Numpy and tabular data

• Matthew Carbone (Brookhaven National Laboratory)

**Abstract**: Essentially all of data-driven science uses tabular data, which can best be thought of as collections of individual data points that all share the same features. For example, a parking garage may contain many different cars, and each car can share the same set of features: number of seats, purchase year, etc. Manipulating tabular data is a key skill for any AI or machine learning application. In this tutorial, you will learn the fundamentals of NumPy (the canonical numerical scientific computing package in Python), and how to use it to its utmost efficiency. **GitHub link**: https://github.com/matthewcarbone/AIML-tutorials