This talk will cover a basic scenario of curating open data into visualizations for an audience. The main goal is to automate data scraping/downloading and reshaping. I use python to automate data gathering, and Tableau and D3 as visualization tools -- but the process can be applied to numerous analytical/visualization suites.

I'll discuss situations where a dashboard makes sense (and when one doesn't). I will make a case also that automation makes for a more seamless data gathering and updating process, but not always for smarter data analysis.

Some python packages I'll cover for web scraping and downloading/reshaping open data include: openpyxl, pandas, xlsxwriter, and BeautifulSoup. I'll also touch on APIs.

The case study for this talk, and the source of the example data visualizations, will be the Economic Intelligence Dashboard (http://open.dc.gov/economic-intelligence/). This public dashboard is a curated collection of open data from disparate sources which, taken together, form a narrative of the economic health of DC. As my main field of focus is on economic data, throughout the talk I will touch on the best (most authoritative; easiest to scrape) sources of economic data and data on the District and the region.