Ensure that your organization is future-fit in the digital age
Download the Brochure
Download the Brochure
Explore artificial intelligence and data science with industry insiders
is the average annual base pay for a CTO in the United States
C-suite executives believe scaling AI over the next five years is crucial to the survival of their business
Is the projected market value of the artificial intelligence industry by 2023.
Source: Analytics Insights
Our Approach to Online Learning
Our Artificial Intelligence and Data Science for Leaders program provides you with the frameworks that will help you discover the opportunities that new technologies present for your organization.
Make the most of your time with a modality that enables you to study the contents and complete assignments at your own pace. If you are unable to attend webinars or live sessions, you can access them later.
Our interactive content includes videos from instructors at the University of Chicago and visual and graphic materials that provide real-life examples.
The teaching assistant—a subject matter expert—will accompany you on your journey through the content, answering your questions and providing feedback on your work.
Meet Your Instructors
Unique Program Features
After completing the course, you will be able to:
- Create a strategy for your organization that makes use of AI to accomplish business goals
- Assemble a balanced data science team made up of professionals with the required backgrounds and skill sets.
- Choose the best areas for early-stage development and understand how to scale AI solutions
- Earn a certificate of completion from the University of Chicago and become part of the UChicago network
- Why is this program crucial for AI leadership?
- What is data science?
- Data science community of roles
- Leadership competencies
- The leadership pipeline
- Data-driven decision-making
- Business acumen
- The five drivers of business
- Developing business acumen
- Economic value estimation
- Data science and the big data picture
- AI and ML landscape and tasks
- Unsupervised learning: descriptive cases and association rules
- Unsupervised learning: recommendation systems
- Unsupervised learning: data mining with pattern recognition and clustering
- Supervised learning: classification and regression
- Reinforcement learning
- Future of AI: trends to 2025
- Learning algorithms in a spectrum
- Overcoming challenges in using machine learning
- Model interpretation and assessment
- Adopting AI in healthcare
- Open-source collaboration and Amazon Web Services (AWS)
- Data ethics and privacy
- Getting the team ready
- Needs assessment
- Building the team
- Giving an effective presentation
- Unir todo en una historia coherente para cada caso
- Compartir las últimas tendencias en ética de datos y privacidad
- Errores inherentes en el modelado predictivo y los riesgos asociados