Beginning Data Analysis and Machine Learning with Jupyter Notebooks

In this beginner-friendly workshop you'll see how you can get started with data analytics and data science using Jupyter Notebooks. Matt will start with the basics of notebooks and then move on to using Python, Pandas, and NumPy to perform basic exploratory data analysis. See how you can use Plotly Express to create interactive charts and visuals with only a minimal amount of code. Once you've grasped the basics of understanding and visualizing the data Matt will move on to machine learning with SciKit-Learn as you train and evaluate predictive regression and classification models. The tutorial will cover common concerns in machine learning such as biases in data, the importance of data cleaning and feature engineering, comparing different models, and model drift. Finally, close with an exploration of the myriad of other ways you and your team can use notebooks to conduct repeatable data science experiments, mix together code and markdown, and even extend Jupyter Notebooks to other languages. You'll leave with a deeper set of tools that you can use to analyze data and make predictions with trained machine learning models.