Data Visualization with Python Training
Course 1274
3 DAY COURSE

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 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

Module 1: Fundamentals of Python

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

Module 2: NumPy and Pandas

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

Module 3: Visualization with Matplotlib

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

Module 4: Simplifying Visualization with Seaborn

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

Module 5: Plotting geospatial data with Geoplotlib

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

Module 6: Adding interaction with Bokeh

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