Professional Certificate in Leading in a Remote Environment
Professional Certificate Series
In this Professional Certificate program offered by Harvard, you will improve your leadership and management skills in today's work environment.
What You'll Learn
How has remote work changed how you manage and lead? While working in a remote environment provides many opportunities for global teams, it also presents unique challenges to maintaining relationships and achieving goals. Leaders need to be adaptive in order to mobilize a remote workforce to meet these new challenges. Learn how to adapt as a leader and manage a remote team in Remote Leadership.
In the first course, Exercising Leadership: Foundational Principles, you’ll learn how to be an effective leader and motivate your remote team while navigating change and common challenges.
In Remote Work Revolution for Everyone, you will learn the professional development skills to excel in virtual teams. You will learn how to build trust, increase productivity, use digital tools intelligently, and manage remote teams.
After completing the Professional Certificate in Leading in a Remote Environment, learners will understand:
- Implement practices that will allow you to be an effective leader and successfully manage a remote team
- Create personal strategies for surviving and thriving amidst change
- Develop strategies for yourself and your team, improving productivity, communication, and collaboration
- Learn how to build relationships and trust between team members with a remote workforce
- Learn how to select the right digital tools to increase productivity and access between remote employees
Job Outlook
- There are hundreds of billions of microcontrollers today, and an increasing desire to deploy machine learning models on these devices through TinyML. Learners who complete this program will be prepared to dive into this fast-growing field.
- Learners will have a fundamental understanding of TinyML applications and use cases and gain hands-on experience in programming with TensorFlow Micro and deploying TinyML models to an embedded microprocessor and system.