Software for Enhancing Data Science Reliability and Quality
Data scientist in Basel
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Data science tool

Reproducible data science is extremely important for robust and efficient results



Part of Rewoso‘s data insights projects



Realized in R-Statistics

Software for Data Processing, Data Analyses, and Data Quality in Real-world Healthcare

A software solution that combines frequently-used data processing tools in health-care analytics with a user-friendly data-quality testing pipeline

I’ve developed a statistical package in R-statistics that combines the most frequently used data preprocessing steps for longitudinal real-world health data, data visualization tools, and a user-friendly data-quality testing framework. Written in R-statistics and available to the company as R-package, the software ensures reproducible results, and efficient data preprocessing. It allows flexible data visualizations using the corporate identity of the company. Importantly, it also includes a data-analytics-quality toolbox, allowing data scientists to test expectations about their data during the data processing, generating a data quality report, which saves code review time. The software uses common software-develolpment principles such as unit tests and version control.

The code and project are for internal use only.