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Questions and Answers
What is a primary use of graphs in data science?
Which graphical technique is primarily used for visualizing categorical variables?
What does univariate non-graphical EDA focus on examining?
Which of the following is not a characteristic examined in quantitative EDA?
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What does multivariate graphical EDA provide?
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Which graphical technique is primarily used for categorical variables?
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What technique is appropriate for exploring the relationship between two categorical variables?
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Which of the following best describes the purpose of using multivariate graphical methods?
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How do non-graphical methods complement graphical methods in EDA?
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Which method would best help understand one variable’s characteristics?
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What is the primary goal of exploratory data analysis (EDA)?
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Which of the following is NOT a typical question asked in EDA?
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How are EDA methods classified?
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What do graphical EDA methods primarily involve?
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What type of distribution describes a scenario where data values are not evenly spread around the mean?
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Which of the following aspects is NOT part of examining data in EDA?
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Why is it important for data scientists to care about variable distribution in EDA?
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What is a characteristic of univariate EDA?
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Study Notes
Exploratory Data Analysis (EDA)
- EDA is the process of examining and understanding data using various techniques to extract key characteristics, facilitating further analysis and decision-making.
- EDA helps to assess data quality, identify patterns, relationships, and trends, identify important variables, and test underlying assumptions.
EDA Methods
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Univariate vs Multivariate:
- Univariate methods examine one variable at a time, while multivariate methods analyze two or more variables simultaneously to explore relationships.
- Multivariate EDA is often bivariate in data science.
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Graphical vs Non-graphical:
- Graphical methods use visual representations to summarize data (e.g., charts, graphs).
- Non-graphical methods utilize statistical calculations to provide insights into variable characteristics and distributions.
Data Distribution
- Data distributions can be symmetrical (e.g., normal distribution) or asymmetrical (e.g., skewed distribution).
Univariate Graphical EDA
- Uses graphs to understand a single variable's distribution, providing insights into shapes, central tendencies, spreads, skewness, and outliers.
- Common techniques include:
- Histograms and Boxplots for quantitative variables.
- Bar and Pie charts for categorical variables.
Univariate Non-graphical EDA
- Examines one variable at a time to understand its underlying distribution or pattern.
- For quantitative variables, it analyzes:
- Spread (standard deviation and variance).
- Central tendency (mean, median, and mode).
- Skewness (measure of distribution asymmetry).
- Kurtosis (measure of distribution tailedness).
- For categorical variables, it involves tabulating the frequency of each category.
Multivariate Graphical EDA
- Displays relationships between two or more variables using graphics, providing a comprehensive understanding of the data.
- Common techniques include:
- Scatterplots and Line charts for quantitative variables.
- Side-by-side Boxplots for one categorical and one quantitative variable.
- Stacked Bars for categorical variables.
Multivariate Non-graphical EDA
- Explores relationships between two or more variables through:
- Cross-tabulation for categorical variables.
- Statistics and computation of covariance and correlation to measure the degree of relationship between variables.
Choosing EDA Methods
- Data scientists utilize a combination of EDA methods to understand their dataset.
- Non-graphical and graphical methods complement each other, offering both quantitative and qualitative perspectives.
- Univariate methods focus on individual variable characteristics, while multivariate methods explore variable relationships within the dataset.
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Description
This quiz covers the essential methods of Exploratory Data Analysis (EDA), focusing on univariate and multivariate techniques. You will learn about graphical and non-graphical methods, as well as data distributions. Test your understanding of key concepts that facilitate data analysis and decision-making.
