Data Science Flexible

This section covers the basics of SQL for accessing and aggregating data from a large variety of databases. You'll also learn key Python packages for analyzing that data, with a special focus on creating insightful and striking visualizations with seaborn. Lastly this section goes over the design and evaluation of experiments. Specifically we'll cover the essentials of designing a good experiment, from setting up your test and control group to picking what metrics to use to evaluate. We'll also cover hypothesis testing and the ways you can use statistical tests to generate and interpret your results. Finish off with a capstone by proposing your own real world experiment, showing everything it would take to run.

Get to know sci-kit learn, the most widely used modeling and machine learning package in Python. Here we'll focus on situations where we have a knowable and observable outcome. You'll start with some of the classical models of machine learning like decision trees and OLS. Learn techniques for both regression and classification. You'll then move to more and more sophisticated models, with random forest and boosting. By the end you'll present a capstone after going off to gather real data and build your own models using machine learning techniques.

Unsupervised learning doesn't focus on an outcome, but rather finds associations and relationships present in data. You'll start with some classic clustering algorithms like k-means to break almost any data set into groups. You'll then get into cutting edge techniques like neural networks. You'll continue with a discussion of NLP, or natural language processing, which allows you to take unstructured text data and apply the techniques you've learned. To finish you will build a capstone built on top of these tools.

To learn more about our specializations, look below. We have various specializations designed to give you expertise in the domain you want to work in, and we're always adding more so be sure to let us know if there's something you'd like to see.