There are already many data science-related materials on the market, but most of them are technical, specialized materials such as introductory documents or academic papers. Also, it seems that there is no data on the various difficulties encountered in applying stories from data science textbooks to actual work environments. thus A guide that can be applied directly in the business based on the experience of actually developing and operating online servicesI've been thinking that it would be worthwhile to make.
In the meantime, I have encountered various data science issues related to search engines and recommendation systems at Microsoft and Snap (Snap), and I am currently leading the Data & Analytics team to solve data science and engineering issues underlying Naver's search quality management and improvement. Many of the classes are based on the work of Naver Search's Data&Analytics (DNA) team, which I lead. I hope this class will help engineers, PMs, and anyone else working with data. Sharing my realistic experience and problem solving processI'll do it.
- Understand the meaning and role of data science for improving online services.
- You can learn in advance about the transformation process to a data organization and the practical challenges encountered along the way.
- You can learn about the latest trends and development directions in data science.
- Those who are struggling with the process of transformation to a data organization and practical difficulties
- Developers, service planners, and data engineers working in the IT industry
- Those who want to know the meaning and role of data science for improving online services
- Those who want to learn about the latest trends and development directions of data science
What makes this class special
A vivid practical guide incorporating the experience of an incumbent
In the field where we actually develop and improve online services, we won't be distracted by responding to changing market conditions, competitor trends, and service issues day by day. We all know that various types of data from service operations are important, but the reality is that it is difficult to find specific insights on how to use the data that is accumulated every day. I would like to give specific guidance to incumbents related to these online services. In the class, I would like to share the process of practical difficulties I have encountered while working on data science and how I solved them.
This class is based on the winner of the Brunch Book AI Class Project.
Jin young Kim
- Head of Data Science & Director @Naver Search US
- Book: Hello Data Science
- Data Intelligence Podcast Operator