Course sequence: Data analytics for the liberal arts

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I’m a proud liberal arts graduate myself who, with some fumbling, ended up in the world of data analytics. It may sound odd, but I never fancied myself much of a “math person,” and I still love to explore the world through the arts and qualitative methods.

I still hold that it’s because of these interests that I decided to dig in on data analytics: I wanted to be as efficient, productive and automated as possible in using quantitative data, so that I had the time and energy to explore it qualitatively.

This distinction holds a truth that we can use to unite data analytics with the liberal arts: computers are good at processing data, and humans are good at making sense of it.

The good news about computers is that they do what you tell them to do. The bad news is that they do what you tell them to do.

Ted Nelson

Why the liberal arts in an age of big data and technology? The liberal arts teach you to have a perspective and make sense of the world. As I said in my post on creating the future with the liberal arts, “The liberal arts student appreciates the grit, fragility and triumph of the human condition. We can only see where we are going if we know where we’ve been.”

Humans may have a gift to contextualize large amounts of long-term information, but we are terrible at holding short-term memory. For example, most of us can only hold seven things at a time in working memory.

 (If you don’t believe me at how bad we are at holding short-term memory, take this test.)

Computers, on the other hand, thrive at executing orders that require processing lots of defined chunks of data. Here is where computational thinking and data literacy holds so much promise for liberal arts students.

Here is a proposed basic two-semester course sequence that I proposed to a liberal arts professor friend of mine. The professor had mentioned the typical scenario of liberal arts grads being ill-prepared for the job market, and that the school was looking for ways to position their students better for careers success.

I know this scenario well — I was one myself, but I’ve come not to regret receiving my education in the first place. Instead, I’ve looked for ways to integrate data analytics education with liberal arts education, specifically so that liberal arts students, who have so much value to provide employers, are seen in a compelling light.

To do so, I’ve integrated a coding bootcamp-like experience to fit a three-credit-hour college course. Students will learn how to build spreadsheet models, read information from databases, and script data collection and analysis processes. These skills will make liberal arts graduates more competitive on the job market.

My second course is geared toward liberal arts students who wish to pursue graduate school and careers in academia. These students can benefit from computational processes as they are increasingly being used in those fields. The course is heavy on text analysis and natural language processing — again, to provide a method for comparing many bits of short-term memory at scale.

I hope the below learning guide can benefit your liberal arts organization. Universities must adapt, but I do not believe the best way to do so is by abandoning liberal arts programs. There is a way to augment this field of study with computational and data analytics methods.

Data analytics for liberal arts PDF

Data analyics for the liberal arts

I imagine this as a two-semester sequence for upperclassmen. The first course, “Intro to Data Analytics” is more of a general survey of data analytics with an emphasis on applications in the workforce. The second, “Data Analytics for the Liberal Arts” is geared toward students interested in graduate school in the humanities or similar. However, these topics would be of use to others as natural language processing has become popular in industry.

Intro to Data Analytics

This is a full-semester, 3-credit hour course. I consider this as a sort of “data analytics bootcamp” conducted as a typical semester-long college course.

Learning outcomes:

  • Student can use multiple data analytics technologies in building a data analysis project
  • Student can create visualizations and other data-backed assets using information design principles
  • Student can conduct compelling presentations based on data analysis


  • What is data analytics? Why is it so popular? Should it be so popular? What can liberal arts students bring to the table? (This can be a bit contrarian and liberal arts triumphalist.)
  • Basic spreadsheet modeling: be able to build a break-even pricing model or a staffing model. Almost anyone in business will have to build a budget at some point and it will probably be in Excel.
  • Databases: Understand the basic architecture that powers modern business and how it’s changed in the last 15 years. Be able to write basic commands in structured query language (SQL) to read from databases. Understand the difference between relational and non-relational databases, structured and unstructured data.
  • Information design: Much of this is based on cognitive science. Learn when to use which chart versus tables, etc. How to design effective slide decks, visualizations and dashboards and use these assets to present sound data-backed presentations and recommendations. Pairs nicely with the liberal arts emphasis on sound communication.
  • Coding: Perform basic task automation and data analysis with R.


  • Build a break-even model for an ecommerce store. Present findings to management.
  • Build an interactive dashboard investigating sales performance over time
  • Perform basic data exploration and analysis of a relational database
  • Conduct reproducible data analysis exercise and presentation using a series of data analytics tools

Data Analytics for the Liberal Arts

This is a full-semester, 3-credit hour course. The text could be Text Analysis with R for Student of Literature with some supplemental material. It may be wise to make “Intro to Data Analytics” a pre-req along with a basic stats course.

This course will be conducted entirely in R. The “target demo” would be students interested in grad school for the humanities or related, but the appeal could easily extend as natural language processing is becoming quite popular in industry.

Learning outcomes:

  • Student can conduct exploratory data analysis of unstructured data sets
  • Student can build reproducible data analysis projects in line with the CRISP-DM or similar methodology
  • Student can create compelling visualizations to aid in their findings


  • Advantages and disadvantages of data analytics in the liberal arts
  • Basic exploratory data analysis: describing, summarizing, visualizing one and multiple variables
  • Unstructured data collection: APIs, web scraping
  • What makes data “tidy” and why it matters
  • Structured vs unstructured data
  • Word frequency analysis
  • Document similarity
  • Document clustering & classification
  • Topic modeling
  • Network analysis


  • Collect data from an API or the Web, explore and prepare it for quantitative analysis
  • Visualize word frequencies and occurrences across the corpus of one or more authors
  • Build a model to classify documents based on predicted original author
  • Conduct network analysis on historical or literary figures 

If helping your undergraduate liberal arts population succeed in their careers and in grad school through data analytics is something you’re wrestling with, please set up a free call with me and we can talk.

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