While on the labour market there is a high demand for employees educated and skilled in the field of natural sciences, engineering and information technologies, a lack of interest in these fields of expertise can be i...
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ISBN:
(纸本)9783030681982;9783030681975
While on the labour market there is a high demand for employees educated and skilled in the field of natural sciences, engineering and information technologies, a lack of interest in these fields of expertise can be identified in young people. The aim of our research is to leverage a scientifically founded and empirically validated approach and contribution towards promoting interest and motivation in dealing with datascience and scientific and technical subjects. To this end a novel pedagogical approach and a related learning environment has been developed and used in a first pilot phase. This approach was implemented using research and exploration questions on weather forecasts and data from temperature measurements as an example pedagogical scenario. The pilot study was executed in real-world settings of three primary and two secondary schools. The results demonstrate high interest and motivation in the science project and related datascience tool.
While machine learning algorithms continue to improve, their success often relies upon the data scientists' ability to detect patterns, determine useful features and visualizations, select good models, and evaluat...
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ISBN:
(纸本)9781450380959
While machine learning algorithms continue to improve, their success often relies upon the data scientists' ability to detect patterns, determine useful features and visualizations, select good models, and evaluate and iterate upon results. data scientists often spend a long time making very little progress as they struggle to determine how to proceed. In this respect, the understanding of data scientists' workflows and challenges has recently attracted a great deal of scholarly interest. However, the literature is mostly based on interviews and qualitative research methodologies. With this in mind, we developed DSWorkFlow, a data collection framework that provides researchers with the ability to observe and analyze data scientists' cognitive workflows as they develop predictive models. Using DSWorkFlow, researchers can collect data from a Jupyter Notebook, to reconstruct the code execution order and extract relevant information about data scientist workflow alongside the concomitant collection of qualitative data. We tested the framework experimentally with seven data scientists as they each created three machine learning models to inform our extraction algorithms.
As the importance of datascience is increasing, the number of projects involving datascience and machine learning is rising either in quantity or in complexity. It is essential to employ a methodology that may contr...
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ISBN:
(纸本)9789895465903
As the importance of datascience is increasing, the number of projects involving datascience and machine learning is rising either in quantity or in complexity. It is essential to employ a methodology that may contribute to the improvement of the outputs. In this context, it is crucial to identify possible approaches. And an overview of the evolution of data mining process models and methodologies is given for context. And the analysis showed that the methodologies covered were not complete. So, a new approach is proposed to tackle this problem. POST-DS (process Organization and Scheduling electing Tools for datascience) is a process-oriented methodology to assist the management of datascience projects. This approach is not supported only in the process but also in the organization scheduling and tool selection.
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