Cambridge Digital Humanities course timetable

Itamar Shatz - Methods Fellow CDH

This course will introduce participants to key concepts in statistical analyses, including statistical significance, effect sizes, and linear models. The goal is to give participants the basic tools that they need in order to understand the use of statistical methods by others and to use these methods effectively in their own research. We will focus on an intuitive and practical understanding of statistical analyses, rather than on the mathematical details underlying them. As such, the course will be accessible for those without a quantitative background, although it will help to have knowledge of basic descriptive statistics (e.g., mean and standard deviation).

The course will cover (approximately) the following topics:

• Session 1: statistical significance and statistical tests (including hypothesis testing, p-values, statistical power, t-test, and chi-square test).
• Session 2: effect sizes, correlation, confidence intervals, and outliers.
• Session 3: linear regression (including simple/multiple regression, residuals, beta coefficients, and R-Squared).
• Session 4: linear regression continued (including test statistics, standard errors, centering, interaction, categorical predictors, linear models, and assumption testing).

Jessica M. Parr, PhD (Simmons University and The Programming Historian)

We welcome Jessica Parr as a guest lecturer for this Methods Workshop, where we will discuss mapping techniques for scholars of the transatlantic slave trade. It will open with a discussion of addressing the Eurocentricity of geospatial techniques and the archives. We will then discuss strategies for reading against the archive to locate Black voices and strategies for determining geospatial coordinates from primary sources. Finally, the workshop will conclude with a demonstration of how to create maps in Tableau and some discussion of data ethics.

Please apply for a place if you would like to attend, on registration, you will be asked to complete and submit an information form (which will remain open until 10 am Monday, 14 February 2022), places are limited and selected on a rolling basis, we would suggest early completion.  We will confirm participation week commencing Monday, 21 February 2022.

Thomas Cowhitt, Methods Fellow - Cambridge Digital Humanities

This Methods Fellow's Workshop Series event will introduce users to social network analysis in R. Participants will be asked to generate their own relational dataset. We will then use several R packages to visualize and interpret relational data. By the conclusion of this course, users will be able to construct a relational dataset, load and clean this dataset in R, and generate static network diagrams and reports on descriptive network statistics.

Thomas Cowhitt, Methods Fellow - Cambridge Digital Humanities

This Methods Fellow's Workshop Series event will introduce users to social network analysis in R. Participants will be asked to generate their own relational dataset. We will then use several R packages to visualize and interpret relational data. By the conclusion of this course, users will be able to construct a relational dataset, load and clean this dataset in R, and generate static network diagrams and reports on descriptive network statistics.

Thomas Cowhitt, Methods Fellow - Cambridge Digital Humanities

This Methods Fellow's Workshop Series event will introduce users to social network analysis in R. Participants will be asked to generate their own relational dataset. We will then use several R packages to visualize and interpret relational data. By the conclusion of this course, users will be able to construct a relational dataset, load and clean this dataset in R, and generate static network diagrams and reports on descriptive network statistics.

This CDH Basics session is aimed at researchers who have never done any coding before. We will explore basic principles and approaches to writing and adapting code, using the popular programming language Python as a case study. Participants will also gain familiarity with using Jupyter Notebooks, an open-source web application that allows users to create and share documents containing live code alongside visualisations and narrative text.

Itamar Shatz - Methods Fellow CDH

This course will introduce participants to key concepts in statistical analyses, including statistical significance, effect sizes, and linear models. The goal is to give participants the basic tools that they need in order to understand the use of statistical methods by others and to use these methods effectively in their own research. We will focus on an intuitive and practical understanding of statistical analyses, rather than on the mathematical details underlying them. As such, the course will be accessible for those without a quantitative background, although it will help to have knowledge of basic descriptive statistics (e.g., mean and standard deviation).

The course will cover (approximately) the following topics:

• Session 1: statistical significance and statistical tests (including hypothesis testing, p-values, statistical power, t-test, and chi-square test).
• Session 2: effect sizes, correlation, confidence intervals, and outliers.
• Session 3: linear regression (including simple/multiple regression, residuals, beta coefficients, and R-Squared).
• Session 4: linear regression continued (including test statistics, standard errors, centering, interaction, categorical predictors, linear models, and assumption testing).

This course demystifies principles of data visualisation and practices of graph creation in Python to help trainees better understand and reflect how Good Data Visualisation under “5 Principles” can be achieved, and develop Python’s application in data visualisation beyond analysis. This course is aimed at students/staff who are interested in and/or use data visualisation in research or outreach and hope to explore data visualisation in Python with basic Python knowledge. It is delivered in a format of 4-hr workshop (on Zoom) + c. 2hr self-paced preparation and post-class exercises+ 1hr asynchronous question-shooting, combining theories, case learning, peer interactions and practical: we first present an introduction on key concepts of and problems in data visualisation, before case studies and group discussion on data visualisation principles and how to visualise data better in practices; then under a demonstration, we employ Python to visualise data and go through types of graphs.

Text-mining is extracting information from unstructured text, such as books, newspapers, and manuscript transcriptions. This foundational course is aimed at students and staff new to text-mining. It presents a basic introduction to text-mining principles and methods, with coding examples and exercises in Python. To discuss the process, we will walk through a simple example of collecting, cleaning and analysing a text.

If you are interested in attending this course, please request a place and complete the application form, submitting it by the end of Monday, 7 March 2022. Successful applicants will be notified by the end-of-day Thursday, 10 March 2022. Preparatory materials will be released on Thursday, 17 March 2022. Places will be prioritised for students and staff in the schools of Arts & Humanities, Humanities & Social Sciences, libraries and museums. However, if you study or work in a STEM department and use humanities or social sciences approaches, you are also welcome to apply.

This course looks at how modern computational techniques in logic can be used to approach historical questions in the history of logic while also reflecting on the differences and similarities between historical and modern approaches to logic.

Historically, the course will focus on two authors’ approaches to modal logic, the branch of logic that deals with possibility, necessity, and contingency. Ibn Sina (9th century) and John Buridan (14th century). Using these two authors and their discussions of logic as a starting place, we will look at how their logical systems can be represented and formalised using contemporary computational methods, as well as reflecting on the similarities and differences between historical approaches to analysing validity and its relationship to modern notions of algorithms.

The overarching aim of the course is to develop the framework that allows us to computationally show that Buridan and Ibn Sina are working with the same modal logic under two different presentations.

This course demystifies principles of data visualisation and practices of graph creation in Python to help trainees better understand and reflect how Good Data Visualisation under “5 Principles” can be achieved, and develop Python’s application in data visualisation beyond analysis. This course is aimed at students/staff who are interested in and/or use data visualisation in research or outreach and hope to explore data visualisation in Python with basic Python knowledge. It is delivered in a format of 4-hr workshop (on Zoom) + c. 2hr self-paced preparation and post-class exercises+ 1hr asynchronous question-shooting, combining theories, case learning, peer interactions and practical: we first present an introduction on key concepts of and problems in data visualisation, before case studies and group discussion on data visualisation principles and how to visualise data better in practices; then under a demonstration, we employ Python to visualise data and go through types of graphs.

Text-mining is extracting information from unstructured text, such as books, newspapers, and manuscript transcriptions. This foundational course is aimed at students and staff new to text-mining. It presents a basic introduction to text-mining principles and methods, with coding examples and exercises in Python. To discuss the process, we will walk through a simple example of collecting, cleaning and analysing a text.

If you are interested in attending this course, please request a place and complete the application form, submitting it by the end of Monday, 7 March 2022. Successful applicants will be notified by the end-of-day Thursday, 10 March 2022. Preparatory materials will be released on Thursday, 17 March 2022. Places will be prioritised for students and staff in the schools of Arts & Humanities, Humanities & Social Sciences, libraries and museums. However, if you study or work in a STEM department and use humanities or social sciences approaches, you are also welcome to apply.

This course looks at how modern computational techniques in logic can be used to approach historical questions in the history of logic while also reflecting on the differences and similarities between historical and modern approaches to logic.

Historically, the course will focus on two authors’ approaches to modal logic, the branch of logic that deals with possibility, necessity, and contingency. Ibn Sina (9th century) and John Buridan (14th century). Using these two authors and their discussions of logic as a starting place, we will look at how their logical systems can be represented and formalised using contemporary computational methods, as well as reflecting on the similarities and differences between historical approaches to analysing validity and its relationship to modern notions of algorithms.

The overarching aim of the course is to develop the framework that allows us to computationally show that Buridan and Ibn Sina are working with the same modal logic under two different presentations.

This course looks at how modern computational techniques in logic can be used to approach historical questions in the history of logic while also reflecting on the differences and similarities between historical and modern approaches to logic.

Historically, the course will focus on two authors’ approaches to modal logic, the branch of logic that deals with possibility, necessity, and contingency. Ibn Sina (9th century) and John Buridan (14th century). Using these two authors and their discussions of logic as a starting place, we will look at how their logical systems can be represented and formalised using contemporary computational methods, as well as reflecting on the similarities and differences between historical approaches to analysing validity and its relationship to modern notions of algorithms.

The overarching aim of the course is to develop the framework that allows us to computationally show that Buridan and Ibn Sina are working with the same modal logic under two different presentations.

This course looks at how modern computational techniques in logic can be used to approach historical questions in the history of logic while also reflecting on the differences and similarities between historical and modern approaches to logic.

Historically, the course will focus on two authors’ approaches to modal logic, the branch of logic that deals with possibility, necessity, and contingency. Ibn Sina (9th century) and John Buridan (14th century). Using these two authors and their discussions of logic as a starting place, we will look at how their logical systems can be represented and formalised using contemporary computational methods, as well as reflecting on the similarities and differences between historical approaches to analysing validity and its relationship to modern notions of algorithms.

The overarching aim of the course is to develop the framework that allows us to computationally show that Buridan and Ibn Sina are working with the same modal logic under two different presentations.

Do you need a database for your data? Or could you store the data in standalone files? Which database paradigm should you consider? What are the consequences of these choices on your work routine? How to navigate all of this with minimal or no programming experience?

These and more are the questions we will address in the course. We aim to provide a gentle introduction to databases and database paradigms, with examples that help explain the differences between the most common database packages and guide researchers to design suitable solutions for their data problems.

Do you need a database for your data? Or could you store the data in standalone files? Which database paradigm should you consider? What are the consequences of these choices on your work routine? How to navigate all of this with minimal or no programming experience?

These and more are the questions we will address in the course. We aim to provide a gentle introduction to databases and database paradigms, with examples that help explain the differences between the most common database packages and guide researchers to design suitable solutions for their data problems.

Do you need a database for your data? Or could you store the data in standalone files? Which database paradigm should you consider? What are the consequences of these choices on your work routine? How to navigate all of this with minimal or no programming experience?

These and more are the questions we will address in the course. We aim to provide a gentle introduction to databases and database paradigms, with examples that help explain the differences between the most common database packages and guide researchers to design suitable solutions for their data problems.

Do you need a database for your data? Or could you store the data in standalone files? Which database paradigm should you consider? What are the consequences of these choices on your work routine? How to navigate all of this with minimal or no programming experience?

These and more are the questions we will address in the course. We aim to provide a gentle introduction to databases and database paradigms, with examples that help explain the differences between the most common database packages and guide researchers to design suitable solutions for their data problems.

If you've ever collected some data but weren't sure how to go about visualising it in a way that could help you uncover new insights, or if you've struggled to present data in a way that helped others understand your findings, this course is intended for you.

We'll talk about how to select the right visualisation for your data, discuss the pros and cons of different approaches, and get hands-on experience displaying information in clear and compelling ways. We'll also discuss broader issues surrounding visualisation science, such as common ways that visualisations are misinterpreted and how to avoid them, and controversies around what counts as best practice in visual communication.

In addition to the weekly online sessions, participants are expected to spend around two hours per week applying the skills learnt to gain greater fluency and enable us to 'workshop' each other's visualisations.

Your participation will also benefit if you have the chance to take our "Give me 5! Principles of Data Visualisation", which is scheduled for 23rd & 30th March. However, attending this workshop is not a prerequisite, so please do not be deterred if you miss the dates.