
Test Your Data Science Knowledge
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20 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary purpose of data science?
To store large amounts of data.
To extract insights and knowledge from data.
To create complex algorithms without data.
To visualize data without analysis.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which programming language is most commonly used in data science?
Python
Ruby
C++
Java
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the difference between supervised and unsupervised learning?
Supervised learning is faster than unsupervised learning.
Supervised learning is only used for classification tasks.
Supervised learning uses labeled data for training, while unsupervised learning uses unlabeled data to find patterns.
Unsupervised learning requires more data than supervised learning.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does the term 'overfitting' refer to in machine learning?
Overfitting refers to a model that is too complex and learns the training data too well, leading to poor performance on new data.
Overfitting refers to a model that is trained on too little data, leading to high bias.
Overfitting is when a model performs equally well on both training and new data.
Overfitting occurs when a model is too simple and fails to capture the underlying patterns in the data.
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a confusion matrix used for?
To visualize data distributions
To perform regression analysis
A confusion matrix is used to evaluate the performance of a classification model.
To calculate the mean of a dataset
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Define the term 'feature engineering'.
Feature engineering is the method of cleaning data before analysis.
Feature engineering is the process of creating and selecting features to enhance model performance.
Feature engineering refers to the visualization of data trends.
Feature engineering is the process of collecting data for analysis.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of a validation set in machine learning?
To replace the need for a test set.
The purpose of a validation set in machine learning is to tune hyperparameters and prevent overfitting.
To evaluate the final model's performance.
To increase the size of the training set.
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