Data Science Quiz For Humanities

Ponne, Bruno

18 hours ago

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Test your skills with this interactive data science quiz covering statistics, Python, R, and data analysis.

Progress

Answered 0 of 15

< !-- Questions 1–15 --> < !-- 1 --> < section class="question" data-q="q1">

Which of the following best describes a z-score?

A measure of central tendency The number of standard deviations a value is from the mean The square of the correlation coefficient A type of probability distribution

< !-- 2 --> < section class="question" data-q="q2">

What is the main advantage of using tidy data principles in R?

Increased computation speed Easier visualization and consistent analysis Reduced memory usage Automatically removes missing values

< !-- 3 --> < section class="question" data-q="q3">

In Python, which library is most commonly used for data manipulation?

matplotlib numpy pandas statsmodels

< !-- 4 --> < section class="question" data-q="q4">

Which metric is best for evaluating a classification model on imbalanced data?

Accuracy Recall Variance R-squared

< !-- 5 --> < section class="question" data-q="q5">

In a linear regression, what does R² represent?

Slope of the regression line Variance explained by the model Covariance between variables Degree of overfitting

< !-- 6 --> < section class="question" data-q="q6">

In historical or humanities datasets, which challenge occurs most frequently?

Excessively large sample sizes Perfectly standardized variable names Missing or incomplete records Highly structured relational databases

< !-- 7 --> < section class="question" data-q="q7">

What does the groupby() function do in pandas?

Sorts values by category Applies aggregate operations to subsets of data Removes duplicates Normalizes columns

< !-- 8 --> < section class="question" data-q="q8">

What is the primary purpose of cross-validation?

Increase training accuracy Test different loss functions Evaluate a model on unseen data to reduce overfitting Speed up model training

< !-- 9 --> < section class="question" data-q="q9">

Feature engineering refers to:

Training a model with more iterations Preparing input variables to improve model performance Removing outliers Selecting the best model

< !-- 10 --> < section class="question" data-q="q10">

Which visualization is most appropriate for the distribution of a continuous variable?

Bar chart Histogram Pie chart Line plot

< !-- 11 --> < section class="question" data-q="q11">

A z-score of +2.5 means:

The value is below the mean The value is 2.5 SD above the mean The value is an outlier The standard deviation is 2.5

< !-- 12 --> < section class="question" data-q="q12">

Which is an advantage of using R for statistical analysis?

Native GPU acceleration Strong statistical libraries and ggplot2 Automatic machine learning Faster than Python

< !-- 13 --> < section class="question" data-q="q13">

Normalization in data preprocessing means:

Converting categorical data to numeric Rescaling values to a standard range like 0–1 Detecting outliers Filling missing values

< !-- 14 --> < section class="question" data-q="q14">

Why may historical datasets be biased?

They always include all records Selective or incomplete record-keeping Automatic modern data collection Perfect measurement systems

< !-- 15 --> < section class="question" data-q="q15">

Which Python function can compute a z-score?

pandas.normalize() scipy.stats.zscore() numpy.z() matplotlib.stats()

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