GenAI for Data & Analytics
This course helps professionals harness generative AI to transform data analytics into a value-driven capability. Learn to integrate people, processes, and technology to turn insights into impactful, adaptive business decisions.
Overview
This course includes:
- On-demand videos
- Practice assessments
- Multiple hands-on learning activities
- Exposure to a real-world project
- 100% self-paced learning opportunities
- Certification of completion
Driving Successful Data Analytics with Generative AI
Harnessing advanced capabilities to enhance the flow of value from data to insights, decisions, and business impact.
Do you want to make data analytics more successful in your organization?
Data analytics is more than a set of tools, models, and reports. It is an organizational capability. Capabilities are intentionally designed to be systems that deliver value. They are created by integrating many moving parts. The Analytics Capability must be built, implemented, and improved on a regular basis to deliver its expected value. Many organizations struggle with this. Analytics and Data Science initiatives often produce outputs but not outcomes. Insights may be generated but fail to influence decisions. Teams are overwhelmed by complexity, and the gap between potential and realized value remains wide.
The struggles partially stem from two forces based on growing demands and changing enablers.
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On the demand side, business leaders expect analytics to provide more than retrospective dashboards. They want predictive and prescriptive insights that shape future outcomes. Decision-making cycles are shorter, expectations for clarity are higher, and stakeholders are becoming more data-literate.
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On the enabler side, technology is advancing rapidly. Generative AI and large language models are emerging as transformative tools that can automate routine tasks, accelerate workflows, and create new ways of collaborating across technical and business boundaries. However, there are gaps in skills and understanding about how to harness these enablers to effectively meet the growing demand.
A core challenge is to reimagine analytics as a capability system that delivers value on a continuous basis and adapts to changes in both demand and enablers.
Skills You Will Gain
Learning Outcomes (At The End Of This Program, You Will Be Able To...)
- Differentiate the building blocks of AI, GenAI, and Data Analytics, and describe how LLMs support the CRISP-DM and Value Chain frameworks.
- Apply prompt engineering techniques to frame business problems for data analytics by using tools likeChatGPT, Gemini in Colab for solutions.
- Create and Communicate actionable insights to generate narratives, reports, and visualizations for analytical findings and their business impact to stakeholders.
- Evaluate data, models, and GenAI outputs for accuracy and clarity to critique, validate, and refine insights across CRISP-DM Phases and Value Chain Steps.
Prerequisites
This course requires a basic understanding of data analytics concepts such as datasets, visualizations, and reporting. Learners should be familiar with Microsoft Excel for basic data manipulation and charting. While prior exposure to Python is helpful, it’s not required, as all coding concepts will be generated and clearly explained using generative AI tools.
Who Should Attend
This course is ideal for business and data analysts aiming to accelerate insights with AI, BI developers enhancing dashboards with intelligent automation, and data scientists or engineers integrating LLMs into analytics workflows. It also benefits domain experts and decision-makers seeking to improve processes and outcomes through data-driven innovation.
