Data Science is the interdisciplinary process that includes raw data collection, data preparation, exploratory analysis, definition and development of machine-learning and statistical inference algorithms, data visualization, and communication of findings (Schutt and O’Neil, 2013). With the increasing number of available data sets, Data Science has influenced a broad range of fields including economics, science and society. Organisations use Data Science methods for decision-making, data-driven journalism is becoming popular in mass media, and new empirical scientific disciplines have emerged thanks to the application of Data Science methods.
Switzerland’s extensive investment in Open Data Initiatives has led to a rich landscape with thousands of open data sets in domains ranging from urban statistical data to mobility planning and cultural information. Anyone willing to develop an app or run a data analysis can browse, download and query the (linked) open data via the data portals and technical infrastructure (e.g., query endpoints and APIs) provided by public organisations such as the statistical offices of the city and the canton of Zurich, and the federal initiative led by the Swiss chapter of Open Knowledge Foundation (Open Data CH).
In order to be able to find meaning in these large amounts of data, artificial intelligence is needed to search, filter and mine the data. However, combining machine intelligence with human intelligence is crucial to guide the process given the unique skills of humans to assess relevance and quality, perform complex associations of ideas, reason, be creative, take decisions based on intuition and subscribe to ethical principles. Moreover, humans are a key resource to audit the algorithmic output of machines.
Relevant Topics for the Hackathon
The three major themes for the challenges will be:
Curating & Finding Data
Collective actions to process data and enable better data search.
Collective actions to extend particular data sets or improve the data quality.
Implementing tools that help citizens learn about available data sets.
Asking & Answering Data Questions
Extending data portals with collective intelligence features (e.g., reporting data usage, providing feedback to data providers, triggering the continuation of data analysis).
Coding data analysis to answer specific questions about the city and the canton of Zurich with available data.
Infrastructure for Data Analysis
Extending data analysis algorithms (in R & Python).
Creating tools to better report data findings.