Data Analytics and Artificial Intelligence in Health Care (5cr)

Objectives (course unit)

The student
- knows the most common terminology and concepts of data analytics
- knows the principles of data mining, storing and analysis mehtods
- knows the most common data management and visualisation methods
- understands the importance and use data in health care process
- knows the concepts, principles and use of machine learning in health care

Content (course unit)

Key concepts: definition of healthcare data, Big Data, data visualization, algorithms, machine learning, artificial intelligence
Categories of health care data
Introduction to Big Data and its utilization in healthcare
Data recovery methods
The most common Big Data systems
Introduction to algorithms, basics of machine learning and artificial intelligence

Assessment criteria, satisfactory (1-2) (course unit)

The student
- is able to process data
- is able to analyze and make data visualizations
- knows the basics and main concepts of artificial intelligence as well as the main applications in the field of healthcare.

Assessment criteria, good (3-4) (course unit)

The student
- is able to process data
- is able to analyze and make data visualizations
- knows the basics and main concepts of artificial intelligence
- is able based on examples to create artificial intelligence applications in the field of healthcare
- understands the importance of data in health care management processes.

Assessment criteria, excellent (5) (course unit)

The student
- is able to process data
- is able to analyze and make data visualizations
- knows well the basics of artificial intelligence and the most important concepts
- is able to create appropriate artificial intelligence applications in healthcare
- understands the importance of data in health care management processes.

Exam schedules

Ei tenttiä.

Assessment methods and criteria

Tarkemmat arviointiperusteet julkaistaan kurssin Moodle -sivuilla kurssin osatoteutuksittain aloituskerralla

Assessment scale

0-5

Teaching methods

Teams -opetus, lähiopetus, Moodle -alustalla tehtävät etätehtävät, loppututkielman teko.

Learning materials

Julkaistaan kurssin Moodle -sivuilla tai kurssin Teamsissa ennen kurssin alkua.

Content scheduling

Data-analytiikka terveydenhuollossa 2,5op osuus
Tekoäly terveydenhuollossa 2,5 op osuus

Further information

Kurssi jaetaan kahteen 2,5 opintopisteen osatoteutukseen, joista toinen on tekoäly ja toinen data-analytiikka. Kurssin kokonaisarvio muodostuu näiden osatoteutusten keskiarvon perusteella lähimpään kokonaislukuun pyöristettynä.