Data Visualisation with Python: Seaborn and Scatter Plots

Offered byFutureLearn

Data Visualisation with Python: Seaborn and Scatter Plots
at
FutureLearn
Overview

Duration	4 weeks
Total fee	₹900
Mode of learning	Online
Official Website	Go to Website
Credential	Certificate

Data Visualisation with Python: Seaborn and Scatter Plots
at
FutureLearn
Highlights

Duration 4 weeks
Weekly study 4 hours
100% online Learn at your own pace

Data Visualisation with Python: Seaborn and Scatter Plots
at
FutureLearn
Course details

Skills you will learn

Python Data visualization Big Data

More about this course

This course will teach you how to bring big data sets to life through data visualisation using the powerful Python programming language.
On this course, you'll look at Python for beginners in data analytics. Python is one of the most widely used, and easiest to use programming languages, powering the back-ends of some of the world's biggest online companies, including Google, Dropbox and Instagram.
You'll learn how a Python programmer uses data to create graphical representations that can be easily analyzed and examined.
This course will also introduce you to Seaborn, a data-visualization library in Python. Seaborn combines aesthetic appeal with the powerful technical insights of the programming language.
You'll learn how to identify a scatter plot, line plot, and other relational plots, as well as how to understand the differences between them.
What are quantitative and categorical variables used for in Python? You'll see how the programming language visualizes categorical data (which has a fixed length) and quantitative data (which can be measured).
You'll also find out how to categorize plots and other quantitative variables of data visualization.
The final section of the course will teach you the basics of uncertainty within visualizations. You'll examine uncertainty in data, point estimate intervals, and confidence bands.
Using your new knowledge, you'll be able to confidently display uncertainty in data and walk through creating a workflow of a visualization based on exploring a dataset.