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• Complexity of Topics: Data analytics involves intricate techniques like regression, clustering, and machine learning, which can overwhelm students, especially without proper guidance and expertise.
• Time Constraints: Tight deadlines often make it challenging for students to complete assignments on time, especially when juggling multiple academic responsibilities and extracurricular activities.
• High Academic Expectations: Professors demand high-quality, detailed analysis, pushing students to deliver precise, error-free solutions, which can lead to stress and performance anxiety.
• Lack of Practical Experience: Many students struggle with applying theoretical knowledge to real-world data problems, resulting in difficulty completing assignments accurately and effectively.
• Difficulty in Data Interpretation: Interpreting large datasets and deriving meaningful insights is challenging for students, especially when the data is complex or poorly structured.
• Fear of Poor Performance: The fear of not meeting academic expectations or making mistakes in data analysis can create anxiety, making it difficult to focus and complete tasks effectively.

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