Host institution: Department of Chemistry and Biochemistry,
University of South Carolina, Columbia, SC, USA

Organizers: Michael L. Myrick

Lecturers: R. Patrick Xian (remote), Santosh Adhikari, Sourin Dey

Acknowledgements: Lee Hallman, Christopher A. Sutton

Day 1 – August 3rd

Lecture topics

Python basics, programming environment, software repositories

• the Anaconda distribution of Python (https://www.anaconda.com/products/distribution)
• “Hello world!” script
• introduction to Jupyter (https://jupyter.org/)
• repositories: GitHub (https://github.com/), PyPI (https://pypi.org/)
• core data structures (numeric, string, list, tuple, dictionary) and their usage
• composite data structures (named tuple, ordered dictionary)
• control flow
• errors and their handling
• basic file handling

Lecture materials

Day 1 folder | Download link

• Teaching notebooks: Python basics and Jupyter (optional material)
• Exercise notebooks: Exercise_01 and its solution

References

1. How to Think Like a Computer Scientist
2. Software carpentry Python fundamentals
3. Awesome Python, curated materials

Day 2 – August 4th

Lecture topics

Functional programming and data visualization with Python.
Bring your own data (BYOD), if possible!

• functional programming in Python
• matplotlib (https://matplotlib.org/)
• seaborn (https://seaborn.pydata.org/)

Lecture materials

Day 2 folder | Download link

• Teaching notebooks: Functional programming and data visualization
• Exercise notebooks: Exercise_02 and its solution

References

1. Python functional programming tutorial
2. David Mertz, Functional Programming in Python, O’Reilly (2016)
3. John D. Hunter, Matplotlib: A 2D graphics environment, Computing in Science and Engineering (2007)
4. Nicolas R. Rougier, Scientific visualization book (2021)
5. Matplotlib tutorial
6. Michael L. Waskom et al., seaborn: statistical data visualization, Journal of Open Source Software (2021)
7. Seaborn tutorial

Day 3 – August 5th

Lecture topics

Scientific and numeric Python software packages

• numpy (https://numpy.org/)
• pandas (https://pandas.pydata.org/)
• operator (https://docs.python.org/3/library/operator.html)
• scipy (https://scipy.org/)

Advanced programming in Python

• some useful built-in functions
• introduction to object-oriented programming (OOP) in Python (https://realpython.com/python3-object-oriented-programming/)
• layout of a Python program, including the import statements

Lecture materials

Day 3 folder | Download link

• Teaching notebooks: Advanced programming in Python, scientific Python, and tabular data processing
• Exercise notebooks: Exercise_03 and its solution

References

1. Charles R. Harris et al., Array programming with NumPy, Nature (2020)
2. Numpy tutorial
3. Pauli Virtanen et al., SciPy 1.0: fundamental algorithms for scientific computing in Python, Nature Methods (2020)
4. Wes McKenney, Data Structures for Statistical Computing in Python, Proceedings of the 9th Python in Science Conference (2009)
5. Wes McKenney’s online book Pandas for Data Analysis O’Reilly (3ed 2022)
6. ODSC pandas workshop tutorials
7. Scipy tutorial by phoenixNAP
8. Python operator module tutorial
9. A somewhat pedantic intro to OOP in Python from Microsoft
10. Advanced concepts for OOP in Python
11. Brandon Rhodes, Python design patterns

Day 4 – August 8th

Lecture topics

Common machine learning frameworks in Python

• scikit-learn (https://scikit-learn.org/)
• pytorch (https://pytorch.org/)

Lecture materials

Day 4 folder | Download link

• Teaching notebooks: introduction to machine learning with scikit-learn, pytorch and deep learning

References

1. Fabian Pedregosa et al., Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research (2011)
2. Scikit-learn official tutorials
3. Andreas Mueller’s scikit-learn tutorial
4. Adam Paszke et al., PyTorch: an imperative style, high-performance deep learning library, NeurIPS (2019)
5. Pytorch tutorials
6. Awesome python machine learning, curated materials

Day 5 – August 9th

Lecture topics

Python packages for molecules and materials, focus on basic data structures and functionalities.

• mendeleev (https://github.com/lmmentel/mendeleev)
• chemical name conversion using cirpy (https://github.com/mcs07/CIRpy) and pubchempy (https://github.com/mcs07/PubChemPy)
• RDkit (https://www.rdkit.org/) and molecular visualization
• pymatgen (https://pymatgen.org/)
• ASE (https://wiki.fysik.dtu.dk/ase/)
• molecular visualization

Lecture materials

Day 5 folder | Download link

• Teaching notebooks: RDKit and chemistry and materials packages

References

1. RDKit tutorials
2. RDKit cookbook
3. Alexandre Varnek eds., Tutorials in Chemoinformatics, Wiley (2017)
4. Daniel S. Wigh et al., A review of molecular representation in the age of machine learning, WIREs Computational Molecular Science (2022)
5. Shyue Ping Ong et al., Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis, Computational Materials Science (2013)
6. Pymatgen tutorial videos on its Youtube channel
7. Ask Hjorth Larsen et al., The atomic simulation environment—a Python library for working with atoms, Journal of Physics: Condensed Matter (2017)
8. ASE tutorials