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Event Features
- Step-by-step learning roadmap
- Skill progression structure
- Timeline guidance
- Beginner learning mistakes explained
- Essentials tools overview
- Portfolio-first learning approach
- Career preparation insights
- Live Q&A support
Event Description
The instructor planned to cover skill order (fundamentals → tooling → projects → interview), recommended learning timelines, key technologies, and common beginner mistakes. A portion of the agenda was dedicated to showing how consistent practice and project work create job readiness.
Schedule
1. The meeting opened with a breakdown of foundational skills like Python, Math, and SQL.
2. The instructor then introduced key Data & AI learning phases including data handling, data visualization, machine learning basics, and applied projects.
3. Timelines were shared to help learners manage expectations.
4. Students asked about balancing learning with college/work and whether degrees were necessary.
5. The instructor emphasized consistency, portfolio building, and real-world projects.
6. The session ended with an encouraging note that beginners can become job-ready with disciplined learning.
2. The instructor then introduced key Data & AI learning phases including data handling, data visualization, machine learning basics, and applied projects.
3. Timelines were shared to help learners manage expectations.
4. Students asked about balancing learning with college/work and whether degrees were necessary.
5. The instructor emphasized consistency, portfolio building, and real-world projects.
6. The session ended with an encouraging note that beginners can become job-ready with disciplined learning.
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