Test Your Data Science Knowledge

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.

Which programming language is most commonly used in data science?

• Python

• Ruby

• C++

• Java

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.

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.

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

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.

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.

What is the significance of the p-value in hypothesis testing?

• The p-value is the same as the confidence level in a study.

• A lower p-value indicates a higher likelihood of the null hypothesis being true.

• The p-value measures the strength of the alternative hypothesis.

• The p-value indicates the probability of observing the data, or something more extreme, if the null hypothesis is true.

Explain the concept of 'big data'.

• Big data refers to small and simple data sets that are easy to analyze.

• Big data is a term used for data that is less than 1GB in size.

• Big data is only about social media data and nothing else.

• Big data is large and complex data sets that require advanced tools and techniques for processing and analysis.

What is the role of a data analyst?

• To create software applications for data management.

• To design marketing strategies based on customer feedback.

• The role of a data analyst is to analyze data and provide insights to inform business decisions.

• To oversee the IT infrastructure of a company.