High-dimensional Data visualization techniques using python

Offered By
Coursera Project Network
In this Free Guided Project, you will:

Learn how to preprocess High-Dimensional data for visualization and analysis

Learn how implement Scatter plot matrix and Parallel Coordinate plot in python

Learn about why/how data reduction techniques

Showcase this hands-on experience in an interview

Clock2 hours
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

By the end of this project you will learn how to analyze high-dimensional data using different visualization techniques. We are going to learn how to implement Scatterplot Matrix and Parallel coordinate plots (PCP) in python. and We will learn how to use these two high-dimensional data visualization techniques to analyze our data by solving three tasks: Outlier Detection, Correlation Analysis and Cluster analysis. we will also talk about Data reduction techniques. we will learn how to sample our data to reduce the number of the data points for a better visualization. We will also learn about the Dimensionality reduction technique to reduce the number of dimensions in our dataset and how it can help us for a better analysis.

Requirements

Python programming language, Working with jupyter notebook environment, and having familiarity working with pandas and matplotlib modules

Skills you will develop

Data Pre-ProcessingData ReductionPython ProgrammingData AnalysisData Visualization (DataViz)

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Introduction to the project

  2. Data Normalization and Clustering

  3. Scatter Plot Matrix

  4. Parallel coordinate plot

  5. Data Reduction

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

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