This course is designed for executives seeking to foster a data-driven culture within their organization through informed leadership. Employees will learn need-to-know vocabulary for describing and asking informed questions about data initiatives, from the different roles that make up a data team to the data analysis techniques available. Through a series of interactive exercises and breakout discussions, this course will help executives better navigate the data components of their job and empower them to effect strategic data-driven innovation at an organizational level.

This course is designed for managers seeking to bolster their data literacy with a deep dive into data science tools and teams, project life cycles, and methods. This course will demystify the structure of data science projects from start to finish, helping students to make more informed decisions about how to identify data-driven solutions, structure their teams, allocate resources, and interpret results. Employees will also learn how to make the most compelling cases possible by comparing the advantages and disadvantages of a variety of models, methods, and visualization techniques.

This 12-hour workshop teaches participants the fundamentals of data visualization, which they can use to support data-driven decision-making and a data-driven culture. By the end of this course, participants will be able to recognize misleading or inaccurate charts and graphs, understand the design principles involved in creating compelling and accurate visualizations, and create a narrative that accurately supports the data, provides context, and reveals actionable insights.

This 12-hour workshop educates participants on the fundamentals of data science and how to apply them in such a way that it is relevant even to those who will neither manage nor consume data regularly. Attendees will learn: data science concepts and associated terminology; why data science is important; what it means to work in a data-driven culture including the skills necessary to thinking critically about data; common issues in data collection and analysis such as bias, data gaps, and imprecision; strategies for interpreting data visualizations produced by others; and foundational steps those who are not data scientists can take to incorporate data analysis into their work.