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Introduction to Data Science Concepts

Data Science

This set of questions covers fundamental concepts in data science relevant to a high school curriculum.

data analysis statistics programming machine learning

8 Questions Medium Ages 14+ Apr 5, 2026

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About this Study Set

This study set covers Data Science through 8 practice questions. This set of questions covers fundamental concepts in data science relevant to a high school curriculum. Every question includes the correct answer so you can learn as you go — pick any format above to get started.

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Browse all 8 questions from the Introduction to Data Science Concepts study set below. Each question shows the correct answer — select a study format above to practice interactively.

1 Which of the following programming languages is commonly used in data science for its extensive libraries like Pandas and NumPy?

A Java
B Python
C C++
D Ruby

2 In data science, what is the primary purpose of data cleaning?

A To add more features to the dataset
B To remove irrelevant data and correct errors
C To create complex visualizations
D To build predictive models

3 What type of data is represented by categories or labels, such as 'red', 'blue', or 'green'?

A Numerical data
B Categorical data
C Time-series data
D Text data

4 Which statistical measure represents the average value of a dataset?

A Median
B Mode
C Standard Deviation
D Mean

5 What is the term for algorithms that learn patterns from data without explicit programming for each task?

A Supervised Learning
B Unsupervised Learning
C Reinforcement Learning
D Machine Learning

6 Which data visualization technique is best suited for showing the distribution of a single numerical variable?

A Scatter Plot
B Bar Chart
C Histogram
D Line Graph

7 In the context of machine learning, what is 'feature engineering'?

A Selecting the target variable
B Creating new input features from existing data
C Evaluating the model's performance
D Choosing the algorithm

8 What does the term 'overfitting' refer to in machine learning?

A A model that performs poorly on training data
B A model that performs well on training data but poorly on unseen data
C A model that is too simple to capture the underlying patterns
D A model that is computationally too expensive

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