Learn Plotly and Dash to build interactive visualizations and dashboards with Python. Explore customization, interactivity, and complex datasets to create dynamic, data-driven applications.

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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

# Correlation matrix

The **Pearson's correlation coefficient** (also known as Pearson's $r$) is a statistic that measures the degree to which two variables move together in 
a linear fashion. Correlation takes on a value between -1 and 1, in which $r=-1$ implies perfect negative correlation  (points move together in a perfect straight line with a negative gradient) and $r=1$ implies perfect positive correlation (points move together in a perfect straight line with a positive gradient).

The image below details how correlation can be judged as a rule of thumb from _Straightforward Statistics for the Behavioral Sciences_. (Evans JD, 1996). To judge a negative correlation, just place a minus sign before each number in the table below:

---
<br>

| **Correlation Value** | **Description**                         |
| ----------------- | ----------------------------------- |
| r = 0 – 0.19      | very weak relationship              |
| r = 0.20 – 0.39   | weak relationship                   |
| r = 0.40 – 0.59   | moderate relationship               |
| r = 0.60 – 0.79   | strong relationship
| r = 0.80 – 1.   | very strong relationship                                  |


<br>

Here is the formula for correlation, however, we don’t  need to compute it from scratch:

$$r = \frac{\sum_{i=1}^{n}(x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum_{i=1}^{n}(x_i - \bar{x})^2}\sqrt{\sum_{i=1}^{n}(y_i - \bar{y})^2}}$$

Learn about bivariate analysis and how correlation matrices can assist in analyzing the relation between two continuous variables.

Correlation Matrices

Introduction and Univariate Analysis

Exploratory Data Analysis - Bivariate and Multivariate Analysis

Customizing Plots

Advanced Customization

Interactivity in Plotly

Time Series Analysis and Visualization Using Python and 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

Build Data-centric Dashboards Using Dash

Correlation Matrices

Correlation matrix