Python

Python, a popular general-purpose programming language, transforms into a powerful environment for scientific computing with a few key libraries: NumPy, pandas, and Matplotlib. This workshop series provides a crash course in Python and its use in scientific computing. Instruction will take place within notebooks, which allow easy experimentation with code.

Lecture 1 - Python Basics

Sunday, June 6, 2021, 1-2 pm

This workshop covers basic data types in Python. We will practice creating containers like lists, dictionaries, sets, and tuples and using them to structure data. The concepts of functions and classes will also be introduced.

Link to Google Colab workspace

Lecture 2 - NumPy, Part 1

Sunday, June 20, 2021, 1-2 pm

This workshop introduces NumPy, the core library for scientific computing in Python. We will learn how to initialize arrays and how to work with them. After initializing NumPy arrays from nested lists, we will practice array indexing and see how NumPy arrays handle data typing.

Link to Google Colab workspace

Lecture 3 - NumPy, Part 2

Sunday, June 27, 2021, 1-2 pm

This workshop covers more advanced analysis with NumPy arrays and shows how to use the library’s documentation. We will work with techniques such as array math and broadcasting, which allows NumPy to perform arithmetic operations with arrays of different shapes.

Link to Google Colab workspace

Lecture 4 - Pandas

Sunday, July 11, 2021, 1-2 pm

This workshop introduces the Pandas library, a comprehensive tool for data manipulation. We will learn how to use Pandas to read in data from .csv files to create dataframes. Then, we will practice essential dataframe operations such as view, delete, append, and several others.

Link to Google Colab workspace

Link to Developer Survey dataset

Lecture 5 - Matplotlib

Sunday, July 25, 2021, 1-2 pm

This workshop introduces the Matplotlib library and basic data visualization in Python. We will learn how to use Matplotlib to create various kinds of plots and subplots. By setting options and label values, we will see how to generate publication-quality images from various data types.

Link to Google Colab workspace

Link to Matplotlib cheat sheet

Advanced Lecture - PyTorch

Tuesday, July 5, 2022, 3-5 pm

This workshop introduces PyTorch, a Python-based scientific computing package that can be used as a NumPy replacement to take advantage of accelerators such as GPUs and as a library to implement neural networks. We will cover the basics of image-recognition algorithms, PyTorch's Tensor library, and how to train a simple neural network to classify images.

Link to PyTorch documentation