General Engineering Course Descriptions
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- Data Visualization
- Able to collect data from various sources and use visualization tools such as Python and Tableau to design and present analysis results according to the needs and objectives of the audience.
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- Introduction to Economics
- Students will be able to understand the basic concepts and principles of economics and apply major economic theories to analyze real-world economic phenomena
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- Semiconductor Material Process an d Performance
- Able to explain the structure and physical properties of semiconductor materials, and use simulation and measurement data to analyze and evaluate performance.
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- Computer Fundamentals
- Able to describe the structure and operating principles of hardware and software, and propose applications based on problem requirements.
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- Basic Programming
- Able to apply structured programming concepts to design, implement, and debug programs with functional modules.
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- AI and Data Science Introduction
- Able to explain AI concepts and machine learning principles, and implement basic data analysis and prediction models.
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- Statistics for Engineering
- Able to analyze engineering data using statistical methods and provide decision-making insights based on the analysis results.
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- General Physics
- Able to explain physical principles and apply them to problem-solving in engineering contexts.
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- Data Engineering
- Able to design and implement data pipelines, and optimize data flows using the latest data processing technologies
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- Python Programming
- Able to use Python to design, implement, and verify automated data processing and analysis workflows.
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- Semiconductor Basics
- Able to explain the electrical characteristics of semiconductors and analyze principles of various device operations.
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- Materials Engineering
- Able to analyze material structures and properties, and propose optimal manufacturing methods for specific purposes.
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- Data Structures and Algorithms
- Able to implement and apply optimal data structures and algorithms to improve problem-solving efficiency.
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- Database Introduction
- Able to design and implement relational databases, ensuring data integrity and security
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- Semiconductor Physics and Devices
- Able to analyze the structure, performance, and characteristics of semiconductor devices.
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- Engineering Mathematics
- Able to model and solve engineering problems using advanced mathematical concepts
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- Probability and Applications
- Able to apply probabilistic models to design and validate hypotheses in engineering contexts.
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- Web Application Development
- Able to develop interactive websites using HTML, CSS, and JavaScript
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- Semiconductor Manufacturing
- Able to design manufacturing processes for semiconductors and propose optimization strategies.
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- Machine Learning Introduction
- Able to implement machine learning models and evaluate their performance using data
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- Spatial Data Analysis
- Able to analyze and visualize spatial data using GIS tools
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- Digital Signal Processing
- Able to design and implement DSP algorithms for signal processing applications.
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- Microsystem and Nanomechanical Properties
- Able to apply micro/nano fabrication techniques to analyze mechanical properties at small scales
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- Big Data Analysis
- Able to use big data frameworks to store, process, and analyze large-scale datasets.
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- Deep Learning Introduction
- Able to implement and train deep learning models.
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- Technological Entrepreneurship
- Able to develop business models and commercialization strategies for technology-based innovations.
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- System Design and AI
- Able to integrate AI algorithms into system design projects
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- Computer Networks
- Able to design, configure, and troubleshoot network systems using OSI and TCP/IP protocols.
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- Embedded Systems Design
- Able to design embedded systems considering hardware constraints and performance requirements.