Data Visualization Training Course

Learn how to use Data Visualization using NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more!

ABOUT PROGRAM

In recent years, the generation of data has witnessed exponential growth. So much so that 90% of all the data in the world has been created in the last two years alone. Everyone, from large business houses to governments across the world, is looking for Data Scientists who can enable and implement data-driven projects and make the data understandable using data visualization. In this program, we will use different types of data visualization techniques like pie-chart, Box Plot, Histogram, Heat Map, or more others data visualization techniques using Matplotlib and Sklearn.

Data Visualization Techniques

In this course, our expert covers all types of data visualization techniques which are mostly used by all the Data Science business-related industries. 

Data Visualization Using Pie-Chart 

Data Visualization Using Heat-Map 

Data Visualization Using Histogram

Data Visualization Using Box-Plot 

Data Visualization Using Bar-Plot

Data Visualization Using Scatter-Plot

Detailed Description About This Course...

Data Visualization Basics 

  • Why we visualize

  • History of data visualization 

  • Quick shot video of data presented masterfully

  • Data models

  • Before and after snapshots

Effective Visuals Charts

  • The visualization of 2 numbers

  • Tables vs charts vs single metrics

  • Chart selection: choosing the right visual for the job

  • Data relationships

  • Chart components

  • Primary and modified charts

  • Visual vocabulary

Managing visual effects

  • Colour and its rules

  • Sequential, diverging, categorical, highlight, alert data

  • Colour vision deficiency

  • Contrast and consistency

  • Brand colours and colour palette

Managing visual effects

  • Colour and its rules

  • Sequential, diverging, categorical, highlight, alert data

  • Colour vision deficiency

  • Contrast and consistency

  • Brand colours and colour palette

Building a Learning Path

  • Step by step chart makeover examples

  • Qualitative data visualisation - text, others

  • Data magazines, Data art, Physical Data and others

  • Data Visualization Tools

  • Personal experience with SAS visual analytics, Tableau, Qlikview, Data Wrapper and D3.js

Exploratory Data Visualisation

  • Statistics are questions

  • Understanding signal and noise

  • Percentages and points

  • Distribution

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