Data Leader Series: Design thinking with Data

This forward-thinking course is crafted to arm professionals with the knowledge and skills needed to lead digital transformation initiatives in the context of Industry 4.0. It uncovers the symbiosis between technological innovation and big data, highlighting the role of advanced data analytics in enhancing operational efficiency and powering predictive analytics to forecast trends and behaviors. Students will explore the essentials of design thinking as applied to AI projects, learning to evaluate successful implementations through a blend of theory and case studies.

As the course progresses, it dives into Agile Scrum and Lean methodologies, advocating for a customer-centric and value-driven approach while addressing the unique dynamics of data science teams. Participants will engage with the scrum sprint framework, gaining insights into iterative development and rapid delivery.

Finally, the concept of business agility serves as a cornerstone of the curriculum, focusing on the capabilities required to adapt and thrive in a fast-paced digital economy. By integrating AI business frameworks, harnessing positivity and productivity, and measuring performance effectively, learners are equipped to identify constraints and capitalize on enablers of organizational agility.

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

1. Participants will learn how big data contributes to digital transformation and supports operational efficiency and predictive analytics in Industry 4.0.
2. Attendees will master the design thinking framework, applying it to develop AI projects and assess their success critically.
3. Students will acquire the ability to implement Agile and Lean principles, focusing on customer value and enhancing team performance in data science.
4. Learners will identify and leverage business agility enablers, measure performance accurately, and drive continuous improvement in a tech-driven landscape

1. Data-driven executives looking to harness big data for strategic advantage.
2. Innovation managers seeking to apply design thinking to AI and tech projects.
3. Agile and Lean practitioners aiming to optimize data science teams and processes.
4. Business strategists focused on enabling and sustaining agility in organizations.
5. AI specialists and data scientists interested in integrating agile methodologies with AI initiatives.

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.