Data Driven Decision Making with Generative AI
About This Course
This course equips learners with in-demand skills and competencies to excel in data-driven decision-making and advanced analytics, addressing critical industry needs such as data literacy, AI integration, and ethical data practices. Learners will gain hands-on experience in data preparation, visualization, statistical analysis, and machine learning—including the transformative role of Generative AI in automating workflows and enhancing insights. They will also learn to design scalable data pipelines, operationalize analytics at scale, and ensure compliance with data governance and regulatory standards like GDPR and PDPA.
This course emphasizes practical, real-world applications, teaching learners to translate business challenges into actionable insights, build compelling data stories, and foster a data-driven culture within organizations. By understanding the application of tools like Tableau, Power BI, Python, and R, learners will be prepared to tackle complex analytics projects and drive impactful decisions. Additionally, the course covers team-building strategies to scale data science capabilities, making it invaluable for professionals aiming to lead or collaborate effectively in data-centric roles.
This course is designed to meet the growing demand for data-savvy professionals across industries like Tourism, Training & Adult Education, Engineering, Human Resources, Healthcare, and Retail. By equipping learners with cutting-edge analytics and AI skills, it opens doors to job upgrading opportunities and roles such as Data Analysts, Data Scientists, AI Specialists, and Analytics Managers.
What You'll Learn
LO2: Analyse data patterns to interpret business problems and propose data-driven solutions using appropriate problem-solving methods.
LO3: Organise data science projects by applying structured CRISP-DM-driven processes to address business challenges.
LO4: Design data visualizations and dashboards by interpreting data patterns and applying data exploration methods to communicate project insights.
LO5: Construct predictive models by interpreting data patterns and applying statistical inference, regression, and relevant tools to extract business insights.
LO6: Construct complex data mining models using statistical and modelling techniques to deliver business insights aligned with organisational procedures.
LO7: Organise data science project capacity by evaluating analytics solutions' efficiency, growth, and ROI and designing scalable pipelines through applied capacity-management techniques.
LO8: Configure data models to examine organizational business hypotheses by managing project capacity and measuring team capability, ensuring scalable, high-performing practices.
Entry Requirements
• 21 years and above
• Able to listen, speak, read, and write English at a proficiency level equivalent to Employability Skills System Workplace Literacy (WPL) Level 6
Recommended to have 1-2 years of working experience