This course is aiming to introduce the tools used for analytics. It will mostly focus on telling the theorem of common machine learning models with application in real cases. The language being displayed in the course is R but the same idea applied in other languages such as Python and Java.
This course prepares students to gather, describe, and analyze CDI data. Data visualization is an essential skill required in today's data-driven world. Practitioners in almost every field use visualization to explore and present data. In this class, we will also introduce and practice visualization analysis by using R studio.
This popular course covers the basic knowledge of big data and AI, including its history and future trends. With successful use cases and application examples, it also discusses AI's impacts on our life and work. Then, a framework and some tools will be introduced to assess AI maturity, with a roadmap to follow for learners to improve competency and employability in the future.
Students will explore a multitude of ethical dimensions to artificial intelligence and complete a capstone project for publication through RMDS. The course is split into three key components:
An exploration of the ethics of AI development
A section dedicated entirely to learning about bias in programming and AI
A critical examination of key social and legal questions to AI.
Analytical workflows, based on the concepts of RM4E and ResearchMap, increase accessibility and reproducibility of analytics and machine learning projects. This course covers the concept and application of analytical workflows as well as walking through the implementation.
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