Course title | Java for science | ||||||||
Assesment method | Project + Exam | Hours per semester | 60 | Lect. | Exercises | Lab. | Project | ||
ETCS | 4 | Hours/week | 2 | 2 | |||||
Prerequisites | |||||||||
Intermediate knowledge of Java (at least one university course completed), basic knowledge of algorithms. | |||||||||
Course description | |||||||||
The first part of the course reinforces knowledge of Java. It is assumed that a student is already familiar with the Java programming language, at least on intermediate level, so that material can be presented in the form of a discussion. Topics such as the outcomes of executions of different Java programs, possible errors, misinterpretations and pitfalls of the language will all be addressed. This course prepares students for international examinations of Java expertise. The second part of the lecture demonstrates the use of Java in various scientific areas. Topics are taught in the form of step by step solutions to specific problems. Then the user is introduced to numerous possibilities of applying Java and is shown where to find further information. At the end of each class examples of issues are presented as an inspiration for the students to develop their own applications. |
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Course objectives | |||||||||
Better knowledge of Java as a tool in business and scientific projects. | |||||||||
Grading | |||||||||
Project (60%) + exam (40%) or exam (100%). | |||||||||
Reference Texts and Software | |||||||||
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Lecture Schedule | |||||||||
1. |
Basic structures and operations
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2. |
Threads
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3. |
Regular expressions
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4. |
Working with methods, encapsulation and inheritance
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5. |
Debugging and optimization
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6. |
Handling exceptions
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7. |
XML
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8. |
Developer's tools
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9. |
Language improvements
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10. |
Gathering web data for data mining
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11. |
Introduction to Data Science
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12. |
Introduction to Map-Reduce processing
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13. |
Distributed File Systems
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14. |
Distributed Databases
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15. |
Machine Learning in Hadoop environment
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