Data: Data Literacy

Data can be broadly defined as factual information that is systematically recorded and analyzed to answer a question. However, the definitions of data differ depending on the field or discipline. 

There are three main data types:

• Quantitative data is information about quantities; that is, information that can be counted, measured, and expressed using numbers. Because quantitative data is expressed in numbers, it can be analyzed using traditional statistics.
• Qualitative data is information about qualities, typically descriptive and conceptual in nature, and cannot be easily expressed in numbers. Qualitative data does not lend itself to traditional statistical analysis, but instead requires qualitative analysis. Sources of qualitative data include text documents, interview and focus-group transcripts, observations and notes, images, and audio and video recordings.
• Mixed data refers to a dataset that combines quantitative and qualitative data, and therefore needs to be analyzed using mixed (quantitative and qualitative) methods.

In addition, we distinguish three main data types based on the format of the data:

• Structured data (usually categorized as quantitative data) is highly-organized and formatted as a table, so it can be analyzed by a computer. Structured data is found in spreadsheets and relational databases.
• Unstructured data (usually categorized as qualitative data) has no pre-defined format or organization, making it more difficult to collect, process, and analyze with the help of computers (although this may already be changing thanks to the development of machine learning and new analytic tools). Usually researchers need to add structure to the unstructured data to make analyses easier.
• Semi-structured data is in-between structured and unstructured data, i.e., it does not conform to a strict structure but has indicators from which machines can derive meaning. 

Data literacy is the ability to read, work with, analyze, visualize, interpret, argue with, and use data to make decisions and solve problems. 

Data literacy skills are considered to be essential 21st-century skills and an important asset in a range of professions and disciplines. 

Data Glossary

An open-access online resource created by the National Network of Libraries of Medicine (NNLM). It contains definitions, relevant literature, and web resources for common data-related terms. 

Data Fluency: Exploring and Describing Data (LinkedIn Learning)

The course covers the fundamentals of data fluency, or the ability to work with data, with a focus on quantitative data and statistical analyses, as well as using data to generate insights and solve problems. 

Length: 4 hours, 20 minutes. (Free access with Bucknell login.)