This course provides a comprehensive beginner through to advanced learning pathway to gain skills in data visualization with both Plotly and Dash in Python.

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plotlydash.tar.gz

ch1_01-the-plotly-layout

codingExercises

Ch3_04-hover-types

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CH1_02-categorical-univariate-analysis-lab_Q

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CH1_03-numeric_univariate_analysis-lab_Q

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CH2_01-continuous-vs-categorical-lab_Q

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CH6_02_Visualising_Classification_Model_Outputs_lab_Q

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CH6_03_dimensionality_reduction_lab_Q

CH7_01-simple-example

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Discover the compelling universe of Plotly and Dash, versatile Python libraries that enable interactive data visualization and web dashboard applications. These tools are essential for anyone seeking to effectively communicate data narratives and build interactive data visualizations.

You'll explore Plotly’s robust data visualization capabilities. You’ll create plots for univariate, bivariate and multivariate analysis before customizing and adding interactivity, using real world datasets. Then, you’ll build upon and use these skills when learning Dash as you build and customize analytical dashboard apps. You’ll journey through Dash's structure, from understanding layouts and HTML components to mastering Dash styling using CSS. You’ll learn the significance of callback functions, enhancing user interactivity, and experience the power of Dash's DataTable component.

By the end of this course, you'll be able to create engaging visualizations with Plotly and develop interactive web applications using Dash.

Mastering Plotly and Dash Data Visualization with Python

Graph objects are better for simple plots.

Plotly Express generates plots in fewer lines of code.

What is the difference between Plotly Express and graph objects?

What is the difference between Plotly Express and graph objects?

As the number of categories in a variable increases, it becomes harder to read and interpret a pie chart.

What is one disadvantage of a pie chart?

To add additional annotations to the plot

What is the purpose of using `fig.show()` in the code examples?

What is the purpose of using <code>fig.show()</code> in the code examples?

A variable that can take on a limited, number of possible values.

A variable that can only take on numerical values.

A variable that has an infinite number of possible values.

A variable that is not relevant to statistical analysis.

A data analysis technique that involves the analysis of two variables

A data analysis technique using linear models

A data analysis technique that involves the analysis of a single variable

A type of plot that displays the relationship between two continuous variables

A plot where a user must count the number of items and input it to create

A type of plot that displays the trend of a continuous variable over time

A type of bar plot that displays the count or frequency of occurrences of each category in a categorical variable

Numeric variables are always continuous, while categorical variables are always discrete.

Numeric variables can take on an infinite number of values, while categorical variables can only take on a limited number of possible values.

Numeric variables are always ordinal, while categorical variables are always nominal.

Numeric variables are always represented as text, while categorical variables are always represented as numbers.

What distinguishes a numeric variable from a categorical variable?

What distinguishes a numeric variable from a categorical variable?

A type of plot that displays the median, quartiles, and outliers of a numeric variable

`hist()` for graph objects and `histogram() `for Express

`make_histogram()` for graph objects and `hist()` for Express 

`go.Histogram()`for graph objects and `px.histogram()` for Express 

`Histogram()` for both Express and graph objects

Which Python library function is used to create a histogram plot in Plotly graph objects and Plotly Express?

Which Python library function is used to create a histogram plot in Plotly graph objects and Plotly Express?

Quiz: Introduction and Univariate Analysis

Quiz: Introduction and Univariate Analysis

Test your understanding of Plotly Express, numeric, and categorical univariate analysis processes.

Introduction and Univariate Analysis

Exploratory Data Analysis - Bivariate and Multivariate Analysis

Customizing Plots

Advanced Customization

Interactivity in Plotly

Visualizing Machine Learning Models with Plotly

Global Energy Consumption Visualization

Introducing Dash

Dash Layout

Dash Styling

Dash Callbacks

User Interactivity in Dash

Tabular data in Dash DataTable

Global Energy Consumption Dashboard

Conclusion

Quiz: Introduction and Univariate Analysis