Data Visualization with Python Training

Course 1274

  • Duration: 3 days
  • Labs: Yes
  • Language: English
  • Level: Intermediate
Get This Course $2,650
  • 3-day instructor-led training course
  • After-course coaching available

#1274
  • Guaranteed to Run - you can rest assured that the class will not be cancelled.
    Aug 24 - 26 9:00 AM - 4:30 PM EDT
    AnyWare
  • Oct 19 - 21 9:00 AM - 4:30 PM EDT
    Herndon, VA or AnyWare
  • Feb 22 - 24 9:00 AM - 4:30 PM EST
    Ottawa or AnyWare
  • Apr 19 - 21 9:00 AM - 4:30 PM EDT
    Herndon, VA or AnyWare

In this Data Visualization with Python Training course, you’ll learn how to use Python’s data visualization libraries, including NumPy, Pandas, Matplotlib, and Seaborn to better understand data analytics.

Data Visualization with Python Training Delivery Methods

  • In-Person

  • Online

Data Visualization with Python Training Course Benefits

  • Learn how to 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 different data formats and representations

  • Learn how to use Geoplotlib and Bokeh

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

Data Visualization with Python Training Outline

In this module, you will learn about:

  • Importance of Data Visualization
  • Visualization Using Python
  • Data Cleaning
  • Data Wrangling
  • Types of Data
  • Statistics
  • Probability
  • Exploratory Data Analysis
  • Python
  • Jupyter Notebook
  • Google Colab and Kaggle Notebooks
  • JupyterLab
  • Basic Python Data Types
  • Flow Control
  • Slicing
  • Defining Functions
  • Lambdas
  • Classes

In this module, you will learn about:

  • NumPy
  • The NumPy ndarray Object
  • 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

In this module, you will learn about:

  • Matplotlib
  • Architecture
  • The Figure Object
  • Axes, Labels, Titles, Legends and Grids
  • Reading Data from Files and Other DataSources
  • The pyplot API
  • The plot() Method
  • The Format String
  • Markers 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

In this module, you will learn about: 

  • 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
  • Qualitative 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

In this module, you will learn about:

  • Geoplotlib
  • Input and Output
  • Interaction
  • The dot Visualization
  • Zooming
  • 2D Histogram
  • Heat Map
  • Voronoi Tessellation
  • Seed Points
  • Delaunay Triangulation
  • GeoJSON
  • Adding Color and Tooltips
  • Tile Providers
  • The DarkMatter Tiles

In this module, you will learn about:

  • 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

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Data Visualization With Python Training FAQs

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

Yes! We know your busy work schedule may prevent you from getting to one of our classrooms which is why we offer convenient online training to meet your needs wherever you want, including online training.

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