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
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