Which step in the Data Science process involves visualizing and interpreting the results of data analysis?

Data Collection
Data Cleaning
Data Visualization
Model Building

The correct answer is C. Data Visualization.

Data visualization is the process of converting data into a visual representation, such as a chart, graph, or map. It is used to communicate the results of data analysis to a wider audience. Data visualization can be used to identify patterns, trends, and outliers in data. It can also be used to compare different data sets and to track changes over time.

Data collection is the process of gathering data. It can be done manually or automatically. Data collection is the first step in the data science process.

Data cleaning is the process of identifying and correcting errors in data. It is important to clean data before it is analyzed. Data cleaning can be done manually or automatically.

Model building is the process of creating a model that can be used to make predictions. A model is a mathematical representation of a system. Model building is the last step in the data science process.

Here are some examples of data visualization:

  • A bar chart can be used to compare the values of different variables.
  • A line graph can be used to track changes in data over time.
  • A pie chart can be used to show the proportions of different categories.
  • A map can be used to show the location of data points.

Data visualization can be used to communicate the results of data analysis to a wider audience. It can be used to identify patterns, trends, and outliers in data. It can also be used to compare different data sets and to track changes over time.

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