When comparing structured and unstructured data, it's important to consider how they are used in data science projects. Structured data is often collected from sources that provide structured output, like databases, APIs, or surveys. Meanwhile, unstructured data is typically collected from sources that provide unstructured output, like web scraping, text mining, or image recognition. For cleaning, structured data needs to be checked for errors, missing values, outliers, or duplicates; while unstructured data should have noise, formatting, or irrelevant information removed. To transform the data types, functions, calculations or aggregations can be applied to structured data; while tokenization, normalization or embedding can be used for unstructured data. Analyzing structured data involves descriptive statistics, inferential statistics or machine learning; whereas natural language processing, computer vision or deep learning are more suitable for analyzing unstructured data. Finally, structured data can be visualized with charts, graphs or dashboards; and unstructured data can be visualized with word clouds, heat maps or network diagrams.