• Datan esikäsittely (TTC8030-3007),
         13.02.2023 – 26.03.2023,  4 cr  (ZJA23KTIDA1) — Face-to-face +-
    Learning outcomes of the course
    The student understands the process of data analytics and its challenges. The student recognizes various data formats, the most common interface solutions and tools and methods used in data processing. The student is able to apply methods needed in data preprocessing.

    EU-KN EUR-ACE Knowledge and Understanding
    EE: EUR-ACE Engineering Design
    EA-ER: EUR-ACE Engineering Practice
    Prerequisites and co-requisites
    Basic skills of computer use, basic skills in programming, knowledge of Python programming language.
    Course contents
    o Various IoT sources/formats, JSON, APIt, retrieval of tables from SQL
    o Types of variables
    o Data preprocessing before bringing it into analysis program
    o Data preprocessing in Pandas (basics of Pandas/DataFrames)
    o Connecting various data sources
    o Data encoding
    Assessment criteria
    Assessment criteria - grade 1 and 2
    Satisfactory 2: The student masters data retrieval from a certain source.The student is able to implement data preprocessing to datasets. The student is able to apply simple methods used in data preprocessing to his/her data. The student can assess his/her own solutions for data preprocessing.

    Sufficient 1: The student knows and understands the significance of data and its advantages. The student knows the significance of data preprocessing and its most common methods. The student is able to apply simple methods obtained and used in data preprocessing.
    Assessment criteria - grade 3 and 4
    Very good 4: The student masters the retrieval of data from various sources. The student is able to plan and implement data preprocessing for various datasets. The student is able to apply the methods used in data preprocessing widely. The student is able to assess and justify his/her own solutions for data preprocessing.

    Good 3: The student masters data retrieval from several sources. The student is able to plan and implement data preprocessign for datasets. The student is able to apply methods used in data preprocessing. The student is able to assess and justify his/her own solutions for data preprocessing.
    Assessment criteria - grade 5
    Excellent 5: The student masters the retrieval of data from various sources. The student is able to plan and implement data processing for various datasets. The student is able to apply the methods used in data preprocessing. The student is able to critically assess and justify his/her own solutions for data preprocessing.
    Language of instruction

    Finnish

    Location and time

    Opintojakso toteutetaan verkkototeutuksena (ei kontaktiopetusta). Opiskelija voi edetä toteutuksella omaan tahtiin. (not translated)

    Planned learning activities, teaching methods and guidance

    Opintojakso sisältää harjoituksia eri aihealueilta sekä opintojakson aihepiirejä yhdistävän harjoitustyön. (not translated)

    Learning materials and recommended literature

    Opintojakson verkkosivut (luentomateriaali, harjoitukset, harjoitustyöohjeistus). (not translated)

    Lecturer(s)

    Antti Häkkinen

    Timing

    13.02.2023 - 26.03.2023

    Learning assignments and student workload

    Harjoitukset 80 h ja harjoitustyö 28 h. Yhteensä 108 h (not translated)

    Groups
    • ZJA23KTIDA1
    Degree Programme

    Bachelor's Degree Programme in Information and Communications Technology

    Mode of delivery

    Face-to-face

    Credits
    • 4 cr
    Unit

    School of Technology