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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.

Questions & Answers

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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