Java for Data Scientists Essential Training

Learn how to use Java for two components of data science—data engineering and data analysis. Instead of poring over every facet of Java, instructor Charles Kelly focuses on a selection of valuable topics that will help you learn how to leverage Java in your data science career. This course revolves around the ingest, model, query, analyze, and visualize (IMQAV) model, which is a framework for data science workflows. Charles goes over test-driven development and object-oriented design. Using the free community edition of IntelliJ from JetBrains, he presents Java examples including Java classes, methods, operations, and libraries. Plus, Charles shares how to apply the skills that you learned in the course to create magic squares and sudoku puzzles.

Topics include:

  • The IMQAV model
  • Downloading software
  • Installing and setting up a Java coding environment
  • Mock tests
  • Code coverage
  • Using windows, views, and modes in IntelliJ IDEA
  • Creating classes and attributes
  • Creating constructors
  • Casting variables
  • Matching literals with regular expressions
  • Libraries
  • Regular expressions
  • Design patterns

Course Timeline:

Java for Data Scientists Essential Training Welcome

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Java for Data Scientists Essential Training What you should know

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Java for Data Scientists Essential Training Using the exercise file

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Java, data science, and IMQAV

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JVM languages

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Downloading software

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Installing software

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Introduction to Testing

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Types of tests

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Mock tests

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Code coverage

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Windows, views, and modes

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Projects

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Editor basics

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Refactoring

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Code execution

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Debugging

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Object-oriented principles

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Primitives

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Strings

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Classes and attributes

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Classes and methods

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Classes and constructors

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Exception handling

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Enumerations

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Casting

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Generics

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Annotations

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Program flow control

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Install and use libraries

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gson

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StringUtils

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Introduction to regular expressions

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Literals

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Metacharacters and representations

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Predefined character classes

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Regex quantifiers

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Regex boundaries and anchors

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Regex examples

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Introduction to reflection

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Introspect fields

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Introspect methods

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Introspect constructors

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Introspect annotations

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Decorator patterns

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Introduction to Design Patterns

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Singleton patterns

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Visitor patterns

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Introduction to magic squares

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Magic squares algorithm

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Adjacency matrix

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Magic characteristics

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Building magic cubes

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Java for Data Scientists Essential Training Next step

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