What are the key steps of a Data Science project?1 pointCollect dataAnalyze the dataSuggest hypothesis or actionsAll of the above

All of the above. A Data Science project typically involves the following key steps:

1. Define the Problem: This is the first and most crucial step. It involves understanding the project's objectives and requirements.

2. Collect Data: This involves gathering the necessary data from various sources which could be in structured or unstructured format.

3. Clean Data: This step involves cleaning and preprocessing the data to remove any inconsistencies, errors or outliers.

4. Analyze Data: This involves exploring the data to identify patterns, trends and relationships between different variables.

5. Model Data: This involves building predictive or descriptive models using statistical or machine learning algorithms.

6. Validate Model: This involves testing the model on a separate set of data (test data) to check its accuracy and reliability.

7. Visualize Data: This involves creating visual representations of the data and model results to make it easier to interpret and understand.

8. Communicate Results: This involves presenting the results to stakeholders in a clear and understandable manner.

9. Make Decisions: Based on the results, decisions are made or actions are suggested.

10. Deploy Model: If the model is satisfactory, it is deployed for practical use.

11. Monitor Model: After deployment, the model's performance is continuously monitored and adjusted as necessary.