Related to data mining/data science, I recently discovered Orange (https://orange.biolab.si/) a high level very powerful free toolkit. Using it is very simple and you can test different models in order to evaluate the best results. However, when writing paper to a Journal, is it plausible to use it instead of R Studio (for instance) or other tools (PyTorch, Weka, TensorFlow)? Or Orange is best indicated just for teaching?
As @Soumya Snigdha Kundu said, it depends on your application. For problems like managing energy generating plants, which is your area of study, you may need to investigate how Orange handles scaling. Depending on the type of control and the variables of your model, you may need something capable of integrating several heterogeneous subsystems.
I have seen people in your area using WEKA.
Anyway, deploying something in Orange will be good to help other folks, since there is not too much literature.
It depends on your usage. Such software's are generally utilized for testing but when it comes to research the main focus is creating a "new" method or approach which wont be available in the software.
Personally, I feel its better to code in the problem rather than using external tools for teaching because the basics are avoided in such systems.
As @Soumya Snigdha Kundu said, it depends on your application. For problems like managing energy generating plants, which is your area of study, you may need to investigate how Orange handles scaling. Depending on the type of control and the variables of your model, you may need something capable of integrating several heterogeneous subsystems.
I have seen people in your area using WEKA.
Anyway, deploying something in Orange will be good to help other folks, since there is not too much literature.
Dear Alessandro,
The terms Machine learning, data mining and data science are used interchangeable but there is difference between these terms. Basically all the terms revolve around dealing with the rapidly using large amount of data. Data science derives understanding from structed and unstructured data. It is used for qualitative analysis. It consist of data visualizations, data mining, language processing etc. It is a wider area of research which makes use of many algorithms and operations to derive informative insights from both structured and unstructured information. Where as data mining analyzes data sets created from structed data to find the hidden correlations and patterns. It is subset of data science used to extract data and generate prediction models. Data Mining also incorporates data cleaning, pattern prediction, statistical analysis, data conversion, machine learning, and data visualization. Both play key roles in helping organizations recognize opportunities and arrive at worthwhile decisions/conclusions so that they can take easily decisions to grow the businesses. Additionally, the knowledge needed for procedures in both of these fields also varies. Hence, the analysis of the differences in their approach, tools used and steps applied. Data science tools include Python, Apache spark, SAS, Tableau, R, Tensor flow etc. Data mining tools used as weka, rapid Miner, KNime, Oracle Data Mining, Apache Mahout, Teradata, Orange. These tools are not only used to teach/research but also used by industries to do the analysis as per requirements.
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