📅 2025-02-21 — Session: Revamped Data Science Curriculum for Future Skills
🕒 03:45–04:20
🏷️ Labels: Data Science, Curriculum Design, Machine Learning, Education, Future Skills
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal:
The session aimed to revamp the data science curriculum to align with future industry demands, focusing on core machine learning skills, emerging techniques, and the removal of outdated content.
Key Activities:
- Developed a comprehensive strategy for curriculum updates, emphasizing core ML skills and emerging techniques.
- Introduced causal inference and Bayesian ML techniques to enhance interpretability and decision-making.
- Outlined foundational pillars for self-learning in advanced ML topics, including mindset, feature engineering, model selection, and causal reasoning.
- Adapted the ML and MLOps course to cover essential data engineering skills.
- Structured a comprehensive course outline combining solid fundamentals with advanced applications.
Achievements:
- Successfully designed a framework for a future-ready data science curriculum.
- Integrated causal and Bayesian ML techniques into the curriculum for better interpretability.
- Created a structured plan for teaching essential ML and MLOps skills.
Pending Tasks:
- Finalize the curriculum details and validate with industry experts.
- Develop detailed course modules for each outlined topic.
- Implement feedback from initial curriculum testing.