Data Science Lifecycle:

1. Data Generation and Collection Identify and gather the data you need to address a problem.

2. Data Cleaning Fix discrepancies and handle missing values in your data. 

3. Data Exploration and Analysis Study your data, then form a hypothesis.

4. Predictive Modeling Use computational tools like machine learning models to make predictions with your data.

5. Data Visualization Communicate your data findings using interactive images, plots, and charts.

6. Accelerate discovery by making your data available to others.