Problem Definition

Defining your question is the first and one of the most important steps in data analysis. It sets the direction for your entire analysis. This is where you decide what you want to learn from your data.

The question you ask should be specific and measurable. It should also be relevant to your data and the problem you're trying to solve.

For example, let's say you have a dataset of customer purchases from an online store. Here are a few examples of questions you might ask:

1. Descriptive: What is the average purchase price for each product category?
2. Diagnostic: Why did sales decrease in the last quarter?
3. Predictive: Can we predict future sales based on past purchase behavior?
4. Prescriptive: What actions can we take to increase sales in the next quarter?

Each of these questions would lead to a different type of analysis. A descriptive analysis might involve calculating averages and creating visualizations. A diagnostic analysis might involve looking for correlations between sales and other variables. A predictive analysis might involve building a machine learning model. A prescriptive analysis might involve using optimization techniques to find the best actions to take.

Remember, the question you ask will guide your entire analysis, so it's worth spending some time to get it right.