Grüzi mitenand und herzlich Wilkommen ins Welt der Daten! 

I am Oleg Lavrovsky, I'll be teaching you the Open Data module in August. I will respond to questions and speak to you in German when we meet, but in this video I will briefly introduce myself in my native language, and tell you a little bit about what you can expect to learn about open data in the weeks ahead.

A little bit about me: I grew up in Moscow and Calgary, met a Swiss in a student exchange, and attended university in Lausanne before starting a career in Information Technology. I became a certified IT professional, and worked in a variety of companies and institutions over the past 15 years. Today I provide engineering services, write code, and consult on a freelance basis out of the Effinger coworking space here in Bern.

The Internet and data processing is central to most of what I do. With business tools, Web applications, the Internet of Things, mobile devices, all generating incredible amounts of data, the Open Data movement helps to involve more people directly in the production, evaluation, and application of data sources through the use of open source software and open data licenses. Ideally this leads to a more open, participative and productive society, represented politically in Switzerland through the Digital Sustainability (Digitale Nachhaltigkeit) parliamentary group, as well as numerous grassroots initiatives and associations like Opendata.ch - the Swiss chapter of Open Knowledge.

Open data today has gone beyond being an instrument for distributing access and a political strategy. It has established itself as an industry, and furthermore as a foundation for next generation technologies, such as training sets for machine learning applications and reusable prediction models used in the field of Artificial Intelligence. With Internet-based open databases like blockchains and countless open data APIs becoming the foundation for more and more enablers of the information society, one may wonder what use it is to discuss the topic at all?

Making data sources to some degree usable and open is part of every IT project, however: there are few global standards that not only promote open licenses that ensure freedom from commercial and technical restrictions, and crucially also enable the public to easily make effective insights from a combination of sources. In the field of data analysis we are especially interested in the context through which data is produced, the specifics of the schema and linked or associated data and metadata, in keeping up with updates and tracking changes over time, and creating containers and tools to make working with data much more effective.

The API of the CKAN portal that provides the backend to opendata.swiss and other open data directories you will see in this course, the Frictionless Data standards that are behind next-generation technologies being worked on at Open Knowledge -- are exemplary standards of this type. You will encounter and be supported by them whenever you try to download data online that is, according to the Open Definition "subject, at most, to measures that preserve provenance and openness". 

Often you will find that such data is better supported by a community of publishers and users who have found an alternative to the Data Sharing and content licensing agreements of the past. In open data communities, people work together to enhance and ensure the lasting value of data sources on the basis of various models of cooperation. However, we will not focus here on the business models of Open Data, but rather on the advantages to you in the field of Data Analysis. 

The course will consist of lectures and workshops on data analysis, and you will learn about loading data, understanding its structure, running experiment and making claims and visualizations based on various types of data. We will be primarily using R and R Studio, both of which are open source projects and first class consumers of open data sources. Being the most popular language for data analysis, R code is a great way to document and communicate components of a data analysis to other people, which is one of the essential mechanisms through which open data leads to positive feedback loops between data users and maintainers.
 
After you have learned how to frame data analysis questions, and have gone over the process of working with data in R, exploring it with statistics and visualization of several examples of open data, we will have a couple of focus sessions together to go over what makes "good" open data lead to effective data analysis, discussing real world experiences and best practices. We will lift the curtain and take you behind the scenes of crowdsourcing projects and open government data publication efforts, talk about some of the legal issues to be aware of in using data, and touch on more advanced concepts like Linked Open Data and Data Packages.

As the only way to become a good data analyst is to a gain experience through practice, repetition and learning from mistakes, I hope this class will give you a good start down the road and inspire you to not only learn about the data you care about, but also what it takes to be a good publisher and promote a healthier ecosystem in our data-driven world. 

By the way, if you would like to get an earlier glimpse into Open Data, check out Opendata.ch for the latest news and announcements, and feel free to join public events, like the annual conference and community gathering in Sankt Gallen on July 3.

Until then, thanks for tuning in, see you on the message boards and on campus soon! En guete :)