Data Sources

Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python. In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.

In this workshop, we cover how to extract data from the web with APIs using Python. APIs are often official services offered by companies and other entities, which allow you to directly query their servers in order to retrieve their data. Platforms like The New York Times, Twitter and Reddit offer APIs to retrieve data.

Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python. In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.

There is enormous interest in building a better understanding of how evidence and data can inform policy. New possibilities have opened up to enable data to be shared and used across states and agencies. One is a technical approach – the Administrative Data Research Facility – which provides a secure environment within which education, training, and workforce data can be shared across agencies and states. The other is human – the Applied Data Analytics training program – which trains government agency staff how to combine and use the data to serve their agency missions. Over 650 participants from over 150 agencies have participated and produced new products and new networks in the process. This presentation discusses the approach sponsored by the California Department of Social Services, joint with the Department of Education and the Economic Development Department. The D-Lab worked with the Coleridge Initiative to successfully combine the two approaches. The presentation will also address the broader vision of how approaches like this can serve to democratize data for the United States.