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The Typesafe Introductory Scala for Data Science Curriculam is designed to give experienced Data Developers and Data Scientists the knowledge to confidently start programming in Scala for data science tasks.
The course ensures they will have a solid understanding of the fundamentals of the language, the tooling and the development process as well as a good appreciation of the more advanced features. If students already have Scala programming experience, then this course could be a useful refresher, yet no previous knowledge of Scala is assumed.
Question: A higher-order function allows us to:
How does Scala determine types when they are not specified?
What are valid members in objects?
What do default values and named parameters help you avoid in your code?
What does the Scala REPL stand for?
What is not true about class parameters?
What is the maximum number of elements in a tuple?
What keyword does not exist in Scala?
Which accessibility rule is not true?
Which are valid collection types in the Scala standard library?
Which collection type has effectively constant time performance characteristics for all operations?
Which combination is thread-safe?
Which feature is not synthetically generated for case classes?
Which is not a valid data container type in Scala?
Which is the top-most collection type in the Scala collections hierarchy?
Which keyword constructs a new instance of a class?
Which language cannot be compiled to run on the JVM?
Which language features are true about Scala?
Which statement is not true about infix notation?
Which statement is true about field and method evaluation?
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