Tag Archives: extracting data from applications

Dataset Source Documentation: Necessary for Data Science Projects with Multiple Data Sources

If you work on a big data project with multiple source datasets, you run the risk of forgetting exactly how you blended them together.

Dataset source documentation is good to keep when you are doing an analysis with data from multiple datasets. Read my blog to learn how easy it is to throw together some quick dataset source documentation in PowerPoint so that you don’t forget what you did.

Understanding Legacy Data in a Relational World

Data systems started being in use in the 1960s and 1970s, but these were flat systems, usually using IBM mainframes.

Understanding legacy data is necessary if you want to analyze datasets that are extracted from old systems. This knowledge is still relevant, as we still use these old systems today, as I discuss in my blog post.

Front-end Decisions Impact Back-end Data (and Your Data Science Experience!)

How the front-end and back-end are connected can impact how data are stored in the application. So if you extract the data, you can have data quality problems caused by the front-end.

Front-end decisions are made when applications are designed. They are even made when you design a survey in SurveyMonkey. What health data analysts often don’t realize is that these decisions have a profound impact on the quality and accuracy of the data that are collected through these front-ends, which is the focus of this blog […]

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