Practice Standardization (Z-score Scaling) - 5.8.2 | Data Cleaning and Preprocessing | Data Science Basic
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the formula for calculating the Z-score?

💡 Hint: Remember to consider how both mean and standard deviation are involved.

Question 2

Easy

Why is standardization important?

💡 Hint: Think about different scales of data.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does standardization do to a dataset?

  • Convert all data to a common scale
  • Remove outliers
  • Change categorical data to numerical

💡 Hint: Think about the concept of mean and deviation.

Question 2

True or False: Standardization is necessary for all types of data.

  • True
  • False

💡 Hint: Consider cases where features are homogeneous.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Suppose you have a dataset with weight, height, and age of individuals. Explain how you would prepare this dataset for a clustering algorithm using Z-score scaling.

💡 Hint: Consider how different measurements affect clustering.

Question 2

You are given a choice between normalization and standardization for a dataset heavily skewed by outliers. Which method would you choose and why?

💡 Hint: Recall the effect of outliers on each method.

Challenge and get performance evaluation