Data scientists gather data from various sources, including databases, sensors, websites, and more. This data can be structured or unstructured.

Raw data often contains errors, missing values, or inconsistencies. Data scientists clean and preprocess the data to ensure it's accurate and ready for analysis.

Machine learning is a subset of data science that uses algorithms to build predictive models or make automated decisions based on data.

Statistical techniques are used to uncover relationships and correlations within the data. This can involve hypothesis testing, regression analysis, and more.

EDA involves visualizing and summarizing data to understand its characteristics and patterns. This step helps identify trends and potential insights.

Effective data visualization is crucial for conveying findings to non-technical stakeholders. Tools like charts, graphs, and dashboards are used to present data in a meaningful way.