Data Visualization with Python and Matplotlib

Big data has a wide range of benefits. Yet, many individuals lack the skill to interpret big data in its original structure. To understand it ideally, data visualization is used. This creates visual representations of data like charts, graphs, etc which makes it simple to read and easy to understand

Request a quote Review training schedules

Learn more about the course below.


Data Visualization with Python and Matplotlib Training program overview

In this course, you will be guided through the setup of Matplotlib- A plotting library for Python programming language. You will learn various types of visual representations like pie charts, histograms, scatter plots and many more. This course has been specifically outlined for individuals are interested in learning the various ways of displaying data visually. At the end of this course, you will be able to create live graphs and visualize geographical data on maps.



This course helps you visualize varied forms of 2D and 3D graphs, like bar charts, line graphs, scatter plots,etc. You will also learn how to customize those graphs such as modifying lines, colors, etc.

The registration process

Once you have completed our simplified enrolment process, you’ll receive an email confirmation with your payment receipt in your registered email ID. You can then access the entire content of the online student portal immediately by logging in to your account on our site. Should you require any assistance please reach out to us via email ( or via our online chat system.

The course curriculum

The curriculum for this Blockchain Security training incorporates all updates to the certification exam. The following is a list of broad topics covered

  • Introduction
  • Getting Matplotlib And Setting Up
  • Section Introduction
  • Basic matplotlib graph
  • Labels, titles and window buttons
  • Legends
  • Bar Charts
  • Histograms
  • Scatter Plots
  • Stack Plots
  • Pie Chart
  • Loading data from a CSV
  • Loading data with NumPy
  • Section Conclusion
  • Section Introduction
  • Source for our Data*
  • Parsing stock prices from the internet*
  • Plotting basic stock data*
  • Modifying labels and adding a grid*
  • Converting from unix time and adjusting subplots*
  • Customizing ticks*
  • Fills and Alpha*
  • Add, remove, and customize spines*
  • Candlestick OHLC charts*
  • Styles with Matplotlib*
  • Creating our own Style*
  • Live Graphs*
  • Adding and placing text*
  • Annotating a specific plot*
  • Dynamic annotation of last price*
  • Section Conclusion
  • Section Introduction
  • Basic suplot additions*
  • Subplot2grid *
  • Incorporating changes to candlestick graph*
  • Creating moving averages with our data*
  • Adding a High minus Low indicator to graph*
  • Customizing the dates that show*
  • Label and Tick customizations*
  • Share X axis*
  • Multi Y axis*
  • Customizing Legends*
  • Section Conclusion
  • Section Introduction
  • Downloading and installing Basemap
  • Basic basemap example
  • Customizing the projection
  • More customization, like colors, fills, and forms of boundaries
  • Plotting Coordinates*
  • Connecting Coordinates*
  • Section Conclusion
  • Section Introduction
  • Basic 3D graph example using wire_frame
  • 3D scatter plots
  • 3D Bar Charts
  • More advanced Wireframe example
  • Section Conclusion
  • Conclusion