• Data Analysis and Visualization (TTC8040-3008),
         20.03.2023 – 28.04.2023,  4 cr  (ZJA23KTIDA1) — Face-to-face +-
    Learning outcomes of the course
    The objective of the course:
    For the development of modern applications and for their functionality, a vital part is played by the data analysis concerning the data. Applications use data that is to be presented to the end users. For the end user, data as such is not in a presentable format. Hence, analysis methods are needed to support the end user who makes the decisions based on the information content.

    Course competences:
    -EA-KW (Knowledge and understanding): You know how to use data analytics methods. You can illustrate the results of the data you analysed.
    -EA-ER (Engineering practice): You know how to implement data analysis methods in your own area of specialization.
    -EA-IG (Investigations and information retrieval): You can interpret data using data analytics methods.
    Prerequisites and co-requisites
    Basics in computing, programming, knowledge and know-how of Python programming language.
    Course contents
    - Datan määrä ja laatu
    - Datan analysointi osana koko tiedonkäsittelyprosessia
    - Datan kuvaaminen
    - Datan muokkaaminen
    - Datan visualisointi
    - Statistiikka
    - Aikasarjat
    - Korrelaatio
    - Lineaarinen ja epälineaarinen regressiomalli
    - Jaksollisen datan mallintaminen
    - Analysoidun tuloksen esittäminen (not translated)
    Assessment criteria
    Assessment criteria - grade 1 and 2
    Sufficient (1):
    You can identify data based on content and metadata. You know the most common techniques of data analytics used in data analysis tasks. You can apply the most common techniques for your data analysis. You can assess the results of data analysis in a restricted manner. You can present results of the data analyzed by you.

    Satisfactory (2):
    You are able to take data into use based on its content and metadata. You are familiar with the most commonly used techniques of data analytics in data analysis tasks. You are able to select the techniques for data analysis and apply the technical competence in practice. You are able to assess the data analysis result superficially. You are able to present the essential results of the data analyzed by you.
    Assessment criteria - grade 3 and 4
    Good (3):
    You can take essential data into used based on its content and metadata. You know the most commonly used techniques of data analytics in various data analysis tasks. You can justify and select the techniques for data analysis and apply your technical competence in practice. Additionally, you are able to assess the implementation and justify its development. You are able to present the essential results of the data analyzed by you.

    Very good (4):
    You are able to take the essential data into use based on its content and metadata. You know the most commonly used techniques in data analytics and are able to justify comprehensively the use of the implemented techniques in various data analysis tasks. You are able to justify and select the correct techniques for data analysis. You are able to apply your technical competence to practice. You are able to assess the implementation comprehensively and validate its further development. You are able to present the data analyzed by you extensively.
    Assessment criteria - grade 5
    Excellent (5):
    You are able to take the essential data take into use based on its content and metadata taking into account the substance. You know the most commonly used techniques in data analytics and are able to critically justify the use of the implemented techniques in various data analysis tasks. You are able to apply your technical competence to practice. You are able to assess the implementation critically and validate its further development. You are able to present the results of the data analyzed by you comprehensively.
    Language of instruction

    Finnish

    Location and time

    Opintojakso toteutetaan kevätlukukaudella 2023. (not translated)

    Planned learning activities, teaching methods and guidance

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

    Learning materials and recommended literature

    Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Opintojaksolla hyödynnetään Python 3.9+-ympäristöä, Git-versiohallintaa, NumPy- ja Pandas-kirjastoja, visualisointikirjastoja sekä muita soveltuvia kirjastoja.

    Harjoitustehtävien tekemisessä hyödynnetään myös Anaconda-ympäristöä sekä Jupyter Notebook -formaattia. (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 annettujen aikataulujen puitteissa. (not translated)

    Timing

    20.03.2023 - 28.04.2023

    Learning assignments and student workload

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

    Groups
    • ZJA23KTIDA1
    Alternative learning methods

    Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä. (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