•   Data Analytics RDI2HM102-3025 25.03.2024-17.05.2024  5   (MAICTE, ...) +-
    Learning objectives
    Upon completion of the course, the student will:
    • Identify data sources and assess their suitability for business needs.
    • Understand the stages of data preparation, modeling, and forecasting.
    • Understand the fundamental concepts of machine learning and artificial intelligence.
    • Master methods of descriptive and explanatory analytics.
    • Be able to utilize various visualization and reporting techniques.
    • Understand the concept of information design.
    Starting level and linkage with other courses
    No prerequisites.
    Contents
    Content
    • Data analytics based on process thinking (CRISP-DM).
    • Methods for descriptive and explanatory analytics.
    • Analysis and forecasting of time series data.
    • Models for predictive analytics and machine learning.
    • Applied examples using Python.
    • Tools for visualization and reporting.
    Assessment criteria
    Assessment criteria - grade 1
    The student understands the data analytics process and can apply it, instructed by the teacher, to a business problem. The student understands the following concepts: descriptive, predictive and prescriptive analytics as well as the characteristics of advanced data and is able to solve simple business questions, guided by the teacher.

    The student is able to derive and visualize dashboards, scorecards and publish those using related digital tools. The students is able to apply one or some forecasting algorithms to a business problem, instructed by the teacher. The students is able to assess the reliability and relevance of business reports.
    Assessment criteria - grade 3
    The student understands the data analytics process and can apply it independently to a simple business problem. The student understands the following concepts: descriptive, predictive and prescriptive analytics as well as the characteristics of advanced data and is able to solve simple business questions independently.

    The students is able to derive and visualize dashboards, scorecards and publish them using related digital tools. The student is able to apply independently one or some forecasting algorithms to a business problem. The students is able to assess the reliability and relevance of business reports.
    Assessment criteria - grade 5
    The student understands the data analytics process and can apply it to a slightly complicated business problem. The student understands the following concepts: descriptive, predictive and prescriptive analytics as well as the characteristics of advanced data and is able to solve demanding business questions.

    The student is able to derive and visualize dashboards, scorecards and publish them using related digital tools. The student is able to apply independently several forecasting algorithms to different business problems. The student is able to assess the reliability and relevance of business reports.
    Further information
    When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25%, your grade will be lowered by one. If you are absent more than 50%, the course is failed.
    Teaching methods and instruction

    In the classroom or online classes, one will learn the basics of data analytics using Python coding under the guidance of a teacher. Students can also study independently with the help of ready-made examples.

    No previous coding experience is required.

    Responsible person

    Heidi Rajamäki-Partanen

    Learning material and recommended literature

    https://1u.fi/hhjuhanurmonen

    Campus

    Pasila Campus

    Teaching language

    English

    Timing

    25.03.2024 - 17.05.2024

    Learning assignments

    The implementation includes individual tasks where the skills learned are applied. The topics of the tasks include Descriptive analytics Diagnostic analytics Time series description and analysis Time series forecasting Predictive analytics and machine learning models

    Enrollment

    02.01.2024 - 15.02.2024

    Content scheduling

    Period 2, Autumn 2024

    Groups
    • MAICTE
    • MASALE
    • MADIGE
    • MASTRE
    • EXCH
    • MACOME
    • ONLINE
    Alternative learning methods

    The implementation allows one to participate in common teaching according to your own skills. The course material supports indivudual learning.

    Teachers

    Juha Nurmonen

    Seats

    31 - 60

    Further information

    Individual assignments are assessed and each contributes its own weight to the final grade. Each individual assignment must be passed according to the criteria given in the implementation during the same course implementation.

    Degree Programme

    BUTEM Degree Programme in Business Technologies, ATBUM Degree Programme in Aviation and Tourism Business, LEBUM Degree Programme in Leading Business Transformation, HOSBUM Degree Programme in Tourism and Hospitality Business

    R&D proportion

    0.00 cr

    Virtual proportion

    0.00 cr

    Evaluation scale

    H-5