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.