I recently attended an insightful Industry Expert Master Class on Data Science & Machine Learning organized by Daffodil Institute of IT (DIPTI).
The session offered strong conceptual clarity on core data science foundations, including:
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The data science lifecycle: data collection, cleaning, exploration, modeling, evaluation, and deployment
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Key machine learning paradigms: supervised vs. unsupervised learning
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The importance of feature engineering, bias–variance tradeoff, and model evaluation metrics (accuracy, precision, recall, F1-score)
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Real-world applications of predictive analytics and data-driven decision-making
A special thanks to Dr. Md. Zahid Hasan for sharing industry-aligned insights that bridged academic theory with practical implementation, especially in research-driven and healthcare-related analytics.
Sessions like this reinforce the importance of combining strong theoretical fundamentals with real-world problem-solving, which is essential for building scalable and reliable data science solutions.