Which techniques do data scientists typically use for exploratory data analysis?1 pointThey use descriptive statistics and data visualization techniquesThey use support vector machines and neural networks as feature extraction techniques.They use deep learningThey begin with regression, classification, or clustering

Data scientists typically use descriptive statistics and data visualization techniques for exploratory data analysis. These methods allow them to understand the main characteristics of a dataset, identify any outliers or anomalies, and visualize relationships between variables.

Descriptive statistics provide a summary of the data, including measures such as mean, median, mode, standard deviation, and range. These statistics can give an initial understanding of the distribution and variability of the data.

Data visualization techniques, such as histograms, scatter plots, and box plots, are used to graphically represent the data. These visualizations can help identify patterns, trends, and correlations that may not be apparent from the raw data alone.

While support vector machines, neural networks, and deep learning are powerful techniques, they are typically used for predictive modeling or classification tasks, rather than exploratory data analysis.

Similarly, regression, classification, and clustering are also used for predictive modeling or to understand the relationships between variables, rather than for initial exploration of the data.