing. And the tools that have to deal with this data are continuously being adapted, which led to the emergence of more evolved, powerful tools such as Julia.

Julia has been growing steadily as a powerful alternative to the current data science tools. Julia's diverse range of statistical packages along with its powerful compiler features make it a very strong competitor to the current top two programming languages of data science: R and Python. However, advanced users of R and Python can use Julia alongside each of them to reap the maximum benefits from the features of both.

Julia, with its ability to compile code that looks and reads like Python into machine code that performs like C, has showed a lot of promise with its efficiency at generating efficient code using the type inference. It is also interesting to note that even the core mathematical library of Julia is written in Julia itself. As it supports distributed parallel execution, numerical accuracy, and a powerful type inference, such as Python, and diverse range of statistical packages, such as R, Julia is a very powerful programming language for the very rapidly evolving domain of data science.

Installing and spinning up the Julia terminal is very easy, as follows:

Then, fire up Julia's interactive session, which is also called as repl (read-eval-print loop). The terminal output would look something like this:

Now, you are all set up to learn and experience Julia for Data Science.