Data Science Journey- R, Phyton or Julia? #datascience

Data Science Journey- R, Phyton or Julia? #datascience
I started my data science journey with R not Python, Julia, or any other programming language useful for data science. This isn’t because I think these tools are bad. They’re not! And in practice, most data science teams use a mix of languages, often at least R and Python. However, we strongly believe that it’s best to master one tool at a time. You will get better faster if you dive deep, rather than spread‐ ing yourself thinly over many topics. This doesn’t mean you should only know one thing, just that you’ll generally learn faster if you stick to one thing at a time. You should strive to learn new things throughout your career, but make sure your understanding is solid before you move on to the next interesting thing. I think R is a great place to start your data science journey because it is an environment designed from the ground up to support data science. R is not just a programming language, but it is also an inter‐ active environment for doing data science. To support interaction, R is a much more flexible language than many of its peers. This flexibility comes with its downsides, but the big upside is how easy it is to evolve tailored grammars for specific parts of the data science process. These mini languages help you think about problems as a data scientist, while supporting fluent interaction between your brain and the computer.
Tamires Soares