Scala Programming: Complete Guide to Scala Programming Language

In this post we will give you a simple introduction to the Scala programming language

Scala stands for “scalable language” and is a programming language released in 2004. Scala was created with the purpose of trying to mitigate some of the criticisms of the Java programming language. In fact, ​​Scala and Java have a close relationship and integration. 

Scala is used for functional programming and static systems. A static system is a system in which output at any instant of time depends on the input sample at the same time.

Scala is object-oriented and runs on the Java Virtual Machine (JVM). JVM allow Java programs to run on any device or operating system and helps manage and optimize the memory.

8 reasons to learn programming in Scala

However, if you are completely new to programming and just want to get started and don’t have a specific purpose to start with Scala, then I would recommend going for Python. This is just my experience, but Python is one of the easiest programming languages to get started with.

Summary: 8 reasons why you should learn Scala

If we summarise the above, reasons you should consider learning Scala

Scala is a general-purpose programming language, and has been applied to a wide variety of problems and domains. Scala is renowned for its broad range of use cases and is commonly used in big data projects.

Two real world Scala examples are:

• Twitter social network has most of its backend systems built using Scala
• Apache Spark big data engine is implemented using in Scala

Although Scala has never been as mainstream popular as languages like Python, Java, or C++, it remains extensively used in numerous companies and open source projects.

Finally, I would say that since Scala is a general-purpose programming language there are a wide range of applications and use areas for it. Additionally, as Scala is closely integrated with Java, almost all Java projects can be connected to Scala. Remember, Scala is designed to work smoothly with Java and Java libraries.

Those picking up Scala for Data Science in particular might want to also learn another language as well as we have seen some decline in popularity for Scala for Data Scientist. However, Scala programming is still pretty popular and a desirable skill to many employers. It will most certainly not just suddenly disappear anytime soon. 

A data scientist that works in Scala will typically also be working with another, or several others, programming languages, such as Python, R and SQL. 

Scala remains popular for data processing, distributed computing, and web development. It powers the data engineering infrastructure of many companies

Main differences between Python and Scala include

Scala, a compiled language, is considered as being about 10 times faster than an interpreted Python. As Scala is based on the JVM, it gets advantages in terms of speed from its many performance optimisations introduced over the years. Running on the JVM, statically typed, access to Java libraries, and multi-threading helps Scala’s performance.

However, by looking at the full picture, Python being an interpreted language allows for a faster development process as the developer doesn’t have to wait for the compilation after each change.

Python is very easy to learn with a highly readable syntax that makes it an excellent choice for beginners or anyone who wants to get fast. Python is well known as one of the easiest programming languages around.

Scala is not a difficult language to get started with, but it is considered a complicated programming language to master.

Python is a dynamically typed interpreted language, whereas Scala is a statically typed compiled language. As Scala is a statically typed language it makes it easier to find compile-time errors. While on the other hand, as Python is dynamically typed may cause it to be more prone to bugs

Python has a huge number of available libraries for various tasks and task complexities. Python has a wide range of very useful libraries for use in machine learning and data science projects, like NumPy, Pandas, Matplotlib, TensorFlow, Keras and more.

In the case of Scala, compared to Python, the number of libraries is not as many as Python, although there are many available Scala libraries. Additionally, the Scala libraries are more focused on Big Data processing. Scala enables the use of most of the JVM libraries.

Python’s community is significantly larger than Scala’s, and hence it offers more in terms of support and the ability to draw on libraries dedicated to different tasks. In addition, the number of available guides, forums, and posts about Python is larger due to it’s popularity.

However, this doesn’t mean that Scala doesn’t have a strong developer community, it certainly does, helped by the fact that it is also an open source programming language. 

There are plenty of free resources available online to learn Scala

Read more at: The official Scala Documentation and Learning

Check it out: Scala tutorial at Tutorialspoint

Click here to check out the course

Check out the Scala tutorial here

Read more about the Intellipaat Scala tutorial here

According to Talent.com, the average Scala developer salary in the USA is $136,500 per year. Entry level positions start at $117,000 per year while most experienced workers make up to $175,500 per year.

Similarly, Glassdoor estimates that when taking additional pay and benefits into account, Scala Developer in United States can expect their total pay value to be on average $127,504.

In Scala, a library is a collection of modules that include pre-written code to assist with common tasks. The most popular Scala libraries include

1. Breeze: Breeze is a library for numerical processing, machine learning, and natural language processing in Scala. The primary focus of Breeze is on being generic, clean, and powerful without losing efficiency.
2. DeepLearning.Scala: Is a simple library for creating complex neural networks from object-oriented and functional programming constructs. The library allows you to build neural networks from mathematical formulas.
3. Epic: Epic is a structured prediction framework for Scala. In addition, Epic also contains classes for training high-accuracy syntactic parsers, part-of-speech taggers, name entity recognisers, and more.
4. Apache Spark MLlib & ML: Apache MLib is Spark’s machine learning (ML) library. The aim is to make practical machine learning scalable and easy. At a high level, it provides tools such as: machine learning algorithms: common learning algorithms such as classification, regression, clustering, etc. 
5. Apache PredictionIO: Apache PredictionIO is an open source machine learning server used to create predictive engines for numeros machine learning applications.  It enables developers and data scientists to quickly build and publish a machine learning engine to the web as a service
6. DeepLearning4J: Deeplearning4J is an open-source framework that provides a set of tools for working with deep learning algorithms. The Deeplearning4J framework includes extensive support in building, training, and deploying neural networks.
7. Spire: Spire is a numeric library for Scala which is intended to be generic, fast, and precise. Spire is used for numerical programming. 
8. ScalaLab: ScalaLab is a MATLAB-like environment for the Java Virtual Machine (JVM). ScalaLab has interfaces compiled within the core to support the implementation of simple, coherent, and efficient MATLAB-like interfaces for many Java scientific libraries. 
9. Smile: Smile is a comprehensive machine learning engine, with features such as classification, regression, clustering, association rule mining, feature selection, efficient nearest neighbor search, etc. Smile provides hundreds of advanced algorithms with a clean interface
10. Vegas: One of the great tools for data visualization with Scala is Vegas. Vegas is considered to be Scala’s alternative to Matplotlib in Python.