Data Visualization with Python

People are realizing the vast benefits and uses of analyzing Big Data, but the majority of people lack the skills and the time needed to understand this data. This is where Data Visualization comes in. Learn to create easy to read and simple, graphs, charts, and other visual representations of data. This course will give you a full understanding of how to program with Python and how to use it in conjunction with scientific computing modules and libraries to analyze data.

If you want to learn in more depth how to visualize data using matplotlib, seaborn, and other tools, sign up for Data Visualization course now!


In this workshop you’ll learn Data Visualization with Python:

  • Manage your workflow with the Jupyter Notebook Environment
  • Creating and manipulating arrays with the numpy library
  • Use the pandas module with Python to create and structure data
  • Learn how to work with various data formats like JSON, HTML, and MS Excel Worksheets within Python
  • Create data visualizations using matplotlib and the seaborn modules with Python

This course has been specially designed for students who want to learn a variety of ways to visually display data with Python. On completion of this course, you will have gained a deeper understanding of the options available for visualizing data.

Prereqs & Preparation

You must bring a laptop with a text editor.

Sublime Text is recommended and has a free trial version (

In addition, students should install Anaconda, which is a free package that includes python and a number of tools that will be used in class (

Day 1

Session I: Course Introduction

  • Getting Matplotilb And Setting Up

Session II: Different types of basic Matplotlib charts

  • Section Intro
  • Basic Matplotlib graph
  • Labels, titles and window buttons
  • Legends
  • Bar Charts
  • Histograms
  • Scatter PlotsStack
  • PlotsPie Chart
  • Loading data from a CSV
  • Loading data with NumPy
  • Section Outro

Session III: Basic Customization Options

  • Getting Stock Prices For Our Data Set
  • 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 Outro

Day 2

Session IV: Advanced Customization Options

  • Section Intro
  • Basic subplot 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 Outro

Session V: Geographical Plotting with Basemap

  • Section Intro
  • Downloading and installing Basemap
  • Basic basemap example
  • Customizing the projection
  • More customization, like colors, fills, and forms of boundaries
  • Plotting Coordinates
  • Connecting Coordinates
  • Section Outro

Session Vl: 3D graphing

  • Section Intro
  • Basic 3D graph example using wire_frame
  • 3D scatter plots
  • 3D Bar Charts
  • More advanced Wireframe example