This session will cover four cutting-edge projects in rapid succession. Find out what is new and upcoming in open source scientific computing!
Does your data science team struggle with operationalizing models, building shared infrastructure or on-boarding new members? This talk for you! We will walk through our solution to these issues with our new open source machine learning framework, Primrose. Primrose has empowered us to go “beyond-the-models”, facilitate the rapid growth of our team, and increase our project velocities.
As professional data scientists and engineers, it is our responsibility to write reproducible, quality code. We show how anyone can use Kedro to build production-ready pipelines from day one—no experience necessary.
Real-world data is messy and missing, yet most statistical models require it to be clean and complete. Analysts are often well versed in modeling, but few are familiar with handling missingness. This talk teaches data professionals best practices for dealing with missingness and introduces Autoimpute, our Python package that helps users grapple with missing data during statistical analysis.
Traditional time series analysis techniques (i.e., visualization, statistics, ARIMA, machine learning, deep learning) have found success in a variety of time series data mining tasks. This presentation introduces a new Python package called STUMPY that offers a simple and intuitive approach for analyzing and understanding time series data.