Spring 2026 | UC Merced
Welcome!
This repository is your home for CHEM 190/290. You’ll find lecture notebooks, labs, homework templates, and project materials used throughout the semester.
This course is hands-on and project-focused. We’ll learn how to work with chemical data using modern data science tools, with an emphasis on:
- Python & Jupyter Notebooks
- Data analysis and visualization
- Basic statistics and machine learning
- Reading and understanding data-driven chemistry research
No prior machine learning experience is required—just curiosity and willingness to experiment.
If you are seeing this page, then you are most likely running a personal JupyterHub instance for this course in your browser. Every time you launch an instance (i.e. click on the provided link), the remote course directory maintained by the instructors is synced to your personal JupyterHub account - this means you are free to edit any notebooks or other files without worrying about affecting other students. This is the overall file structure for the course home directory
.
├── Lectures/ # Lecture content & demos
├── Homework/ # Homework assignments
├── Projects/ # Mini-projects & final project information
├── Datasets/ # Course datasets (do not modify)
├── Chem290Resources.pdf # Short list of programming / data science resourses from previous year
└── README.md # This file, essentially course home page
If you accidently delete a file, you can simply close and relaunch the JupyterHub instance. Any missing files will automatically be added back to your directory. You shouldn't need to create any files yourself (may change when we get to projects), but if you do they will persist in your user directory even after closing and relaunching the instance.
Behind the scenes, your JupyterHub instance is running on a cloud server hosted by the UC Merced CIRT team - we reccomend students at least briefly read through the Usage Policy the CIRT team has for these instances https://ucm-it.github.io/hpc_docs/docs/jupyter/jupyterhub-policy
All Jupyter Notebooks we will work on in this class are self-contained and ready to edit. Just navigate to the appropiate notebook and begin writing your own code.
All work is done in Python using Jupyter Notebooks. Common packages include:
numpypandasmatplotlibscikit-learn
Everything you need is already available on JupyterHub—no local installation required.
All official announcements, updates, and due dates will be posted on CatCourses.
Check it regularly to stay on track.
- Ask questions during lecture or lab
- Come to office hours
- Reach out on CatCourses if something is unclear