Data Leader Series: Data Mining

This dynamic course unfolds the profound narrative of digital transformation within the context of Industry 4.0, examining how technological innovations and an ever-increasing reliance on data are reshaping the corporate landscape. Participants will delve into the world of Data Mining, understanding its fundamental concepts, frameworks, and pivotal role in informing business decisions. The course demystifies complex Data Mining processes, emphasizing the CRISP-DM framework to steer participants from raw data to actionable insights. As the course progresses, it explores the practical application of these concepts within Data Science Projects, highlighting evaluation techniques, project prioritization, and the unique considerations required in the healthcare sector. It confronts the challenges and ethical implications of AI adoption, equipping participants with the foresight to navigate these concerns proactively. The final module centers on Data Science Teams, fostering leadership skills that are essential for managing teams, organizational capacity, and effective communication of data science findings. This course is not only an educational journey but also a strategic toolkit designed to empower professionals to become adept navigators of the digital transformation era.

Upon completion of this course, learners will be able to: 

1. Participants will understand the core principles of digital transformation, learning to apply Data Mining techniques using the CRISP-DM framework effectively.
2. Learners will be equipped to critically assess data science solutions, prioritize projects, and manage specialized projects in healthcare sectors.
3. The course will prepare professionals to address and manage the challenges of AI adoption in business, including navigating the complex landscape of AI ethics.
4. Attendees will acquire leadership skills for building and managing data science teams, optimizing data capacity, and clearly communicating project outcomes.

1. Business leaders interested in harnessing data mining and AI for strategic advantage.
2. Data scientists aiming to deepen their knowledge of industry-standard frameworks.
3. Healthcare managers focusing on data-driven decision-making and project management.
4. Ethicists and compliance officers concerned with AI adoption and ethical practices.
5. Team leads responsible for managing data science projects and personnel.

Dwight graduated with a degree in Biotechnology (from NUS in 2001), Advanced diploma in Pharmaceutical management (from MDIS in 2007) and Masters in Education (from NTU in 2013). In 2014, Dwight ventured into Data Analytics and Database Management and picked up Statistics/Machine Learning, Big Data analytics, text mining & social media analytics and web applications. His other expertise includes data science programming languages such as R and Python. He is also deeply passionate in deep learning using sequential models and sequence to sequence models using Keras and h2oAI machine & deep learning platforms and libraries. He is also a Senior Data Analyst, where he performs data analytics and create predictive models using healthcare data.