Tools for Data Analysis

In addition to providing the raw data to our campus community, I think the library can take a leadership role in providing the tools and expertise to mine this data into something usable and useful. However, many of the tools that are used to transform data are highly specialized and have a pretty steep learning curve. So I’m going to work to provide an overview of the tools available and focus on those that would be useful in the context of undergraduate education. Continue reading “Tools for Data Analysis”

2017-02-132017-02-13

Chicago Data for Undergraduate Research

We are talking a lot about data, data literacy, and how North Park University can use Chicago data in the classroom. There are already a lot of courses using data in instruction and research so part of my work is figuring out what is already happening. Continue reading “Chicago Data for Undergraduate Research”

2017-01-182017-02-13

Interlibrary Loan Data Analysis and Visualization

I’m working on improving the interlibrary loan services at North Park as well as improving my skills in statistics and data visualization. I’ve combined these two interests to look at analyzing and visualizing our interlibrary loan data using Tableau. Continue reading “Interlibrary Loan Data Analysis and Visualization”

2014-04-112014-11-18

Data Mining and OPAC Usage Data

I’m working with someone in our IT department to look at our consortial OPAC usage data. This is really at the edge of my abilities so I’ll be rather blindly documenting that process here – hopefully it will be interesting and helpful to someone but I’m certainly not an expert in this area – at least not yet!

Tentative Process

Download logs from CARLI.
Creator Decoder.
Design database
Write parser thing
Do basic analysis in MySQL.
Do more advanced analysis in Weka.

We are going to use the program Weka to do this bit of data mining and I’ve installed this on my local machine. It was pretty easy to install and download – so that was nice. Here is the link: http://www.cs.waikato.ac.nz/ml/index.html

Research Questions

These are the basic questions that are currently guiding my database design:

Basic numbers for the searches – how many?
How does VuFind compare to Classic Search?
Types of searching (title, keyword, author)
Search terms – what are the most popular? What are common misspellings?
Platform and OS?
What formats/filters/facets are applied?
Time of day?
Mobile vs. Desktop?
Look at Curriculum Center headings (start with a heading and see if it is used).
Number of search terms (how many terms in a kw search, for example)
Spelling Errors
Failed searches (no results)
Clicked vs. type subjects (i.e. what happens when someone clicks a subject heading?)