Java for Data Scientists Essential Training

2h 44mIntermediate2017-02-01

Authors

Charles Kelly

Chief Technology Officer at SAGE

Course details

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.

Learning objectives
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

Skills covered

JavaData EngineeringOracleData AnalysisProgramming LanguagesData ScienceBusiness Analysis and StrategyBusiness Software and ToolsSoftware Development

Concepts

0. Introduction

01 - Welcome
02 - What you should know
03 - Using the exercise files

1. Getting Started with Java

04 - Java, data science, and IMQAV
05 - JVM languages
06 - Downloading software
07 - Installing software

2. Test-Driven Development

08 - Introduction to testing
09 - Types of tests
10 - Mock tests
11 - Code coverage

3. IntelliJ IDEA

12 - Windows, views, and modes
13 - Projects
14 - Editor basics
15 - Refactoring
16 - Code execution
17 - Debugging

4. Object-Oriented Java

18 - Object-oriented principles
19 - Primitives
20 - Strings
21 - Classes and attributes
22 - Classes and methods
23 - Classes and constructors
24 - Exception handling
25 - Enumerations
26 - Casting
27 - Generics
28 - Annotations
29 - Program flow control

5. Libraries

30 - Install and use libraries
31 - gson
32 - StringUtils

6. Regular Expressions (Regex)

33 - Introduction to regular expressions
34 - Literals
35 - Metacharacters and representations
36 - Predefined character classes
37 - Regex quantifiers
38 - Regex boundaries and anchors
39 - Regex examples

7. Reflection

40 - Introduction to reflection
41 - Introspect fields
42 - Introspect methods
43 - Introspect constructors
44 - Introspect annotations

8. Design Patterns

45 - Introduction to design patterns
46 - Singleton patterns
47 - Decorator patterns
48 - Visitor patterns

9. Applying Data Science

49 - Introduction to magic squares
50 - Magic squares algorithm
51 - Adjacency matrix
52 - Magic characteristics
53 - Building magic cubes

Conclusion

54 - Next steps