MS-DS Master of Data science Exploratory Data Analysis Questions and Answers Flashcards
6 cards from real MS-DS Master of Data science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 MS-DS Master of Data science Exploratory Data Analysis Questions and Answers flashcards as text
What is the purpose of a QQ (quantile-quantile) plot in exploratory data analysis?
Answer: To assess whether data follows a theoretical distribution
A QQ plot compares the quantiles of observed data against quantiles of a theoretical distribution to check for distributional fit.
Which measure of central tendency is most robust to outliers?
Answer: Median
The median uses only the positional middle value, so extreme outliers do not affect its calculation.
During EDA, what does a high kurtosis value indicate about a distribution?
Answer: Heavy tails and a sharp peak relative to a normal distribution
High kurtosis (leptokurtic) means the distribution has heavier tails and a sharper central peak compared to a normal distribution.
Which pandas method provides a quick statistical summary including count, mean, standard deviation, and quartiles?
Answer: describe()
The describe() method returns count, mean, std, min, 25%, 50%, 75%, and max for each numeric column.
What is the main advantage of using a violin plot over a standard box plot?
Answer: It shows the full probability density of the data
Violin plots combine a box plot with a kernel density estimate, revealing the full shape of the distribution including multimodality.
When exploring missing data patterns, which visualization technique reveals whether missingness is random or systematic?
Answer: Missing value heatmap or matrix plot
A missing value heatmap shows patterns of missingness across rows and columns, helping determine if data is missing at random or follows a systematic pattern.