Data Science for Leaders

Issued by North Carolina State Executive Education

Badge earners have developed foundational technical skills and intermediate communication skills needed to effectively leverage data science processes to improve decision-making. They are distinguished data-savvy leaders, managers, and/or consultants who understand the challenges facing the industry. They can effectively identify a data science project opportunity, prepare the question to be answered and location of data, and communicate effectively with data science technical experts.

Type Experience
Level Foundational
Time Weeks
Cost Paid

Skills

Earning Criteria

Complete the "Introduction to Data Science Principles" session which provides: a basic overview of data analytic terminology; lays out use cases for Data Science projects; and, explains end-to-end Data Science. The session also covers: unprecedented and rapid increase in the volume and types of data available in the world today; the impacts of this change for business; and, basic analytics terminology and examples of application to business growth and challenges presented.
Complete "Management and Analytics" which covers: the roles and responsibilities of leaders, SME, and data scientists; how to create successful career paths; "People Analytics" defines HR; four gears of data analytics maturity, data analytics leader, team composition and management; analytics leader's playbook, defining a sustainable and resilient analytics agenda, build customer-driven systems analytics; challenges and opportunities of Big Data Analytics; and, how to avoid critical mistakes.
Complete the "Building the Model" session which covers: asking the right question prior to data analysis is critical; designing the right question for a data project; recognizing different types of data that will be important; and, identifying how to use each of them in various business decision-making models (e.g., the difference between strategic and operational decisions). The session will also discuss aggregation vs. isolation and the basics of Machine Learning.
At the end of the four-week course, learners have to develop and present a data analytic project that demonstrates their knowledge of a current issue affecting the industry, identify its business impact, and utilize data sets and analytics to assess the issue. They must defend their model to the professor, learners, and business professionals.