Recall & Review
beginner
What is Exploratory Data Analysis (EDA)?
EDA is the process of examining data sets to summarize their main characteristics, often using visual methods. It helps understand the data before applying any modeling.
Click to reveal answer
beginner
Name three common techniques used in EDA.
Common techniques include:
1. Summary statistics (mean, median, mode)
2. Data visualization (histograms, scatter plots)
3. Checking for missing values and outliers.
1. Summary statistics (mean, median, mode)
2. Data visualization (histograms, scatter plots)
3. Checking for missing values and outliers.
Click to reveal answer
beginner
Why is it important to check for missing values during EDA?
Missing values can affect analysis and model accuracy. Identifying them early helps decide how to handle them, like filling in or removing those data points.
Click to reveal answer
intermediate
What does a box plot show in EDA?
A box plot shows the distribution of data through its quartiles, highlights the median, and identifies outliers. It helps quickly see spread and skewness.
Click to reveal answer
intermediate
How can correlation analysis help in EDA?
Correlation analysis shows how two variables move together. It helps find relationships or dependencies, which can guide feature selection for modeling.
Click to reveal answer
Which of the following is NOT a typical step in Exploratory Data Analysis?
What does a histogram help you understand in EDA?
Why is it useful to identify outliers during EDA?
Which plot is best to check the relationship between two numeric variables?
What does a correlation coefficient close to 0 indicate?
Describe the main goals and steps involved in Exploratory Data Analysis.
Explain how visualizations help in Exploratory Data Analysis and name two common types.