Java for Data Scientists Essential Training

Description

In this course, you will :

• Learn how to use Java for data engineering and data analysis, two components of data science.
• Instead of delving into every aspect of Java, instructor Charles Kelly focuses on a few key topics that will help you learn how to use Java in your data science career.
• revolves around the IMQAV (ingest, model, query, analyse, and visualise) model, which is a data science workflow framework.
• explains test-driven development and object-oriented design
• demonstrates how to use the skills learned in the course to create magic squares and sudoku puzzles.

Syllabus :

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

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