Data Science

3 units

With the development of information technology, every organization must adapt to a data-driven world where decisions are made based on not only fundamental business logic but also on real-time and high-dimensional data. The process of making data-informed, or even data-driven, decisions are called data science. Such decision-making process requires the leaders of organizations understand the technological platform of data acquisition, storage and analysis. It also requires leadership to be familiar with various data-driven decision-making techniques, their advantages, and their disadvantages. This course introduces this topic and helps leaders to utilize data to better manage their organizations. The class will start with a general introduction of data science and provides several frameworks to classify data science into multiple sub-fields. Participants will have a general understanding of data science and the organization structure of various data science teams. The class then dives into two major sub-fields of data science: causal inference and machine learning. For each sub-field, participants will learn the basic concepts behind it, how to apply the techniques in this sub-field to different problems, and how to build a team that is specialized in these techniques. Participants will also learn the advantages and disadvantages of each sub-field, and how to combine techniques from multiple sub-fields to solve complex problems. Last, this class will touch upon the topics between data science and society, including data privacy, fairness in machine learning, and adverse usage of machine learning models. Participants will learn the ethical questions behind using different data science techniques.