Data scientist vs geospatial scientist

Differences between data scientist and geospatial scientist duties and responsibilities

Data scientist example responsibilities.

• Update, maintain, and manage regional CRM database and records for customers, vendors, and suppliers.
• Configure and manage JobScope ERP system for a make-to-order/make-to-stock design and manufacturing environment.
• Lead the analysis in SAS for data integration of mortality data using meta-analysis integration methods.
• Implement a proximal stochastic gradient descent with a line search to fit a regularize logistic regression in Scala
• Perform cross-validation-test on linear regression model of data using scikit-learn.
• Develop python base statistical visualization to provide insights of fuzzy social media data.
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Geospatial scientist example responsibilities.

• Develop and implement tools in both IDL/ENVI and Erdas to automate preprocessing and enhancement of imagery data for exploitation and interpretation.
• Test multiple HSI detection algorithms against hundreds of different camouflage schemes and materials.
• Compare the performance of multiple HSI detection algorithms to detect and discriminate among different camouflage schemes and materials.
• Develop probabilistic models of disease progression to predict future healthcare utilization and capitation revenue.
• Communicate defects, encounter during regression test and followed-up with developers until all issues are resolved.
• Require to acquire geospatial information and extract essential elements from a wide array of multi-intelligence data to include Sigint and Masint.
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