Practice Data Cleaning and Preprocessing - 1.4.3 | Introduction to Data Science | Data Science Basic
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skills—perfect for learners of all ages.

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is data cleaning?

💡 Hint: Think about why accuracy in data is important.

Question 2

Easy

Name one reason why standardization is important.

💡 Hint: Consider the formats of the 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 data cleaning involve?

  • Correcting errors
  • Adding new data
  • Removing duplicates

💡 Hint: It's the opposite of introducing data.

Question 2

True or False: Standardization ensures that all data points are on the same scale.

  • True
  • False

💡 Hint: Consistency is key in data analysis.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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.

💡 Hint: Categorize missingness and determine an ideal approach of filling in or removing.

Question 2

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.

💡 Hint: Consider the majority format selection or how the analysis might impact result comprehensibility.

Challenge and get performance evaluation