Effective Scala Programming: Master Scala 3 Fundamentals

First, get up and running with Scala on your computer. Then, learn how to write and evaluate basic expressions and definitions. Last, learn how to model a business domain by defining case classes and sealed traits.

Manipulate collections of values by using the generic List data type from the standard library. Then, learn about the other generic data structures provided by the standard library (sets, sequences, and maps), and their respective use cases. All the collection classes have a comprehensive set of high-level operations to transform, filter, and iterate over their elements. Last, you will learn a couple of techniques to implement loops in Scala, and their tradeoffs.

How to write Scala projects larger than a worksheet? Learn how to define an entry point for a Scala project, and discover the tools you can leverage to write, compile, run, and debug your code. Then, use object-oriented programming techniques to break down a complex project into small modular components. Finally, we discuss when to use case classes versus simple classes.

The recommendation in Scala is to use immutable data types by default. Indeed, they have desirable properties such as being thread-safe and allowing local reasoning. However, in some cases, mutable variables lead to simpler code, as you will see in the lectures. We then dive into different testing techniques such as unit testing, property-based testing, integration testing, and mocking.

Programs automate repetitive tasks. Sometimes, writing a program is a repetitive task. We look into a unique feature of Scala that lets the compiler write some (repetitive) parts of your programs for you! Based on the expected type of a parameter, the compiler can provide a value for this parameter if it finds an unambiguous candidate value. This technique is often used to automatically generate the serializer and deserializer of a data type, solely based on its definition, for instance.

Scala supports a simple low-level mechanism for error handling: exceptions. We discuss the pros and cons of this mechanism, and we show alternative approaches that you can apply when you want to make it explicit in a method signature that it can fail with an error. We then move to a similar topic, input data validation, where we want to construct nice-looking error messages listing all the errors that the user needs to fix. We conclude with a couple of lectures showing how to work with asynchronous computations, and how to leverage the multiple CPUs of a computer by running tasks in parallel. You will see that the same idioms we used for writing data validation rules can also be used to write graphs of parallel computations.