Welcome to the official Github repo for UCSD Course NANO281 - "Data Science in Materials Science". Here, you will find the materials for your lectures, labs, as well as other useful resources.
All lecture materials are in the lectures folder.
- slides contains the lecture slides in PDF format.
- slides_tex contains the lecture slides in latex beamer format. This is useful if you want to extract an equation or figure.
- notebooks contains the Jupyter notebooks that we will be using for in-lecture demos.
You will be assessed on three lab sessions. The instructions for all labs are in the labs folder.
All lectures and labs will be conducted in Python 3.9+. Prior to the labs, please do the following:
- Install python on your laptop.
- If you are unfamiliar with Python, please go through the official Python tutorials to understand the basics of variable assignment, if and for loops, etc. While we are not expecting advanced Python knowledge, everything will go much quicker if you are already familiar with the basics.
In preparation for your lectures and labs, please ensure that you have your Python environment setup properly. You only need to do this once. There are alternative approaches to setting up your machine, but the approach outlined here is guaranteed to work and is reproducible. Your instructors would be better able to assist if there are issues in the installation.
- Download the Python 3.9+ version of Miniconda (recommended) or Anaconda for your OS.
- Follow the official instructions and install Miniconda/Anaconda.
- Start the terminal (Mac/Linux) or Anaconda Prompt (Windows).
- Create a virtual environment for NANO281:
conda create --name nano281 python=3.9- Activate the virtual environment.
conda activate nano281- Install the necessary Python libraries.
conda install --yes numpy scipy matplotlib pandas jupyter seaborn scikit-learn
conda install --channel conda-forge --yes pymatgen- An alternative to steps 4-6 is to download the nano281_env.yml file from the Google classroom or Github repo and create the environment directly.
conda env create -f nano281_env.yml- Subsequently, always activate your nano281 environment prior to working on your lectures/labs using:
conda activate nano281If for any reason you find that your conda environment is corrupted and you need to start over, please run:
conda env remove --name nano281
and simply start from step 4 above to redo your setup.
The course is intended to be self-contained and all textbooks are optional. However, the following are useful to have around:
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition Amazon, or get the free online version.
- Python Data Science Handbook. Buy from Amazon or get the free online version.