Python Data Visualization Training

June 25 - 27, 2024
8:00am - 3:30pm

Location:

Center of Higher Learning Computer Lab / Building 1103 - Room 1005 / Stennis Space Center, MS

Course Information:

In this Data Visualization with Python training course, you will learn how to use Python’s data visualization libraries, including NumPy, Pandas, Matplotlib, Bokeh and Seaborn to better understand data analytics. You will improve your Python data wrangling skills, work with industry-standard tools, learn different data formats and representations and learn how to use Geoplot and Bokeh.

Costs:

$1300 per person for a 3-day training course

 Prerequisites:

A working knowledge of Python to the level of Introduction to Python Training

Learn How to Do the Following:

  • Use various plot types with Python

  • Explore and work with different libraries for data visualization

  • Understand and create effective visualizations

  • Improve your Python Data Wrangling skills

  • Work with industry-standard tools, including Matplotlib, Seaborn, and Bokeh

  • Learn how to use Geoplotlib and Bokeh

  • Continue learning and face new challenges with after-course one-on-one instructor coaching.

Course Outline:

  • Module 1: Fundamentals of Python

    • In this module, you will learn about the following:

      • Importance of Data Visualization

      • Visualization Using Python

      • Data Cleaning

      • Data Wrangling

      • Types of Data

      • Statistics

      • Probability

      • Exploratory Data Analysis

      • Python

      • JupyterLab

      • Basic Python Data Types

      • Flow Control

      • Slicing

      • Defining Functions

      • Lambdas

      • Classes

  • Module 2: NumPy and Pandas

    • In this module, you will learn about the following:

      • NumPy

      • The NumPy ndarrays

      • Slicing ndarrays

      • Boolean Indexing

      • Element-wise Arithmetic

      • Transpose of a ndarray

      • Dot Products

      • Stacking

      • SciPy

      • pandas

      • Series and DataFrames

      • Loading and Saving Data with pandas

      • Creating DataFrames

      • Inspecting Data

      • Selecting Columns and Rows

      • The head() and tail() methods

      • Basic Plots

      • Descriptive Statistics From a DataFrame

      • Filtering, Sorting and Grouping

      • Replacing Values and Renaming Columns

      • Joining and Combining DataFrames

      • Reading Data From Files

      • Reading From a Relational Database

      • Loading External Data From NoSQL Stores (MongoDB)

      • SciPy

      • Sci-Kit Learn

  • Module 3: Visualization with Matplotlib

    • In this module, you will learn about the following: 

      • Matplotlib 

      • Architecture

      • The Fix Object

      • Axes, Labels, Titles, Legends and Grids

      • Reading Data from Files and Other DataSources

      • The pyplot API

      • The plot() Method

      • The Format String

      • Marker and Line Styles

      • Plotting Labelled Data

      • Plotting Multiple Graphs on the Same Axes

      • Saving Figures

      • Labels and Titles

      • Annotations

      • Legends

      • Line Chart

      • Area Chart

      • Stacked Area Chart

      • Scatter Plot

      • Bubble Chart

      • Heat Map

      • Contour Plot

      • Histogram

      • Kernel Density Estimate Plot

      • Box Plots

      • Violin Plots

      • Bar Plot

      • Grouped bar or column chart

      • Stacked Bar Plots

      • Error bars

      • Radar Plots

      • Pie Plots and Donuts

      • Tree Maps

  • Module 4: Simplifying Visualization with Seaborn

    • In this module, you will learn about the following: 

      • Seaborn

      • Styling

      • Scaling and the Plotting Context

      • Overriding Context Settings with the rc Parameter

      • Themes

      • Colors in Seaborn

      • Varying Hue to Distinguish Categories

      • Vary Luminance to Represent Numbers

      • Choosing a Palette with the color _palette() Function

      • Qualitive Color Palettes

      • Sequential Palettes

      • Diverging Palettes

      • Histograms

      • Multiple Histograms on the Same Axes

      • Kernel Density Plots

      • Box Plots

      • Violin Plots

      • Contour Plots

      • The FacetGrid

      • Some Functions that Return a FacetGrid

      • Pair Plots

      • The relplot() Function

      • The regplot() and implot() Functions

      • Creating a Regression Plot

      • Variables That Take Discrete Values

      • Using a Representative Value

      • Squarify

  • Module 5: Plotting geospatial data with Geoplotlib

    • In this module, you will learn about the following: 

      • Geoplotlib

      • Input and Output

      • Interaction

      • The dot Visualization

      • Zooming

      • 2D Histogram

      • Heat Map

      • Voronoi Tessellation

      • Seed Points

      • Delaunay Triangulation

      • GeoISON

      • Adding Color and Toolkits

      • Tile Providers

      • The DarkMatter Tiles

  • Module 6: Adding interaction with Bokeh

    • In this module, you will learn about the following: 

      • How Bokeh Works

      • Bokeh Server

      • Programming Interfaces

      • The Bokeh Models

      • Glyphs, Plots and Layouts

      • The bokeh.plotting Interface

      • Some Glyph Methods on the Figure Object

      • Widgets in Bokeh

      • Using Bokeh Server

      • Setting Up the Widgets

      • The TextField Widget

      • The Other Widgets

      • Running Bokeh Server

      • Widgets Using CustomJS

      • Widgets with ipwidgets

Registration:

Seating is limited! To register, contact Ashley McGinty at Ashley.n.west@usm.edu or 228-688-3170. The deadline for registration is June 7, 2024.