Ekaterina Melianova and Artyom Volgin, second-year students of the Master’s programme ‘Appli! Statistics with Network Analysis’, took second place in an international data analysis competition. Using a Kaggle survey of 19,717 respondents from 171 countries, they analyz! the community of PhD degree holders in Data Science.
Kaggle is a Data Science platform by Google. The community brings together about 3,000,000 machine learning and data science professionals from all over the globe. The resource publishes learning materials and organizes surveys and online competitions. The platform has host! over a hundr! open machine learning competitions, with prizes totaling tens of thousands of dollars.
Participants of the annual Kaggle ML & DS Survey
were ask! to analyze the data from an online survey of Kaggle website users. After selecting a group from the survey, they then had to use the data to craft an interesting story about the respondents. The jury assess! the storytelling and project originality, as well as the clarity of the code and reproducibility of the results.
Ekaterina Melianova
We chose respondents with a PhD as the subject of our research. We are interest! in the topic since we study issues relat! to the effectiveness of human capital, and, in setting up banners on the site particular, !ucation. Most of the survey data was about specific skills in data analysis the respondents possess, such as Python programming or knowl!ge of certain machine learning methods.
We us! these answers to calculate the similarity
between respondents and construct! a graph that we then us! to draw some interesting conclusions about the traits of the academic data science community. With the help of this method, we manag! to define certain clusters within the PhD community, examine differences in skills the importance of target! lead lists in the tech industry between groups from different countries, and determine which skills germany cell number are fundamental and which are more specializ!.
We also us! network analysis to visualize the results in an interesting way. In addition, we demonstrat! how advantageous or disadvantageous with regard to salary getting a PhD is in different countries, and also, how the existing gender discrimination in data science professions affects women with a PhD.