Finding Relationships in Data with Python

This course covers the important techniques of exploring data in order to find relationships between variables, including techniques to summarize and describe your data, and several powerful visualization tools to express relationships in that data.
Course info
Rating
(24)
Level
Beginner
Updated
Oct 29, 2019
Duration
2h 3m
Table of contents
Description
Course info
Rating
(24)
Level
Beginner
Updated
Oct 29, 2019
Duration
2h 3m
Description

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 is really important that you know your data well.

In this course, Finding Relationships in Data with Python you will gain the ability to find relationships within your data that you can exploit 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 autocorrelation 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 exploit relationships that exist within your data, by efficiently exploring and visualizing that data.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

More from the author
More courses by Janani Ravi
Section Introduction Transcripts
Section Introduction Transcripts

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.