The wealth of Python data visualization libraries makes it hard to decide the best choice for each use case. However, if you're looking for statistical plots that are easy to build and visually appealing, Seaborn is the obvious choice. You'll begin this course by using Seaborn to construct simple univariate histograms and use kernel density estimation, or KDE, to visualize the probability distribution of your data. You'll then work with bivariate histograms and KDE curves. Next, you'll use box plots to concisely represent the median and the inter-quartile range (IQR) and define outliers in data. You'll work with boxen plots, which are conceptually similar to box plots but employ percentile markers rather than whiskers. Finally, you'll use Violin plots to represent the entire probability density function, obtained via a KDE estimation, for your data.

Seaborn's smartly designed interface lets you illuminate data through aesthetically pleasing statistical graphics that are incredibly easy to build. In this course, you'll discover Seaborn's capabilities. You'll begin using strip plots and swarm plots and recognizing how they work together using low-intensity noise. You'll then work with time series data through various techniques, like resampling data at different time frequencies and plotting with confidence intervals and other types of error bars. Next, you'll visualize both logistic and linear regression curves. Moving on, you'll use the pairplot function to visualize the relationships between columns in your data, taken two at a time, in a grid format. You'll change the chart type being visualized and create pair plots with multiple chart types in each plot. Lastly, you'll create and format a heatmap of a correlation matrix to identify relationships between dataset columns.

This course will get you familiar with the building blocks of Altair visualizations and some of the important chart settings. You will touch upon some of the fundamentals of plotting graphs in Altair. You'll start off by learning about the basic data structures that can form the basis of Altair visualizations, including JSON data and Pandas DataFrames in both wide-form and long-form. You'll then move on to plotting one of the simpler graphs, histograms, to visualize the distribution of values for a quantitative field in your dataset. While doing so, you'll get to explore the different ways in which Altair graphs can be customized including augmenting your chart with text, layering histograms to view two distributions together, and making histograms interactive.

This course will introduce you to a breadth of charts available in Altair and how you can use them to get an all-round understanding of your data. The focus is to get you familiar with the wide variety of graphs that are available. You'll begin by visualizing a distribution of numeric values using box plots and violin charts, each of which has its own strengths and limitations when analyzing distributions. You'll then move on to bar charts to analyze numbers associated with categories in your data. While doing so, you will get to explore a variety of aggregate operations that are available in Altair in order to calculate a sum, mean, median, and so on. You'll then use line charts to visualize the changes in a particular value over a period of time and also its related visual - the area chart. Finally, you'll produce scatter plots to visualize the relationship between a pair of fields in your data. Throughout this course, you'll delve into a number of customizations which are available in Altair for each of the graphs which you plot.

This course introduces you to the use of Altair visualizations which can convey very detailed information for specialized datasets. You will cover some of the graphs that can be used to convey the information in very specific kinds of datasets, while also giving you some hands-on experience with advanced chart configurations. You'll begin by plotting information on a map, both to mark locations of places as well as to convey numerical information about regions. You'll then build a heatmap to analyze the numbers associated with a combination of two categorical variables. Next, you'll implement candlestick charts to visualize stock price movements, dot plots to analyze the range of movement for some values, and Gantt charts to view a project plan. Finally, you'll explore the use of window functions to analyze the top K elements in each category of your dataset.

With Dash, you can create interactive web apps with elements such as buttons, dropdowns, sliders, range sliders, checkboxes, date pickers, and more. In this course, you'll learn how to get started with Dash, beginning with installing Dash and various extension libraries using the pip package installer. You'll move on to building web apps using Dash and the Plotly Express library. You'll also work with two other important Dash extension libraries - the Dash Core Components library and the Dash HTML Components library. You'll put all of these libraries together while exploring some of the default interactivity features, such as zooming and panning charts. You'll create a callback app where the title of a chart updates based on the values on a range slider before creating a more refined app with a callback that updates the chart itself and not just the title. Finally, you'll build a fully-fledged interactive scatter plot.

If you've used Dash before, you'll know how quickly the native HTML table abstraction from dash_html_components can become complicated and cumbersome due to the need to create a TR tag for each row and then individual TD tags within each TR tag for each cell value. Using the dash DataTable abstraction mitigates all of these weaknesses. In this course, you'll practice this technique before using the Dash data table to display data in the form of tables in Dash apps. You may also know that you can harness the power of Dash using HTML components or components from the Dash Core Component library. In this course, you'll also get a chance to work with user input and buttons in Dash apps using the most appropriate components.

The Dash DAQ library can be used to represent data in ways that correspond to real-world physical measurement mechanisms, such as switches, thermometers, knobs, dials, gauges, LED displays, and tanks. In this course, you'll learn how to work with this library. In addition, you'll practice creating widgets using Dash core components. You'll also create Dropdowns, TextAreas, RadioButtons, and Checklists. Finally, you'll cover two important aspects of building web apps - building tabbed apps with different controls on each tab and accepting user upload of files. You'll build a web app that accepts a file for upload, either via drag-and-drop or via direct user navigation, and then checks whether the file is a .csv file and if so, displays the contents of that file in a DataTable.

Final Exam: Data Visualization for Web Apps Using Python will test your knowledge and application of the topics presented throughout the Data Visualization for Web Apps Using Python track of the Skillsoft Aspire Pythonista to Python Master Journey.