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Introduction to Data Analytics and Artificial Intelligence (3 cr)

Code: TTC2050-3022

General information


Enrollment

20.11.2023 - 04.01.2024

Timing

08.01.2024 - 30.04.2024

Number of ECTS credits allocated

3 op

Mode of delivery

Face-to-face

Unit

School of Technology

Campus

Lutakko Campus

Teaching languages

  • English

Seats

20 - 35

Degree programmes

  • Bachelor's Degree Programme in Information and Communications Technology

Teachers

  • Antti Häkkinen

Groups

  • TIC22S1
    Bachelor's Degree Programme in Information and Communications Technology
  • 11.01.2024 10:30 - 12:00, Introduction to Data Analytics and Artificial Intelligence TTC2050-3022
  • 18.01.2024 10:30 - 12:00, Introduction to Data Analytics and Artificial Intelligence TTC2050-3022
  • 25.01.2024 10:30 - 12:00, Introduction to Data Analytics and Artificial Intelligence TTC2050-3022
  • 01.02.2024 10:30 - 12:00, Introduction to Data Analytics and Artificial Intelligence TTC2050-3022
  • 08.02.2024 10:30 - 12:00, Introduction to Data Analytics and Artificial Intelligence TTC2050-3022
  • 15.02.2024 10:30 - 12:00, Introduction to Data Analytics and Artificial Intelligence TTC2050-3022
  • 22.02.2024 10:30 - 12:00, Introduction to Data Analytics and Artificial Intelligence TTC2050-3022
  • 07.03.2024 10:30 - 12:00, Introduction to Data Analytics and Artificial Intelligence TTC2050-3022
  • 14.03.2024 10:30 - 12:00, Introduction to Data Analytics and Artificial Intelligence TTC2050-3022
  • 21.03.2024 10:30 - 12:00, Introduction to Data Analytics and Artificial Intelligence TTC2050-3022
  • 28.03.2024 10:30 - 12:00, Introduction to Data Analytics and Artificial Intelligence TTC2050-3022

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

Lectures and, in addition, guidance in doing exercises in class

Oppimateriaali ja suositeltava kirjallisuus

Course material page (lecture materials, exercises)

Teaching methods

Weekly lectures (lecture material, example exercises led by a teacher)
Distance learning (exercises)
Weekly guidance sessions for doing the exercises

Student workload

Lectures 35h (lectures, exercises led by a teacher)
Distance learning 46h (exercises)
Guidance sessions in class 20h
Total 81h

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