This session will provide a clear overview of the data science process. We will discuss how to take an identified problem or idea and frame it as an effective question that will guide the project. We will then talk about acquiring, pre-processing, and exploring the data. Next, we'll consider how to model the data in a way that enables us to answer the proposed question, and how to choose the best from among several effective algorithms. Finally, we will discuss the important skill of data storytelling in communicating the insights and predictions of the analysis in a compelling way. The goal is to enable attendees to understand the workflow of solving a data problem.

This session will provide a primer on machine learning, a high-level non-intimidating overview. We’re going to demystify machine learning and its use. We’ll look at how learning occurs and how deep learning is different. We will review 6-8 common algorithms such that you get a good intuition about each one. (e.g., Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbor, K-Means, Neural Networks, Deep Learning). There will be very little math and no coding. The goal is for everyone to become conversant with machine learning.