Introduction to Big Data and AI Technologies (5 cr)
Code: MS00CS30-3001
General information
Enrollment
02.12.2023 - 30.01.2024
Timing
01.01.2024 - 31.07.2024
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Teaching languages
- English
Degree programmes
- Master of Engineering, Software Engineering and ICT
Teachers
- Reetta Raitoharju
Groups
-
VAVY2324
- 20.03.2024 08:00 - 12:00, Introduction to Big Data and AI
- 27.03.2024 08:00 - 12:00, Introduction to Big Data and AI
- 10.04.2024 08:00 - 12:00, Introduction to Big Data and AI
- 08.05.2024 12:00 - 16:00, Introduction to Big Data and AI Technologies MS00CS30-3001
Objective
After completion of the course, students will be able to:
• Evaluate the use and limitations of Big Data, AI, and their enabling technologies
• Complete basic data mining tasks with current tools, depending on discipline
• Use generative text models such as ChatGPT for academic and workplace tasks
• Communicate effectively with data science and AI experts
• Use appropriate visualizations to present data analyses
Content
Course objectives:
• Recognize the evolution of tools and techniques fundamental to the current state of data science and artificial intelligence
• Understand the drivers, capabilities, and benefits of AI
• Exposure to current data mining tools
• Understand and use different types of visualization techniques
• Survey machine-learning techniques for predictive analytics
• Understand the fundamentals of neural networks and “deep learning”
Materials
Learning Materials:
To be announced later.
Teaching methods
Pedagogic Approach:
• Readings of texts and online discussions
• Written reflections of case studies
• Working through structured projects in data analysis and visualization
• As appropriate, discussing analytics in the current workplace
Content scheduling
This course covers the fundamental theories, concepts, and tools to understand the emerging role of Data Science and Artificial Intelligence in what is broadly called Business Analytics. The course will cover both the conceptual foundations as well as the commercial tools and techniques available for analytics. Depending on the background of the class, the course will work on example projects using spreadsheets or scripting languages such as Python or R for data analysis and manipulation: key topics are how to import, clean, manipulate, analyze, and visualize data. Additionally, current issues surrounding Large Language Generative Models such as ChatGPT will be discussed in regard to a variety of workplace applications. The course relies on the analysis of case studies and the completion of example projects.
Contact teaching (online or in campus)
20.3. 8-12
27.3. 8-12
10.4. 8-12
Online tutoring
4.4. 16-18
25.4. 16-18
Evaluation scale
H-5