Course Catalog: https://classes.berkeley.edu/content/2026-summer-dighum-101-001-lec-001
Offered Through: UC Berkeley D-Lab
Instructor: Matthew Kollmer
Dates: May 26 – July 2, 2026 - Mondays, Tuesdays, Wednesdays, Thursdays
Time: 1:00 - 3:00 PM (Pacific Time)
Office Hours: Mondays and Tuesdays, 3:00 - 4:00 PM or by appointment on Zoom
Prerequisites: None
Requirements: Computer, Zoom account, and Internet connection
The digital and data revolution has begun to transform the study of the humanities by introducing new archival data sources, tools and methods, and modes of analysis. In this applied course, students will learn foundational knowledge of Python, the leading programming language in Digital Humanities and data science. By the end of this course, students will be able to program Python in Jupyter Notebooks, analyze and visualize data for purposes of computational text analysis and gain a fundamental understanding of machine learning.
The goal of this course is to teach you basic principles for conducting professional research in the Digital Humanities. You will learn how to program Python in Jupyter Notebooks to access, explore, visualize, and analyze data in humanities contexts. You are strongly encouraged to concurrently enroll in DIGHUM 100 – Theory and Methods in the Digital Humanities to complement this experience with a strong theoretical foundation. By the end of this course, you will learn:
- A variety of Pythonic approaches to explore questions in the humanities;
- How to understand data more holistically, its generative process and lifecycle;
- Strategies for organizing research projects based on your interests;
- Methods for data acquisition and visualization, computational text analysis, and machine learning;
- The importance of developing a critical lens in your field of study.
This course will meet four days a week on Zoom. Each class is divided into two types:
- Lecture days: The instructor will lecture on core concepts in Python and the digital humanities and provide coding walkthroughs in Jupyter notebooks. Most lecture days will also include coding exercises and time for questions and discussion.
- Project days: These days will be exclusively focused on your individual and group projects. They are structured work time with the instructor(s) available to answer questions or concerns.
Please attempt these setup steps before the first day of class. We’ll go over the setup process together, but if you attempt setup before the first day it will be easier to identify any issues in the process.
We will learn to use the programming language Python. Just as you use a text editor like Microsoft Word for writing, you'll need a code editor to write and manage your code. In our case, we’ll be using Visual Studio Code (VS Code), a user-friendly and free editor designed for programming.
To install and manage additional tools or libraries (also called packages) that extend Python’s functionality, we will use a package manager–specifically, Conda. Conda allows us to install, update, and organize the various packages we’ll need throughout the course.
Finally, we will be using GitHub, a platform for sharing and collaborating on code. GitHub allows you to store your code in the cloud, track changes over time, and work with others on shared projects, similar to Google Docs.
Please follow the steps below to install the required software:
- Go to the Anaconda Installer: https://www.anaconda.com/download/success
- Navigate to your device type download (Mac or Windows)
- Download Miniconda
- You could download Anaconda Distribution instead, but it shouldn’t be necessary for this class. For more info about the differences, see here: https://www.anaconda.com/docs/getting-started/concepts/anaconda-or-miniconda
- If you’re getting an error message that reads something like “you can’t download to this device”, try going to https://repo.anaconda.com/miniconda/. Then choose the file that matches your type of device and also ends with “.pkg”.
- Click through the installation setup
- Go to https://github.com/
- Click “sign up” in the top right corner
- Create your account
- Visit VS Code: https://code.visualstudio.com/
- Click “download” for your device
- Follow the download directions
- Open VS Code and select “Continue with GitHub”
- Sign into VS Code with your GitHub account
- In VS Code, look to the menu bar and select “Terminal” then “New Terminal”
- From the Terminal, copy and paste this line of text:
git clone https://github.com/dlab-berkeley/DIGHUM101-2026.git- Then press enter. This should download a folder called DIGHUM101-2026 onto your computer. In VS Code, open that folder before continuing.
- Note for Mac users: the first time you use Git, your computer may ask you to install “Command Line Developer Tools.” Click “install” and wait for the installation to finish. This gives your computer access to the git command needed to clone the course repository.
- Note for PC users: if you get a response that says something like "The term 'git' is not recognized...", you need to download Git on your laptop, too. Go to Git Microsoft download and download Git for Windows. If prompted during installation, make sure you select any options that read something like this: "Git from the command line and also from 3rd-party software". Then try the "Git clone" line again.
- At this point, some of you may be all set up. Hooray! You can test if you’re set up by opening Week 1, opening file 1_1_setup.ipynb and running its code.
- If you can’t run 1_1_setup.ipynb, go back to the Terminal. Type this line of text:
cd DIGHUM101-2026- Then hit enter and then type this line of text:
conda env create -f environment.yml- Then hit enter. This will tell Anaconda to build your Python environment based on the directions in the environment.yml file (part of the course download).
- Once downloaded, type
conda activate dighum101in the Terminal. Then test if you’re all set up with 1_1_setup.ipynb.
A final note on this setup process: if you’re following along this way, you can easily download the latest course materials by simply typing:
git pullinto the Terminal. This will download any new stuff as we move through the course.
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Attendance and Participation: 10%
-
Individual Project
- Topic Statement: 10%
- Demonstration of Code: 20%
- Project Showcase: 10%
-
Group Project
- Topic Statement: 10%
- Short Paper: 20%
- Presentation: 10%
-
Final Video Reflection: 10%
Grading is straight scale: A=90-100; B=80-89; C=70-79; D=60-69; F<60
This course is synchronous and attendance is required. Attendance will be tracked through Zoom sign-ins. Please be present during our Zoom meetings. Come prepared to listen, ask questions, make comments, and attempt coding exercises.
Attendance makes up 10% of your overall grade.
The individual coding project is an opportunity to become more comfortable with Python. The scale and degree of individual coding projects will vary. If you already have some programming experience, you may want to develop a larger, more intensive project. If you’re new to coding, your project may borrow code and ideas from class and apply them to different data or variables. In either case, the goal is simply to practice your coding skills and develop greater familiarity with Python.
It is completely fine if parts of your initial plans for the individual coding project prove more difficult or complicated than you anticipated. While you will need to submit executable code as part of the project, it is also important to document and describe what didn’t work. What proved too time-consuming? What parts of coding in Python were challenging? These details should be included in your submissions alongside your final code versions.
The Individual Coding Project is worth 40% of your overall grade. It is split into three parts: the Topic Statement, the Demonstration of Code, and the Project Showcase. For more info, see the Individual Project Directions.
The group project allows you to survey a Python-dependent method in the digital humanities and reflect on its broader contributions to the field. The group project also invites you to think about the collaborative nature of the digital humanities–a field where researchers often work in teams or labs and where the work depends on layers of interdependent technologies and resources. There is no coding required for the group project. Instead, you will be studying related papers or projects that use Python to enact the method of your choosing.
The Group Project is worth 40% of your overall grade. It is split into three parts: the Topic Statement, the Short Paper, and the Presentation. For more info, see the Group Project Directions.
At the end of the course, you will submit a brief video (3-5 minutes) reflecting on your experiences and taking inventory of your personal development as a programmer with Python. This video reflection is a simple way to describe what you’ve learned and how you may or may not pursue Python in the digital humanities moving forward.
The Final Video Reflection is worth 10% of your overall grade.
Large Language Models (LLMs) are creating dynamic, wide-ranging impacts for programmers and researchers. In just the past year, several frontier models have gained high capacity to generate functional code. We will be discussing these developments and LLMs in class. Using LLMs is not strictly prohibited, but there are three important considerations to bear in mind:
- Responsibility: The responsibility for all submitted work, whether text or code, lies with you. You should be ready to demonstrate your understanding of it and ensure that you have verified its accuracy.
- Honesty: Honesty regarding your use of these tools is of utmost importance. Submitting automatically generated code or text without appropriate citation will be regarded as plagiarism.
- Originality: While leveraging AI tools for assistance is permissible, the work you submit should predominantly reflect your original ideas, code, and language. Assignments that are in large part copied from an LLM are unlikely to be evaluated well.
In other words, there are permissible uses of AI in this class. It is acceptable to converse with LLMs about coding exercises or coding problems you're having trouble with. It is acceptable to use code generated by LLMs but you must cite your LLM usage and have a complete understanding of any code you submit. The majority of code you submit should also be written by you. Ignoring this policy would result in plagiarism, a serious offense that will be treated accordingly.
Most importantly, though, please remember that this class is meant to help you develop a working knowledge of Python in DH contexts. If you're relying on LLMs for every difficult part in the learning process, you need to pause and reflect. Why are you avoiding the challenging aspects of learning? What is it about the difficult parts that make you feel stuck? No matter how useful LLMs are or become, you'll never be able to code effectively unless you work through the difficult aspects of coding. Please be mindful about your learning processes and how LLM usage could be hindering them.
| Week | Date | Topics |
|---|---|---|
| 1 | May 25 | NO CLASS – Memorial Day |
| 1 | May 26 | Syllabus, major assignments, setup, getting started |
| 1 | May 27 | Vocabulary, data types & structures, reading errors, libraries |
| 1 | May 28 | Pandas intro, Project workday (group assignments, brainstorming topics) |
| 2 | June 1 | Functions, conditionals, iterations |
| 2 | June 2 | Pandas overview |
| 2 | June 3 | Text analysis, regular expressions, preprocessing, tokenization, word frequency analysis |
| 2 | June 4 | Project workday: choosing methods, identifying datasets |
| 3 | June 8 | Text analysis continued, n-grams, pos-tagging, TF-IDF |
| 3 | June 9 | Data visualization |
| 3 | June 10 | Web scraping, APIs |
| 3 | June 11 | Project workday: group project collaboration |
| 4 | June 15 | Web scraping, APIs continued |
| 4 | June 16 | Network Analysis, Geospatial data |
| 4 | June 17 | Machine learning intro, topic modelling, text classification |
| 4 | June 18 | Project workday: individual project time |
| 5 | June 22 | Machine learning continued, sentiment analysis, NER, word embeddings |
| 5 | June 23 | Machine learning continued, Transformers, BERT |
| 5 | June 24 | Transformers, LLMs, coding with LLMs |
| 5 | June 25 | Project workday: group project collaboration, individual project time |
| 6 | June 29 | Asynchronous – individual project showcase |
| 6 | June 30 | Asynchronous – individual project showcase feedback |
| 6 | July 1 | Group presentations |
| 6 | July 2 | Group presentations |
| Week | Assignments Due |
|---|---|
| 1 | None |
| 2 | Individual Project: Topic Statement Due Friday, June 5 at 11:59pm |
| 3 | Group Project: Topic Statement Due Friday, June 12 at 11:59pm |
| 4 | None |
| 5 | Individual Project: Demonstration of Code Due Sunday, June 28 at 11:59pm |
| 6 | Individual Project: Recorded Showcase Due Monday, June 29 at 12:00pm Individual Project: Showcase Feedback Due Tuesday, June 30 at 11:59pm Group Project: Presentation Due Wednesday, July 1 at 1:00pm Group Project: Short Paper Due Friday, July 3 at 11:59pm Final Video Reflection Due Friday, July 3 at 11:59pm |
Parts of these materials were created by Evan Muzzall, 2018-2021, Brooks Jessup, 2021, Tom van Nuenen, 2022, Prashant Sharma 2023-2024, Merve Tekgurler 2025.
- https://github.com/dlab-berkeley/DIGHUM101-2025
- https://github.com/dlab-berkeley/DIGHUM101-2024
- https://github.com/dlab-berkeley/DIGHUM101-2023
- https://github.com/dlab-berkeley/DIGHUM101-2022
- https://github.com/dlab-berkeley/DIGHUM101-2021
- https://github.com/dlab-berkeley/DIGHUM101-2020
- https://github.com/dlab-berkeley/DIGHUM101-2019
- https://github.com/dlab-berkeley/DIGHUM101-2018
Course materials have also made use of the following repositories and resources:
- https://github.com/dlab-berkeley/Python-Fundamentals
- https://github.com/dlab-berkeley/Python-Data-Wrangling/
- https://github.com/dlab-berkeley/Python-Web-APIs
- https://github.com/cs124/pa2-naive-bayes/blob/main/pa2.ipynb
- https://github.com/cs124/pa2-naive-bayes/blob/main/numpy_tutorial.ipynb
- https://web.stanford.edu/class/cs124/lec/Lab1_UnixText_2025_upload.pdf
- https://github.com/cs124/labs/blob/main/Lab3_InformationRetrieval.md
- https://github.com/dlab-berkeley/Python-Intermediate-Legacy
- https://github.com/dlab-berkeley/Python-Data-Visualization