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Event Features
- Full data lifecycle breakdown
- Insider tool recommendations
- Real business workflows
- Visualization and modeling phases
- Use-cases across industries
- Hands-on skill relevance
- Fundamentals-first approach
- Live clarification session
Event Description
The instructor planned to introduce each stage, explain its purpose, and demonstrate common tools and techniques used in industry. The agenda also included business scenarios to show how lifecycle awareness improves problem-solving and career readiness.
Schedule
1. The instructor walked through the 7 lifecycle stages: data collection, storage, pre-processing, analysis, modeling, visualization, and deployment.
2. Real business scenarios such as marketing analytics and fraud detection were used to highlight practical application.
3. Tools like SQL, Pandas, Power BI, and cloud services were mentioned.
4. Questions focused on data cleaning and visualization tools.
5. The instructor concluded by reminding learners that lifecycle understanding is foundational before advanced AI concepts.
2. Real business scenarios such as marketing analytics and fraud detection were used to highlight practical application.
3. Tools like SQL, Pandas, Power BI, and cloud services were mentioned.
4. Questions focused on data cleaning and visualization tools.
5. The instructor concluded by reminding learners that lifecycle understanding is foundational before advanced AI concepts.
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