To build predictive models
To find hidden patterns
To summarize data
To collect data
Answer is Right!
Answer is Wrong!
The correct answer is: B. To find hidden patterns.
Data exploration is the process of analyzing data to find hidden patterns and insights. It is the first step in data science, and it is essential for building predictive models and making informed decisions.
There are many different ways to explore data. Some common methods include:
- Visualization: Data can be visualized using charts, graphs, and other visuals. This can help to identify patterns and trends that would not be visible in a table or spreadsheet.
- Statistical analysis: Data can be analyzed using statistical methods to identify relationships between variables. This can help to find hidden patterns and insights.
- Machine learning: Machine learning algorithms can be used to automatically identify patterns in data. This can be a powerful tool for finding hidden patterns that would be difficult to find manually.
Data exploration is an iterative process. As you explore data, you will likely find new questions to ask and new insights to discover. This is why it is important to be flexible and to be willing to change your approach as you learn more about the data.
The other options are incorrect because:
- A. To build predictive models is not the primary objective of data exploration. Data exploration is the first step in data science, and it is essential for building predictive models, but it is not the only step.
- C. To summarize data is not the primary objective of data exploration. Data exploration is about finding hidden patterns and insights, not about summarizing data.
- D. To collect data is not the primary objective of data exploration. Data exploration is about analyzing data that has already been collected, not about collecting data.