Data Science Challenges

Data science is exciting but comes with its hurdles. From data quality to model deployment, challenges abound. Let's dive in!

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· Missing values · Inconsistencies · Outliers

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· Creating meaningful features from raw data is an art · It requires domain knowledge and creativity

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· Overfitting leads to poor generalization · Underfitting results in low accuracy · Regularization techniques help

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· Black-box models are powerful but lack transparency · Understanding how models make decisions is crucial for trust and explainability

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· Large datasets and complex models demand significant computing power · Access to GPUs and cloud resources is often necessary

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Bias, privacy, and fairness must be considered. Responsible AI is essential.

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Data science is exciting but comes with its hurdles. From data quality to model deployment, challenges abound. Let's dive in!