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An Introduction to the course

Friday, 25 January 2019
We introduce the main themes of the course.


Welcome to A Functional Approach to the Digital Humanities, which we will often refer to as FunDHum. FunDHum provides a workshop-style introduction to computer science grounded in examples from the digital humanities and employing a functional programming approach using the Racket programming language. What does that all mean? Let’s unpack some key terms.

Computer Science

Although you will find that computer scientists don’t always agree on all the details, most agree that, at the core, computer science is the study of algorithms and data structures. That is, computer scientists think about the instructions and processes one might write to accomplish a task and about ways to organize information to support those instructions and processes. In many ways, computer science is the study of problem solving in formal domains: We consider how to make explicit the requirements of a problem, to develop a solution in a formal language (most typically a programming language), and to analyze that solution for both correctness and efficient use of resources. Whether or not you continue your study of computer science beyond this course, I hope that FunDHum will help you think you think more carefully about solving problems.

You will develop your skills in algorithmic thinking in a variety of ways, but most frequently by writing and exploring computer programs in which you represent the algorithms and data structures that you have created. Since you will write programs, we will also explore some issues of software design, including ways to write programs to make them more readable and maintainable by other humans and the use of both experimental testing and formal reasoning to help ensure that the programs you write meet their goals. Since you are writing in a language, albeit an artificial language, we will also consider issues of style and elegance.

To some, “computer programming” is the same as “computer science”. While programming can be part of the work of a computer scientist, it is often only a small part of that work. Some computer scientists study the limits of computing, what is and is not computable. Many explore the design of algorithms and data structures for particular domains, from the details of how to implement algorithms in physical circuitry to the abstract questions of how to represent intelligence. Some consider the implications of computing: Computing allows us to accomplish many tasks that previously seemed impossible, such as gathering and analyzing the purchasing or viewing habits of large segments of society; we have a responsibility to think through the implications of such abilities and, when possible to find ways to avoid broader harm.

Just in case it wasn’t clear: While you will develop a wide variety of computer programs in FunDHum, the primary role of our programming exercises is to help you develop skills at algorithmic thinking.

Digital Humanities

Although one can develop such skills by exploring algorithms abstractly, we will find it much more useful to focus on a particular problem domain. In FunDHum, that problem domain is the relatively new field of Digital Humanities. But what are the Digital Humanities? Perhaps we should start by considering the Humanities themselves. From my perspective, humanists study the works of humanity, including language, philosophy, religion, and art. Of course, scientists and social scientists might also study these areas. However, humanists and scientists approach their study differently. While scientists tend to employ the scientific or experimental method, humanists often attend more to the particular details or broader contexts of individual works, frequently guided by an underlying theory or framework.

What about the digital humanities? Because the field is evolving, the community has not agreed upon a consistent definition. That’s almost certainly a good thing; definitions can limit possibilities, and fields need have room to grow. At the same time, some limits are useful. For the sake of FunDHum, we will consider the aspects of the digital humanities that relate to the ways in which algorithmic thinking and digital tools can contribute to humanistic work, particularly humanistic work involve text written in natural language. I use the term “contribute” carefully; as a computer scientist, I know that computers cannot replace the careful thought and analysis that undergird humanistic study. However, these technologies can reveal new topics and modes of inquiry and provide new modalities for presenting results.

Let’s consider a few examples.

An early form of digital humanities involved using computers to find patterns in moderate-sized or large corpora, such as the works of Shakespeare or a digitized collection of 18th century British novels. This form of analysis is often termed “distant reading” to contrast it with the close reading that is core to many forms of humanistic study. It also suggests the potential benefits of stepping back and looking at a larger group of works. While distant reading incorporates a wide variety of practices, one of the more popular is that of “topic modeling”, the use of computers to find groups of words that tend to appear together. Of course, the topics themselves are not intended to serve as the final “result”. Rather, discovering an unusual grouping of words may lead the scholar to a set of readings which she can then explore more closely through traditional techniques. She may discover insight from the particular grouping of words. She may find the unexpected connections between works leads her in do directions. She may conclude that the relationships appear to be specious.

“Place” plays a large role in many works; Humanists increasingly find that tools which render places on a map can provide a useful mechanism for both exploring and explaining the role of place. Mapping place names in, say, Hamlet, may suggest that although the primary action takes place in one location or set of locations, other locations play enough of a role in the play that they influence the action. Mapping tools need not be applied only to traditional literary texts. For example, Mark Laver at Grinnell College has developed an informative project involving place names in the songs of Kendrick Lamar. In many such situations, the maps may lead the scholar to a closer analysis of the text or texts. At the same time, the maps themselves can serve as a kind of product of scholarly inquiry, providing evidence for a broader argument about the role of place.

Digital maps can also be the primary product of humanistic inquiry. Consider, for example, America’s troublesome separation of immigrant children from their parents in the second decade of the 21st century. While there is power in narratives that explore the experience of a few individuals or a particular community, some digital humanists have found it valuable to develop map-based visualizations of where these children get housed compared to where they arrive.

Workshop-style learning

We have established that FunDHum introduces computer science through problems and approaches drawn from the digital humanities. But how should one teach and learn these materials? Computer science has often been taught in much the same way that the natural sciences are traditionally taught, through lecture and associated lab. However, it is clear that most students learn computer science better by doing rather than by listening. Hence, FunDHum is designed to support a so-called “workshop style” approach. For each class period, students will read a few pages that provide some background information. While, in a few cases, we will rely on articles written by others, most frequently you will read materials written specifically for this work. Class time is then devoted to students working in pairs on a series of problems.

Why pairs? Because research suggests that programmers work better in pairs, in both the classroom and in regular practice. How much better? Studies suggest that two people working together are regularly more productive than two people working separately. It’s not just that “two heads are better than one”; we also know that having to describe a thought process to someone else improves that thought process and helps find flaws.

If you are reading through FunDHum on your own, I suggest that you do your best to follow a similar approach. You won’t learn this material by just reading, so make sure that you work through the various problems. You may even want to design some new problems. If you’re fortunate enough to have a friend who might be interested in these topics, try to work as a pair.

Functional programming in the Racket Programming Language

You will learn methods of algorithmic thinking, in part, by writing programs that represent your algorithms and data structures in a formal language, one that computers can understand. We call such languages “programming languages”. Although Python, PHP, and R are the most popular programming languages for the digital humanities, we will use the Racket programming language.

Why Racket? There are many reasons. Racket, like Python, was designed for both novice and experienced programmers: Racket is simple enough for a beginner but powerful enough for an expert. For example, Racket’s syntax is simpler than that of most other languages, although filled with parentheses. Racket comes with an affiliated program development environment, DrRacket, that is particularly supportive of new programmers. DrRacket makes it easy to develop, to experiment, and to debug. As importantly, Racket encourages particular approaches to algorithm and program design; I think that emphasizing those approaches will make you a better algorithmic thinker and programmer. Among other things, Racket supports a model of programming called “functional programming” which is characterized by an emphasis both on functions as fundamental values and on writing procedures that do not affect the state of the system. Functional approaches are at the heart of many modern programming languages, and I’ve found it useful to introduce them early.

Broad themes

The digital humanities encompass a wide variety of practices and perspectives, even if we narrow our focus to algorithms that work with text. We could not hope to cover all pertinent approaches and algorithms from the digital humanities in a year, let alone in a single semester. That’s okay; my goal is not to teach you everything. Rather, I hope to expose you to enough algorithmic concepts and ways of thinking about humanistic “data” that you can learn new things and, as importantly, design your own algorithms.

In FunDHum, we will emphasize three core aspects of the digital humanities: data representation, authoring of “algorithmic” texts, and, most centrally, algorithms for analyzing and manipulating text.

Data representation

One of the broad problems in the digital humanities is how to represent data electronically to support a broad variety of algorithms. Consider, for example, the markup appropriate for an electronic version of a book. It’s clear that we want to provide the textual content of the book. But an algorithm should be able to easily access the “parts” of that book, not just chapters, but also section headings, authors, and so on and so forth. Scholars of the book might care about the details of the layout, including the positions of line and page breaks or the typeface choices throughout the book. And, while a book may exist in the abstract, we may also want to encode the information associated with a particular printed instance of the book, including the marginalia and the ownership history, if available.

While we will not be able to cover the full range of issues that digital humanists face, we will explore a variety of such issues and practice developing our own schemas for representing information. Along the way, we will examine common formats for representing and annotating textual data, focusing primarily on “markup languages” [25] like HTML and XML and simple file formats for storing additional information.

Authoring algorithmic texts

Your daily experience with the Web tells you that computers have changed how people write, from the abbreviated text of Twitter and SMS messages to the multiply linked documents of Wikipedia and the World Wide Web. We will explore various techniques for building “algorithmically supported” documents, documents that draw some of their content from algorithms, such as algorithms that analyze or reframe other documents.

Textual analysis

Algorithms are core to the study of computer science. Hence, the design of algorithms that we can use to analyze and manipulate texts form the core of the course. These algorithms will, of course, rely on the ways in which we choose to represent data. And they can be used in the service of the documents we author. We will consider seemingly straightforward issues of textual analysis, such as word frequencies, as well as the complexities they raise, such as identifying words with similar stems. We will also explore more complex algorithms, including primary aspects of topic modeling.


It would be irresponsible of us as computer scientists and as humanists if we did not consider the broader context of the work we do. At times, we will consider the implications and potential pitfalls of the algorithms we write and data we represent.

Computer technology is changing the world. Unfortunately, the people who develop computer technology do not necessarily represent a sufficient breadth of perspectives; fewer than 20% of computing professionals self-identify as women and even fewer identify as people of color. Since technology often reflects its designers perspectives and biases, this lack of diversity is troubling, not just because it means that many kinds of technology better support or prioritize some groups (or, as importantly, deprioritize the needs of some groups), but also because of missed opportunities for other approaches and techniques.

One of the kinds of difference that many often miss is that of difference in physical ability. As developers find themselves excited by new technologies and new opportunities, they forget to consider how others might encounter those technologies, particularly those who have limited sight, limited hearing, or limited movement. The digital humanities also find themselves hampered by the lure of new technologies; far too many digital humanities projects are inaccessible to the differently abled, whether they represent highly visual interfaces that cannot be used by the visually impaired, or archives of auditory materials that lack transcripts, or complex user interfaces. Whenever possible, we will ask ourselves whether the approaches we are using are broadly accessible and, if they are not, we will consider ways in which we could make them more accessible.

Looking ahead

There you have it. FunDHum introduces algorithmic thinking through three core approaches to the digital humanities: data representation, algorithmic texts, and textual analysis. In the lessons to come, you will explore the fundamentals of algorithm design, develop your own algorithms, and enhance both your problem-solving skills and your ability to represent things precisely.

Let’s get started.