|
Abstract:
|
Data science can be defined as an interdisciplinary field that uses scientific methods, programming, and statistics to extract knowledge and real world insights from noisy data that can be structured or unstructured. Skills needed to be a successful data scientist include data manipulation, visualization, computing, modeling, and inference, skills which are a natural part of the workflow for applied statisticians. Despite this, statistics departments often fail to be at the forefront of data science because of a focus on theory and methods for general problems rather than developing statistical thinking skills in the context of real world questions. Statistics can reclaim data science education by grounding curricula in real, messy data examples that teach students to answer explicit questions using data in a practical setting. However, it remains up for debate which data science skills are best taught by statisticians, and which are better delegated to other departments. The focus of this round table discussion is to explore and debate what topics should be included in a data science curriculum to broadly educate students for careers in both industry and academia.
|
