30.4.1 - Data Collection and Preprocessing
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Practice Questions
Test your understanding with targeted questions
What is data cleaning?
💡 Hint: Think about why we need accurate data for analysis.
Why is normalization needed?
💡 Hint: Consider how different units can affect calculations.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is the primary goal of data cleaning?
💡 Hint: Remember the importance of accurate data for reliable analysis.
True or False: Normalization is unnecessary if all data is already on a similar scale.
💡 Hint: Think about data representation in machine learning models.
2 more questions available
Challenge Problems
Push your limits with advanced challenges
Given a dataset with missing values, describe two different methods you could use to address the issue, including the potential consequences of each approach.
💡 Hint: Think about the trade-offs between data integrity and accuracy in predictions.
Imagine you have collected sensor data with significant outliers due to equipment malfunction. How would you identify and handle these outliers in your preprocessing steps?
💡 Hint: Consider how outliers can skew the results in statistical analyses.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.