In this Data Visualisation with Python Training course, you’ll learn how to use Python’s data visualisation libraries, including NumPy, Pandas, Matplotlib, and Seaborn to better understand data analytics.
Data Visualisation with Python Training Delivery Methods
Data Visualisation with Python Training Course Benefits
Learn how to use various plot types with Python
Explore and work with different libraries for data visualisation
Understand and create effective visualisations
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 Visualisation with Python Training Outline
In this module, you will learn about:
- Importance of Data Visualisation
- Visualisation 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
- Histogramme
- 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
- Histogrammes
- Multiple Histogrammes 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 Visualisation
- Zooming
- 2D Histogramme
- 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
