Finding Relationships in Data with Python

Course Overview
(Music playing) Hi, my name is Janani Ravi, and welcome to this course on Finding Relationships in Data with Python. A little about myself; I have a master's degree in Electrical Engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high quality video content. Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist must possess these days. Increasingly, different organizations are using the same models and the same modeling tools, so what differs is how those models are applied to the data. Today more than ever it's really important that you know your data well. In this course, you will gain the ability to find relationships within your data that you can explore to construct more complex models. First, you will learn to summarize your data using univariate, bivariate, and multivariate statistics. Next, you will discover how specific forms of visualization have evolved to identify and capture specific types of relationships. You will then learn some advanced tools such as the use of auto-correlation plots and KDE plots that help model probability distributions. Finally, you will round out your knowledge by using four of these libraries, Matplotlib, Seaborn, Altair, and Plotly to find relationships. When you're finished with this course, you will have the skills and knowledge to identify and explore relationships that exist within your data by efficiently exploring and visualizing that data.