Complete Python Masterclass For Data Visualisation

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About Course

Data visualisation is an integral part of Data Science. Programming Language Python is used to visualise any given data. Unlock your proficiency in the realm of data with our Complete Python Masterclass for Data Visualisation.

This masterclass course will significantly focus on Matplotlib – a comprehensive library for creating static, animated, and interactive visualizations in Python. Firstly, you will taught about the setup and installation of Anaconda Navigator and Matplotlib. Secondly, you will learn to utilise Matplotlib to plot Line Plots, Histograms etc, followed by Time Series Data Visualisation. Finally, the course will end with the installation and setup of Plotly and Cufflinks, preceded by two other modules.

At the end of the course, you will receive a certificate of achievement. This certificate will enhance your employability in the IT industry. Join now!

Certification

After completing and passing the Complete Python Masterclass for Data Visualisation course successfully, you will be able to obtain a Recognised Certificate of Achievement. Learners can obtain the certificate in hard copy at USD 19.30 or PDF format at USD 15.44.

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What Will You Learn?

  • Python Environment Setup for Data Visualisation
  • Basic to Advanced Plotting with Matplotlib
  • Time Series Data Visualization
  • Creating Visualisations with Seaborn
  • Interactive Plots with Plotly and Cufflinks

Course Content

Introduction to seaborn

  • Introduction to seaborn
    00:00

Installation and Setup

3D plots, spread plot and hist plot, bubble plot, and heatmap

Adding a grid to the chart

Adding a legend

Adding a legend

Adding edges to dots

Adding legends, titles, location and rotating pie chart

Adding legends, titles, location and rotating pie chart

Adding median to histogram

Advanced Histograms and Patches (Part 1)

Advanced Histograms and Patches (Part 2)

Cat plots

Changing colors of markers

Changing colors, linestyles, linewidth and markers

Changing edge color and adding shadow on the edge

Changing edge colour of the histogram

Changing fill color of different areas (negative vs positive for example)

Changing the axis scale to log scale

Changing the axis scales

Changing the size of the dots

Connecting data points by line

Controlling Plotted Figure Aesthetics

Converting string dates using the .to_datetime() pandas method

Converting string dates using the .to_datetime() pandas method

Creating Box and Whisker Plots

Explaining Matplotlib libraries apart

Filling area on line plots and filling only specific area

Filling only a specific area

Getting separate figures

Histograms vs Bar charts (Part 1)

Histograms vs Bar charts (Part 2)

Installing Matplotlib, seaborn & cufflinksv

Installing the Anaconda Navigator

Jointplot, pair plot and regression plot

Label Styling

Line plots

Line, Scatter, Bar, box and area plot

Overlaying bar plots on top of each other (Part 1)

Overlaying bar plots on top of each other (Part 2)

Plotting a basic scatter plot

Plotting a basic stack plot

Working on hue, style and size in seaborn

Plotting a stack plot od data with constant total

Plotting a stem plot

Plotting live data using FuncAnimation in matplotlib

Plotting multiple plots in one figure

Reading data from a csv file with pandas

Saving figures to your computer

Setting up the number of rows and columns

Subplots using seaborn

Using the Python datetime module

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