Профессия Data Scientist
Following the great success of DSAA’2014 in Shanghai, the 2015 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA’2015), to be held on 19-21 October 2015 in Paris, has seen the significant growth of the number of submissions, participates, sponsors and key stakeholders. Without any doubt, DSAA has been recognized to be the first and most influential event in the data science and analytics focused community. Data driven scientific discovery and innovation and practical development, applications and economy have been increasingly recognized as the major trend of future IT and business. Data science, big data and advanced analytics play the most important role in driving data innovation and economy. DSAA thus carries a critical role in substantially promoting and strengthening the above trends and results.
In this paper, we present our current research regarding information interaction strategies of students of minor specialization in Data Science. We employed an online platform, consisted of a third-party and our software, to provide students with means of learning and analyse their learning activity. We developed several indicators to estimate their activity: coding activity, friends network size, and Q&A activity. We show that high-achieving and low-achieving students use resources in different ways, with substantial inequality in resource access/use. Based on the research, we propose two features that supposedly would provoke students to participate in a Q&A activity decreasing inequality in the use of these resources.
We present in the form of two visualizations some preliminary results of the ongoing study of data science community in Russia. The rst visualization aggregates data about top researches and their elds of interest according to the Google Scholar service. The second graph is a map of the largest online communities on date science on VKontakte platform.
A phenomenon of citizen science, its features and prospects are the topic of high actuality nowadays. And it seems to be natural, that citizen science and crowdsourcing techniques penetrate to such popular area as data science. This paper considers the questions about teaching data science and the areas, which borrow the techniques from data science. The review of learning outcomes, which may be gained from projects of citizen science, allows to propose educational data expeditions to be adopted into educational courses. Moreover, the paper represents the principles of citizen science as a mean of making a fully open educational project and to validate it as a learning tool.