SEMT202 Data Science and Machine Learning Applications in Digital Learning
11:00 AM - 11:45 AM Thursday, October 24
Expo Hall: Emerging Tech Stage
Most digital learning professionals know that data science and artificial intelligence (AI) can make their everyday processes more efficient and help them reach business goals faster. However, many still have questions around the real-life applications of data science and AI within digital learning, including:
- What are some examples of how AI and data science can address digital learning challenges?
- What processes can be automated?
- At what stage of the learning strategy can these technologies be implemented?
- What are the first steps for implementing data science solutions?
- What expertise and resources are necessary to successfully implement AI and data science solutions?
- What are some best practices for preparing learning data for processing, etc.?
In this session, you will study some of our practical use cases to learn about the diverse data science and machine learning applications in education. After this session, you will have a strong understanding of how you can leverage data science for your digital learning strategy to become more efficient in reaching your business goals.
In this session you will:
- Discover diversity and variability of the data science and machine learning applications in education
- See how EPAM uses these technologies to improve learning outcomes and the impact of learning in our organization
- Learn how you could implement these technologies in your own digital learning strategy
Data science, machine learning, artificial intelligence, predictive analytics
Designers, developers, managers, senior leaders (directors, VP, CLO, executive, etc.)
Education and Learning Business Development
Chris Kelly works in education and learning business development at EPAM. He has actively consulted with numerous organizations who are seeking to develop technology-based solutions focused on learning. His background is unique in that he understands the different challenges organizations face around learning while understanding the process and different options around developing a digital learning solution. This deep domain knowledge enables him to guide organizations to avoid common pitfalls resulting in solutions that ultimately improve learner performance.