Author
Released
2/1/2017- 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
Skill Level Intermediate
Duration
Views
- [Charles] This course explains how you can use java for two components of data science, data engineering and data analysis. This course is not an exhaustive treatment of every facet of java. Rather, it is a selection of valuable topics to help you in your data science career. The focus of the course is object-oriented java. A guiding principle for the course is test-driven development. Throughout the course, I'll use the free community edition of IntelliJ from JetBrains to present java examples including java classes, methods and operations, libraries, regular expressions, reflection, and design patterns.
I'll conclude the course by illustrating how you can apply the data science skills that you will learn in the course to an interesting problem, using java to create magic squares and Sudoku puzzles. I enjoy using data science in my work, designing guidance centers for robotic vehicles. I hope that you will enjoy java and data science as much as I do, let's get started.
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Introduction
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Welcome1m 3s
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Using the exercise files1m 8s
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1. Getting Started with Java
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JVM languages1m 8s
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Downloading software3m 10s
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Installing software6m 57s
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2. Test-Driven Development
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Introduction to testing2m 15s
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Types of tests5m 54s
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Mock tests2m 15s
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Code coverage1m 47s
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3. IntelliJ IDEA
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Windows, views, and modes5m 15s
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Projects5m 47s
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Editor basics5m 36s
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Refactoring2m 38s
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Code execution1m 23s
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Debugging2m 4s
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4. Object-Oriented Java
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Object-oriented principles1m 20s
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Primitives59s
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Strings4m 12s
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Classes and attributes4m 13s
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Classes and methods3m 46s
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Classes and constructors2m 44s
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Exception handling3m 27s
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Enumerations5m 26s
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Casting3m 19s
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Generics1m 40s
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Annotations1m 59s
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Program flow control9m 43s
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5. Libraries
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Install and use libraries5m 15s
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gson2m 40s
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StringUtils1m 56s
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6. Regular Expressions (Regex)
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Literals4m 23s
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Predefined character classes1m 28s
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Regex quantifiers2m 40s
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Regex boundaries and anchors1m 26s
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Regex examples1m 55s
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7. Reflection
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Introduction to reflection1m 17s
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Introspect fields2m 50s
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Introspect methods2m 51s
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Introspect constructors3m 15s
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Introspect annotations2m 5s
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8. Design Patterns
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Singleton patterns2m 19s
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Decorator patterns3m 23s
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Visitor patterns2m 32s
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9. Applying Data Science
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Magic squares algorithm5m 11s
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Adjacency matrix5m 42s
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Magic characteristics1m 35s
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Building magic cubes5m 31s
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Conclusion
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Next steps53s
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Video: Welcome