Introduction to Data Analytics and Artificial Intelligence (3 cr)

Materials

Course material page (lecture materials, exercises)

Evaluation scale

0-5

Further information

The assessment of the course consists of returned exercises.

Student workload

Distance learning 61h (exercises)
Guidance sessions in class 20h
A total of 81 hours

Assessment criteria, satisfactory (1)

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.

Assessment criteria, good (3)

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.

Teaching language

en

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)

Location and time

Weekly guidance for doing exercises in classrooom

Number of ECTS credits allocated

3

Qualifications

Ohjelmoinnin perusteet

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

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