Scala Vs Python Which Language You Should Learn
As a programmer, you must have or would face this question on the path to career progression in the field of data science. While designing definitive distinctions between Scala programming and Python programming is a daunting task, it is important for you to eloquently understand the difference and chose a language goal, according to your career graph as well as interest areas in big data analytics.
If you are planning to learn both eventually, I'd recommend starting with Python programming. It is the closest to what you already know in data science (mostly imperative and object-oriented, it seems), yet it has functional programming features that will help you get used to the new. After you get used to functional programming, Scala may become easier to learn.
You may notice that changing paradigms is a lot harder than a simple change of syntax. But hybrid languages like Python may help, as you don't have to change your way of thinking and coding all at once, and instead doing that at your own pace.
On the other hand, if you only plan to pick one, take your future programming plans into account. There are a few questions that you must answer yourself before picking one language.
Do you want to leverage the JAVA ecosystem or the Python ecosystem?
If this were a different comparison, there might be a more definitive answer to that question, but with Java and Python, you'll pretty much have access to everything you need. If you know what you might be building, look in PyPI and Maven (or use google) and see what libraries are available. Compare the syntax, commit history, github stars, people using them, etc. Which ecosystem better supports your potential use case(s)?
Both scala programming and python programming are in demand, but as a Scala developer, you should probably have some familiarity with Java and the Java ecosystem. Learning the Java ecosystem is a much bigger task than learning the Python ecosystem, and you're likely going to be in the market competing against developers with a solid Java background.
Both the JVM and Python interpreter are fairly ubiquitous, but you may have better support on your target platform for one over the other.
Scala is faster than Python in the vast majority of use cases. Pypy is a Python interpreter with a build in JIT compiler. PyPy is very fast, but doesn't support most Python C extensions, so, depending on the libraries you're using, the cPython interpreter with C extensions for your libraries may outperform PyPy. Where performance is critical, Python often has fast modules written in C, so the particular libraries you're intending to leverage make a difference.
For data, anything matrix related in a JVM language without spending the time to write your own versions of BLAS/LAPAC is horrible. Cleaning data is a pain compared to Python as well. You are also way more likely to get hands on with technology in Python faster than Java due to its open source nature as well. PyCude for Nvidia existed before any Java counterpart. . The good scale code takes time and is admittedly more difficult to write than quality Python code which will discourage many researchers needing quick results who have little experience with producing quality code. On the upside for scala, the threading is better and it is picking up speed, just not as much as will be had from a language such as Python programming, which benefits from use by scientists and academia as much as from programmers and others.
If the explanation above, has not successfully guided you yet to choose between scala training and python training, here is a brief review that can bring about a definitive answer.
Scala vs Python.
- Easy to learn for Java developers
- Has a great community
- You could do almost anything in Python
- There's nothing really new in Python
- Slow in runtime
- Many new features for Java/PHP/C++/JS developers
- As fast as Java thanks to JVM
- Not as verbose as Java code
- Shares libraries from Java community
- Scala community is a little bit lukewarm
- Lots of syntactic sugars - be cautious to learn and use them
- Language itself is still in evolution