Java for Data Scientists Essential Training

Leverage Java in your data science career. Learn how to use Java for two components of data science—data engineering and data analysis.

Introduction

• Welcome
• What you should know
• Using the exercise files

1. Getting Started with Java

• Java, data science, and IMQAV
• JVM languages
• Downloading software
• Installing software

2. Test-Driven Development

• Introduction to testing
• Types of tests
• Mock tests
• Code coverage

3. IntelliJ IDEA

• Windows, views, and modes
• Projects
• Editor basics
• Refactoring
• Code execution
• Debugging

4. Object-Oriented Java

• Object-oriented principles
• Primitives
• Strings
• Classes and attributes
• Classes and methods
• Classes and constructors
• Exception handling
• Enumerations
• Casting
• Generics
• Annotations
• Program flow control

5. Libraries

• Install and use libraries
• gson
• StringUtils

6. Regular Expressions (Regex)

• Introduction to regular expressions
• Literals
• Metacharacters and representations
• Predefined character classes
• Regex quantifiers
• Regex boundaries and anchors
• Regex examples

7. Reflection

• Introduction to reflection
• Introspect fields
• Introspect methods
• Introspect constructors
• Introspect annotations

8. Design Patterns

• Introduction to design patterns
• Singleton patterns
• Decorator patterns
• Visitor patterns

9. Applying Data Science

• Introduction to magic squares
• Magic squares algorithm
• Adjacency matrix
• Magic characteristics
• Building magic cubes

Conclusion

• Next steps