Artificial Intelligence and Data Science for Leaders

Describe the general challenges and opportunities that data, data science, and AI present leaders across organizations and industries and identify where in your own organization there may be opportunities for strong data science leadership. Understand the diversity of roles required for data science teams to be effective and how they work together.

Learn to create a plan to grow in your own professional life as a data science leader and understand how data-driven decision-making creates value beyond buzzwords. Understand how business acumen enables data-driven decision-making, and why it is crucial in the success of data scientists and data science projects.

Explore the economic drivers of a business, understand approaches in the continuous development of business acumen, and learn how to conduct an economic value estimation.

Learn to distinguish among the major subdisciplines and trends in the machine learning landscape and to describe various techniques and use cases for supervised, unsupervised, and reinforcement learning. You will also learn to identify the ML approaches that would be most promising for your own business problems.

Understand future projections and trends in the AI realm and discover the usage of various algorithms in a simple to advanced spectrum for a specific use case. You will grasp the concepts of anomaly detection using AI and model interpretation and assessment.

Discover AI as an end-to-end service using the healthcare context as an example and learn the importance of open-source developments and understand the process behind employing full stack scalable big data pipelines with AWS as a use case. You will also analyze and act on the ethical implications of data management and research.

Develop leadership competencies for planning and building a world-class team and learn about the tools needed to set up your organization for data science and long-term success. In this module you will apply your learning to the real-world with a case study.

Apply knowledge gained from previous modules to a real-life use case by addressing a fully-formed business problem typically faced by data science teams and develop a team made up of technical and non-technical workers and define the role of each team member in the context of a real-life use case.