What are the main types of data analytics (descriptive, diagnostic, predictive, prescriptive)?

1. Descriptive Analytics — “What happened?”

**Descriptive analytics focuses on summarizing past data** to understand what has already happened in a business. It answers questions like:

• How many sales did we make last quarter?
• What was our website traffic last month?

🛠 Tools used: Excel, Google Analytics, Tableau 📊 Example: A company tracks monthly sales reports to identify which products performed best.

2. Diagnostic Analytics — “Why did it happen?”

Diagnostic analytics digs deeper to find the cause behind past outcomes. It uses techniques like **data mining, correlations, and root cause analysis**. It answers questions like:

• Why did sales drop in a specific region?
• Why did customer churn increase?

🛠 Tools used: SQL, Python, Power BI 📊 Example: A telecom company analyzes customer complaints to find why users are canceling their subscriptions.

3. Predictive Analytics — “What could happen?”

Predictive analytics uses historical data, statistical models, and machine learning to forecast future events. It answers questions like:

• What will our next quarter’s sales look like?
• Which customers are most likely to buy again?

🛠 Tools used: Python (scikit-learn), R, SAS 📊 Example: An e-commerce platform predicts which customers might abandon their shopping carts.

4. Prescriptive Analytics — “What should we do?”

Prescriptive analytics goes a step further by **recommending actions** to achieve desired results. It combines data insights, predictive models, and optimization algorithms. It answers questions like:

• How can we increase revenue while reducing costs?
• What’s the best marketing strategy for **maximum ROI**?

🛠 Tools used: AI, optimization models, simulation tools 📊 Example: An airline uses prescriptive analytics to determine optimal ticket pricing and flight schedules.