Naver is using various service elements to analyze and analyze complex usage patterns that users create, consume, and share contents. This presentation briefly introduces user data analysis cases around Naver search service and shares experiences of building and operating Naver search user feedback analysis platform. Specifically, we are considering why we are considering Bayesian approximation, data parallelism, task parallelism, and online algorithms in real business to derive service / business insight by analyzing large-scale data of Naver. Let me introduce you whether you are conducting research. In addition, we talk about the collaborative process that the Data Science Group should be preparing for and the competencies that a data scientist should have to make the Big Data successful.
– Graduated Department of Computational Statistics, Seoul National University
– Department of Statistics, Seoul National University (Master of Science)
– University of Washington (Ph.D, Biostatistics)
– Currently the leader of Naver Search Data Science