Practice Data Cleaning And Preprocessing (1.4.3) - Introduction to Data Science

Practice: Data Cleaning and Preprocessing

Lesson 1.4.3Introduction to Data Science

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9 questions

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2 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
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is data cleaning?
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Think about why accuracy in data is important.
Question 2.
Name one reason why standardization is important.
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Consider the formats of the data.
3Question 3.
What does data cleaning involve?
- Correcting errors
- Adding new data
- Removing duplicates
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It's the opposite of introducing data.
4Question 4.
True or False: Standardization ensures that all data points are on the same scale.
- True
- False
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Consistency is key in data analysis.
Question 5.
You inherit a dataset with 20% missing values across various columns. Discuss a comprehensive strategy for addressing these missing values, including potential biases in your approach.
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Categorize missingness and determine an ideal approach of filling in or removing.
Question 6.
You notice that categorical data in your dataset is inconsistent (e.g., 'male' vs 'Male' vs 'M'). Create a step-by-step guide for standardizing this entry.
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Consider the majority format selection or how the analysis might impact result comprehensibility.

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