Tools for AI and Data Science (SZF_171)
About Study Course
Objective
The course aims to provide students with the knowledge and practical skills to use the latest artificial intelligence (AI) and data science technologies and related tools in a business or organizational context.
By learning the selection and application of technologies and tools such as cognitive services, generative artificial intelligence platforms, edge computing and cloud deployment techniques for machine learning models, and DevOps, students will learn to organize an appropriate development environment, enable innovation, and improve operational efficiency, achieving the goals set by the organization in areas related to the application of AI and data science.
By combining theory and practice, the course will prepare future leaders who will know what tools and working methods to use to exploit the opportunities of technology.
Prerequisites
Basic knowledge of Python programming, fundamentals of data science and machine learning, students are familiar with data visualization methods. Previous experience with cloud platforms (e.g. AWS, Azure) is desirable, but not mandatory. This knowledge will help students master the artificial intelligence tools and methods covered in the course.
Learning outcomes
1.Students will be able to distinguish, define, describe, and explain the importance and application of AI and data science technologies and related tools in the process of creating AI and data science solutions.
1.Students will be able to configure and start using tools related to AI and data science technology to use them in the development of new AI and data science solutions. Students will be able to choose appropriate tools based on information about the work goal to be achieved, the type of data and the risks.
1.Students will be able to analyze, evaluate, and present the alternatives, options, and applications of tools needed to create AI and data science solutions in an organizational context. Students will be able to analyze and explain the use of different tools within the development of a single AI or data science project, depending on the project workflow or phase.
