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Data types are a defining feature of bigdata as unstructured data needs to be cleaned and structured before it can be used for dataanalytics. In fact, the availability of cleandata is among the top challenges facing data scientists.
Descriptive Analytics Projects: These projects focus on summarizing historical data to gain insights into past trends and patterns. Examples include generating reports, dashboards, and datavisualizations to understand business performance, customer behavior, or operational efficiency.
Data Wrangler simplifies the data preparation and feature engineering process, reducing the time it takes from weeks to minutes by providing a single visual interface for data scientists to select and cleandata, create features, and automate data preparation in ML workflows without writing any code.
Presenters and participants had the opportunity to hear about and evaluate the pros and cons of different back end technologies and data formats for different uses such as web-mapping, datavisualization, and the sharing of meta-data. These can be cleaned to remove artifacts and/or outdated elements.
Data science in healthcare allows physicians to access patients’ health data, see the change over time, and tweak the treatment plan if something goes wrong. Utilizing bigdataanalytics allows medical professionals to take advantage of historical information and get valuable insights.
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