If you are unsure how to get started or have any questions along the way, reach out to one of the data librarians.  They are happy to help! 

Maggie Marchant (Social Sciences)

Paul Robbins (STEM)

Adam Griggs (Humanities)

At the core, data are observations that can be interpreted.  They can come in many forms and be about many topics, such as the natural world (science), society (social sciences), or art and literature (humanities).

Build data skills through hands-on practice.  The National Student Data Corps provides projects to practice data skills described in this guide.  Choose a project to work on here.

Many questions can be answered using existing data that is made available by the government, researchers, or other organizations.

Sometimes the data you need is not readily available, and you will need to gather it yourself.  This can be as simple as using an app to record your average sleep or as complex as developing an experiment to test medical treatments.

It is important to learn to assess, interpret, and work with data for two main reasons.

1. Data is frequently used to back up opinions and arguments.  In order to assess the validity of news and research you need to ask yourself:  what form is the data in; who collected it; how did they collect it; how is it interpreted; is it openly available so you can do your own analysis?
2. In order to answer questions, whether for an assignment or personal interest, you will likely need to understand how to find, analyze, interpret, manage, and store data. This can be as personal as keeping track of information and records for your own family history or as technical as recording complex chemical experiments or financial transactions.

Do you need a number or statistic to back up a claim?  Check out Sources for Statistics to find ready-to-use data points that answer specific questions (e.g. What is the average daily social media use in the United States?).

Do you need data to analyze for a project?  Check out Sources for Datasets to find multi-variable datasets to download, analyze, and determine the answers to many questions such as trends, correlations, and statistical significance.  

Data come in two main types, quantitative and qualitative. 

Quantitative data consists of numbers and is often structured with specific variables in spreadsheet format. For example:

• census information
• experiment results
• geographic locations
• survey responses (numerical or multiple choice)
• usage statistics

Qualitative data is nonnumerical and typically less structured.  It includes text, images, sound, and video.  For example:

• audio music files
• interview transcripts
• photographs
• survey responses (open ended)
• video footage