Practice Key Challenges In Advanced Data Science (1.7) - Introduction to Advanced Data Science

Practice: Key Challenges in Advanced Data Science

Lesson 1.7Introduction to Advanced Data Science

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11 questions

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5 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
5 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
Explain what is meant by data quality.
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Think of how good or bad data can impact decisions.
Question 2.
What does model interpretability mean?
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Consider why transparency in decision-making is important.
3Question 3.
What is a significant challenge related to data quality?
- Data completeness
- Model interpretability
- Data integration
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Think about what makes data reliable and usable.
4Question 4.
True or False: Model interpretability refers to how well a human can understand the model's decisions.
- True
- False
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Remember why transparency is important in data science.
Question 5.
Design a comprehensive strategy to address data quality issues in a large dataset used for predictive modeling.
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Consider both pre-processing and ongoing quality checks.
Question 6.
How would you propose to bridge the skills gap in a data science team lacking diversity of expertise?
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Think about initiatives to build a supportive learning environment.

7 more questions in this section. Enroll free to practise them all.