•   Data Analytics and Artificial Intelligence in Health Care 7Y00FJ97-3001 01.08.2021-13.12.2021  5 cr  (21YHS, ...) +-
    Learning outcomes of the 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
    Course contents
    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

    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.

    Good

    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.

    Excellent

    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.

    Name of lecturer(s)

    Tony Torp

    Recommended or required reading

    Julkaistaan kurssin Moodle -sivuilla tai kurssin Teamsissa ennen kurssin alkua. (not translated)

    Planned learning activities and teaching methods

    Teams -opetus, lähiopetus, Moodle -alustalla tehtävät etätehtävät, loppututkielman teko. (not translated)

    Assessment methods and criteria

    Tarkemmat arviointiperusteet julkaistaan kurssin Moodle -sivuilla kurssin osatoteutuksittain aloituskerralla. (not translated)

    Language of instruction

    Finnish

    Timing

    01.08.2021 - 13.12.2021

    Registration

    28.05.2021 - 31.07.2021

    Credits

    5 cr

    Group(s)

    21YHS

    21YHT

    21YHL

    Seats

    0 - 40

    Teacher(s)

    Ossi Nykänen, Lea Saarni, Tony Torp

    Further information for students

    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. (not translated)

    Unit, in charge

    MD in Wellbeing Technology

    Degree programme(s)

    Master's Degree Programme in Well-Being Technology

    Office

    TAMK Main Campus

    Evaluation scale

    0-5

    Exam schedule

    Ei tenttiä. (not translated)

    Content periodicity

    Data-analytiikka terveydenhuollossa 2,5op osuus
    Tekoäly terveydenhuollossa 2,5 op osuus (not translated)