• Data-Analysis and Machine Learning Basics (TTC8020-3005),
         05.09.2022 – 16.10.2022,  4 cr  (ZJA22STIDA1) — Face-to-face +-
    Learning outcomes of the course
    You understand the practices of data analytics and machine learning and the structure and flow of the project. You understand how a data-based project is designed, built and implemented. You will also recognize the key terminology and most common practices of data-based projects. You understand the importance of data visualization. You know the concepts of the teaching and test dataset and the most common ways of splitting them. You will get basic information about the data analytics and machine learning tools used.

    EUR-ACE Competences:
    Knowledge and Understanding
    Engineering Practice
    Course contents
    - Structure and implementation of a data-based project
    - Data analytics and machine learning practices
    - The concepts of the teaching and test data set and the most common ways of splitting them
    - Documentation and visualization of the data-based project
    - Introduction to data analytics and machine learning's most common tools and practical skills needed
    Assessment criteria
    Assessment criteria - grade 1 and 2
    Satisfactory 2: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project. Additionally, the student knows their implementation at a cursory level and is able to validate their conclusions.

    Sufficient 1: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project at a cursory level. Additionally, the student is able to assess their implementation and conclusions.
    Assessment criteria - grade 3 and 4
    Very good 4: The student knows the various phases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of data analytics and machine learning project regardless of the problem to be solved. In addition, the student is able to assess their implementation and validate the conclusions.

    Good 3: The student knows the variousphases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess their implementation in a versatile manner and to validate the conclusions.
    Assessment criteria - grade 5
    Excellent 5: The student knows the various phases of a data analytics and machine learning project and is able to systematically proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess critically their implementation and validate the conclusions.
    Language of instruction

    Finnish

    Location and time

    Opintojakso toteutetaan syyslukukaudella 2022. (not translated)

    Planned learning activities, teaching methods and guidance

    Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.
    Harjoitustehtävät tehdään pääsääntöisesti ryhmätöinä. (not translated)

    Learning materials and recommended literature

    Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. (not translated)

    Lecturer(s)

    Juha Peltomäki

    Working life cooperation

    Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin. (not translated)

    Exam dates and re-exam possibilities

    Opintojakso arvioidaan palautettujen harjoitustehtävien avulla. Palautukset tulee suorittaa annettuihin aikatauluihin mennessä. (not translated)

    Timing

    05.09.2022 - 16.10.2022

    Learning assignments and student workload

    Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op) kurssilla on 108 tuntia. (not translated)

    Groups
    • ZJA22STIDA1
    Alternative learning methods

    Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä. (not translated)

    Assessment methods

    Arviointimenetelmät käydään läpi opintojakson alussa. (not translated)

    Degree Programme

    Bachelor's Degree Programme in Information and Communications Technology

    Mode of delivery

    Face-to-face

    Credits
    • 4 cr
    Unit

    School of Technology