•   Data Analytics RDI2HM102-3028 20.01.2025-16.05.2025  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. This course unit is part of the master's degree's curriculum. Completion of the course requires master's study entitlement.
    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
    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.

    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.
    Teaching methods and instruction

    Note: the course subscriber (degree program) has required that this course be implemented in the Python coding language. Therefore, no other data analytics application programs are used in the course, only the Python coding language.
    The course is divided into four parts, each of which focuses on analyzing structured data from different perspectives. Each part includes coding tasks and an exam. The parts are as follows.
    - descriptive analytics
    - diagnostic analytics
    - time series analysis and forecasting
    - introduction to machine learning.

    All necessary material can be found on Moodle, including a Q@A section where students can raise questions related to the course.

    Responsible person

    Veijo Vänttinen

    Learning material and recommended literature

    Material given in Moodle

    Campus

    Pasila Campus

    Exam dates and re-exam possibilities

    Three exams and subject specific coding assignments issued throughout the course

    Teaching language

    English

    Timing

    20.01.2025 - 16.05.2025

    Learning assignments

    Four assignments: - descriptive analytics - diagnostic analytics - time series analysis and forecasting - machine learning.

    Enrollment

    02.01.2025 - 17.01.2025

    Groups
    • MAICTE
    • MALEAE
    • MASALE
    • MAEXPE
    • EXCH
    • MAAVBUM
    • MASTROME
    • MASBUTUMe1
    • INSTRUCTED
    • VIRTUAL
    • MADIGE
    • MASTRE
    • MACOME
    Teachers

    Veijo Vänttinen

    Seats

    61 - 100

    Further information

    Four assignments on scale accepted/rejected.
    Three exams:
    1) descriptive and diagnostic
    2) time series and forecasting
    3) machine learning

    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, BUTUM Degree Programme in Business Technologies and Management

    R&D proportion

    0.00 cr

    Virtual proportion

    5.00 cr

    Evaluation scale

    H-5