Introducing Data Analysis
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Understanding data analysis begins with a simple but powerful idea about how knowledge is built from information.
The DIKW model — standing for Data, Information, Knowledge, and Wisdom — describes this transformation as a gradual process of refinement.
At the base lies data, the raw material of facts, numbers, or observations.When organized and given context, data becomes information.
Through interpretation and connection, information develops into knowledge. Finally, wisdom represents the ability to apply that knowledge with understanding and judgment. This conceptual hierarchy, discussed in works such as Frické (2019), reminds us that data alone does not provide meaning; analysis is what turns it into insight.
Data analysis is the process that bridges these levels. It transforms unstructured or fragmented data into information and knowledge that can be interpreted and applied. In practice, this involves collecting, cleaning, exploring, and interpreting data to reveal patterns, relationships, or trends.
Within the Dynamic Humanities, such analysis connects computation to culture — viewing data not as detached numbers, but as traces of human thought, creativity, and behavior. Data analysis is both technical and interpretive. It relies on computational tools to handle large or complex materials, while human reasoning provides context and meaning to the results. Python serves as a bridge between these two sides, combining the precision of mathematics with the depth of humanistic inquiry.
The chapters that follow introduce the core Python tools that make this work possible — NumPy, Pandas, and Matplotlib — and explore how the Data Science Lifecycle, data types, and workflows structure the practice of analysis in the Dynamic Humanities.
The hierarchy is a teaching analogy, not an established law of reasoning. Frické reviews substantial criticism of it; collecting more data does not automatically create knowledge or sound judgment. In this book, definitions, validation and explanation make that interpretive work explicit.
Bibliography
Frické, M. (2019). The Knowledge Pyramid: the DIKW Hierarchy. Knowledge Organization, 46(1), 33–46. https://doi.org/10.5771/0943-7444-2019-1-33