An Application Driven Big Data/Machine Learning Education program for Engineering Professionals – Methods and Examples

John Aa. Sørensen*, Jacob Nordfalk

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    Complexity is the new normal. Thus the constantly evolving tools in the open-source R/Python programming domains are of outmost importance for the applications of big data/machine learning tools. This implies that an engineering professional, who was not originally exposed to these subjects, is complexity challenged during continuing education, as there is a need to apply still more advanced data analysis/machine learning methods for information acquisition, aggregation and finally decision support, within many different engineering disciplines based on an increasing amount of applied statistics, linear algebra and optimization. This paper presents an application driven education program addressing this challenge. Initially the paper presents the main challenges to the continuing education. They are divided into the two areas: the technology drivers and the prerequisites of the very diversified population of todays engineering professionals. This is followed by a proposed Four Phase education program with
    R/Python script snapshots presenting machine learning model examples and examples on application driven company relevant projects. Finally the paper presents suggestions for possible future road-maps for continued education. This paper is mainly targeted those who are in an initial process of establishing a program for continued education of non specialists within the community of engineering professionals. It proposes a selection of very practical tools and structures, which aim at supporting a fast hands-on introduction to two
    integrated development environments (IDE’s) for the programming languages R and Python and their practical applications.
    Original languageEnglish
    Title of host publicationProc. of the 2nd International Conference on Electrical, Communication and Computer Engineering (ICECCE)
    Number of pages5
    PublisherIEEE
    Publication date2020
    ISBN (Electronic)978-1-7281-7116-6
    Publication statusPublished - 2020
    Event2nd International Conference on Electrical, Communication and Computer Engineering - Istanbul, Turkey
    Duration: 14 Apr 202015 Apr 2020

    Conference

    Conference2nd International Conference on Electrical, Communication and Computer Engineering
    Country/TerritoryTurkey
    CityIstanbul
    Period14/04/202015/04/2020

    Keywords

    • Big-data
    • Machine-learning
    • Open-source
    • R/python
    • Industrial applications
    • Life long learning

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