Introduction to Data Analytics and Artificial Intelligence (3 cr)
Code: TTC2050-3026
General information
Enrollment
18.11.2024 - 09.01.2025
Timing
13.01.2025 - 31.03.2025
Number of ECTS credits allocated
3 op
Mode of delivery
Face-to-face
Unit
School of Technology
Campus
Lutakko Campus
Teaching languages
- English
Seats
0 - 35
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Antti Häkkinen
Groups
-
TIC23S1Bachelor's Degree Programme in Information and Communications Technology
- 15.01.2025 09:45 - 11:15, Introduction to Data Analytics and Artificial Intelligence TTC2050-3026
- 22.01.2025 09:45 - 11:15, Introduction to Data Analytics and Artificial Intelligence TTC2050-3026
- 29.01.2025 09:45 - 11:15, Introduction to Data Analytics and Artificial Intelligence TTC2050-3026
- 05.02.2025 09:45 - 11:15, Introduction to Data Analytics and Artificial Intelligence TTC2050-3026
- 12.02.2025 09:45 - 11:15, Introduction to Data Analytics and Artificial Intelligence TTC2050-3026
- 19.02.2025 09:45 - 11:15, Introduction to Data Analytics and Artificial Intelligence TTC2050-3026
- 05.03.2025 09:45 - 11:15, Introduction to Data Analytics and Artificial Intelligence TTC2050-3026
- 12.03.2025 09:45 - 11:15, Introduction to Data Analytics and Artificial Intelligence TTC2050-3026
- 19.03.2025 09:45 - 11:15, Introduction to Data Analytics and Artificial Intelligence TTC2050-3026
- 26.03.2025 09:45 - 11:15, Introduction to Data Analytics and Artificial Intelligence TTC2050-3026
Objective
Purpose and objectives:
The course gives you an overview of the methods of data analytics and artificial intelligence, their possibilities and applications as well as the most commonly used programming environments and libraries.
EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Content
Definitions of data analytics and artificial intelligence
Practical applications of artificial intelligence
Examples and principles of machine learning and neural networks
Data analytics programming languages and environments: Python, R, Anaconda, Pandas
Location and time
Weekly guidance for doing exercises in classrooom
Oppimateriaali ja suositeltava kirjallisuus
Course material page (lecture materials, exercises)
Teaching methods
- Weekly guidance in classroom (students can come to class to do exercises every week and get support for doing the exercises from the teacher)
- Distance learning (students can complete the course at their own pace, doing exercises independently)
Student workload
Distance learning 61h (exercises)
Guidance sessions in class 20h
A total of 81 hours
Further information
The assessment of the course consists of returned exercises.
Evaluation scale
0-5
Arviointikriteerit, tyydyttävä (1-2)
Sufficient 1: You recognize some of the most important methods, possibilities and applications of data analytics or programming environments used in them.
Satisfactory 2: You recognize some of the most important methods, possibilities and applications of data analytics and programming environments used in them.
Arviointikriteerit, hyvä (3-4)
Good 3: You recognize the most important methods, possibilities and applications of data analytics and artificial intelligence and the programming environments used in them.
Very good 4: You recognize the most important methods, possibilities and applications of data analytics and artificial intelligence and the programming environments used in them. Additionally, you understand some principles of artificial intelligence methods.
Assessment criteria, excellent (5)
Excellent 5: You recognize the most important methods, possibilities and applications of data analytics and artificial intelligence and the programming environments used in them. Additionally, you understand the most important principles of artificial intelligence methods.
Qualifications
Ohjelmoinnin perusteet