Data Science isn’t just about collecting data; it’s about asking the right questions and finding the answers that can help make decisions. Depending on the type of question, data scientists use different techniques to uncover insights. Let’s explore the four main types of Data Science questions and how they work in everyday examples.

data science questions


Descriptive Questions: What Happened?

Descriptive questions help you understand past events or trends by summarizing historical data. These questions focus on "what happened" in the past, giving you a clear picture of data trends. Example: In a retail business, you might ask, “What were the total sales in the last month?” or “Which products sold the most over the last year?” Use Case: Descriptive analysis is often used for monthly reports, customer behavior analysis, or tracking the success of a marketing campaign.

Diagnostic Questions: Why Did It Happen?

Diagnostic questions dig deeper to understand the reasons behind specific trends or behaviors. They help identify the "why" behind an event. Example: If you notice that your website traffic dropped last month, you may ask, “Why did the website traffic decrease in September compared to August?” Use Case: Diagnostic analysis is used to find out why sales might have dropped, why certain products are returned more frequently, or why customers leave bad reviews.



Predictive Questions: What Will Happen?

Predictive questions are all about forecasting the future using historical data. These questions allow you to make informed guesses about future events. Example: If you run a coffee shop, you could ask, “How many cups of coffee are we likely to sell next weekend based on the weather and past sales data?” Use Case: Predictive analysis is common in sales forecasting, customer behavior prediction, and demand planning.


Prescriptive Questions: What Should We Do About It?

Prescriptive questions go beyond predicting future events; they suggest actions you can take to achieve a desired outcome. These questions provide recommendations for decision-making. Example: Using data from your sales forecasts, you could ask, “What should we do to increase coffee sales on rainy days? Should we offer special promotions or discounts?” Use Case: Prescriptive analysis is used for optimizing marketing strategies, pricing decisions, and business operations.


Examples of Data Science Questions in Action:

Descriptive:

Question: “What were last quarter’s sales figures for our top 5 products?” Insight: You can see which products performed best and when, helping you understand your strengths.

Diagnostic:

  • Question: “Why did customer satisfaction drop after launching the new product line?”
  • Insight: After analyzing customer reviews and feedback, you may discover that the new product's quality was inconsistent.

Predictive:

  • Question: “How many units of our new product will sell during the holiday season?”
  • Insight: You can forecast sales and stock up on the right amount of inventory to meet customer demand.

Prescriptive:

  • Question: “What marketing strategies should we use to boost sales of our new product line?”
  • Insight: Based on customer behavior data, you might decide to offer targeted ads or discounts to specific customer groups.