Fundamentals of Data Science

What is data science?

• Data science is solely focused on programming languages.

• Data science is a branch of philosophy that studies human behavior.

• Data science is the study of extracting insights and knowledge from data using scientific methods and algorithms.

• Data science is the art of creating visual art from data.

Which programming language is commonly used in data science?

• Python

• Java

• C++

• Ruby

What is the purpose of data cleaning?

• The purpose of data cleaning is to ensure data accuracy and consistency.

• To simplify data entry processes

• To increase data storage capacity

• To enhance data visualization

What is a dataset?

• A dataset is a single piece of data.

• A dataset is a collection of related data organized in a structured format.

• A dataset is a random collection of unrelated files.

• A dataset is a type of software application.

What is the difference between structured and unstructured data?

• Structured data is always in text format, while unstructured data is in binary format.

• Structured data is organized and easily searchable, while unstructured data is unorganized and harder to analyze.

• Unstructured data is always organized in a specific format.

• Structured data is less reliable than unstructured data.

What is a machine learning model?

• A machine learning model is a mathematical representation that learns from data to make predictions or decisions.

• A machine learning model is a physical device that performs calculations.

• A machine learning model is a type of software that requires no data to function.

• A machine learning model is a set of rules that cannot adapt over time.

What does 'overfitting' mean in machine learning?

• Overfitting means a model is perfectly accurate on both training and new data.

• Overfitting is when a model performs well on training data but poorly on new data due to excessive complexity.

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns.

• Overfitting is when a model performs well on new data but poorly on training data.

What is the role of a data analyst?

• To design user interfaces for software applications.

• To manage the company's financial accounts.

• To create marketing strategies for products.

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

What is the significance of data visualization?

• Data visualization enhances understanding and communication of data insights.

• Data visualization replaces the need for data analysis.

• Data visualization is only useful for large datasets.

• Data visualization complicates data analysis.

What is the difference between supervised and unsupervised learning?

• Supervised learning can only be applied to images, while unsupervised learning can be applied to text.

• Supervised learning is used for clustering, while unsupervised learning is used for classification.

• Supervised learning requires no data for training, while unsupervised learning requires labeled data.

• Supervised learning uses labeled data for training, while unsupervised learning uses unlabeled data to find patterns.