Visual Analytics for Data Scientistsdoi:10.1007/978-3-030-56146-8_5Natalia AndrienkoGennady AndrienkoGeorg FuchsAidan SlingsbyStefan Wrobel
Applications Visual analytics has proven to be useful in many applications, such as crime analysis, network security, and business analytics. Powerful tools were developed to support data scientists with interactive visualizations. Often, web-based tools are employed to support sharing results, and...
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Software-wise, some stages of the pipeline have been designed in a decoupled plug-and-play way, so that future researchers and data scientists can extend it to their needs. 3.1. Deep Learning (DL) module In the Deep Learning (DL) module, an input time series dataset is loaded, processed...
Simple language from embedded natural language generation facilitates report interpretation and reduces the learning curve for business analysts. Share modeling insights via a PDF report. SAS plus open source Modelers and data scientists can access SAS capabilities from their preferred coding environment –...
Users of SAS Visual Analytics 7.2, 7.3 and 7.4 should register for this course, which covers the same topics as the SAS Viya 8.3 version. Strategies and Concepts for Data Scientists and Business Analysts Data scientists and statistical business analysts will learn key skills, includ...
SAS� Visual Analytics has two add-on offerings, SAS� Visual Statistics and SAS� Visual Data Mining and Machine Learning, that provide knowledge workers and data scientists an interactive interface for data partition, data exploration, feature engineering, and rapid modeling...
Data engineers, analysts, and scientists use visual ETL features to create extract, transform, and load (ETL) flows using an intuitive visual interface. With visual ETL, analytics users can discover, prepare, move, and integrate data from multiple sources. This simplifies the process of data mani...
Epiviz exposes a fully-featured JavaScript code dialog which scientists can use to define complex ways in which these transformations can be applied. 3. The extension of user workspaces to include user-defined code customizations. Reproducibility is an essential aspect of genomics data analysis ...
Beyond predicting a correct class for each data instance, data scientists also want to understand what differentiating factors in the data have contributed to the classification during the learning process. We present a visual analytics approach to facilitate this task by revealing the RNN attention ...