Data Visualization Mastery

Most organizations depend on data visualization to help drive business decisions. Using the correct charts and graphs helps communicate the right information and identify patterns and trends within your data. You'll begin this course by identifying the importance of data visualization and its application in curating data that is easy to digest, understand, and interpret. Next, you'll discover best practices for visualizing data and the significance of creating user-centered visuals. You'll then explore different presentation types such as comparison, composition, distribution, trends, and relationship, and learn how to map your charts to these categories. Finally, you'll investigate the use cases for popular charts such as line charts, pie charts, histograms, scatter plots, and others. You'll also recognize when to use specialized charts such as Gantt charts, sunburst charts, and Sankey diagrams.

Excel charts can be used for a myriad of data visualizations, including categorical data and continuous data, like time-series data. In this course, you'll learn how to bring data into Excel and build and customize various charts. You'll start by importing data from an existing workbook into a new spreadsheet. You'll then import data from CSV and JSON file formats and Microsoft Access database files. Next, you'll use the Power Query editor to perform various operations. Moving on, you'll create column and clustered column charts and perform various formatting operations on the clustered column chart, such as adding data labels, error bars, axis titles, and trendlines. Lastly, you'll create a simple line chart, formatting various aspects, such as the line, background, title, legend, axes, and position of charts relative to each other.

Data visualizations in Excel reveal the insights uncovered by your data in easy-to-consume representations. You can identify categorical values, recognize how parts sum up to a whole, see percentages rather than absolute values, discretize continuous variables, and approximate the probability density function of variables. In this course, you'll build charts to uncover all of this information. You'll start by working with column and bar charts. You'll then create and differentiate between clustered and stacked column charts. You'll move on to formatting and customizing bar and column charts before working with 2D and 3D chart types and customizing them in various ways. Lastly, you'll work with histograms, examining how they work, what they're used for, and how to customize them to your needs.

Line charts are possibly the most common type of visualization for time-series data, enabling you to see time trends at a glance. These can be augmented with trendlines, used to visualize time trends in data. Stacked area charts are a powerful type of visualization, combining information about trends over time with information about composition and parts of a whole. In this course, you'll learn how to create and customize all of the visualization types above. You'll begin by exploring the purpose of line charts before moving on to formatting and customizing them. You'll then practice using trendlines to evaluate different regression models on data in a line chart. You'll also customize and format these trendlines. Following this, you'll work with area charts and stacked area charts, examining, in detail, the several types of stacked area charts in Excel and customizing their appearance.

Data visualization options in Excel are vast. You should choose your visualization type based on the data and what you want to show from it. For example, using High-Low-Close and Open-High-Low-Close charts (also called candlestick charts), you can summarize several stock performance aspects. Excel also lets you build radar charts - great for visualizing multivariate ordinal data, such as ratings or scores, to spot strengths or spikes. In this course, you'll not only learn how to build and customize the charts mentioned, but you'll also create treemaps to visualize hierarchical data and pie charts to display parts of a whole. You'll then generate pie-of-pie and bar-of-pie charts, both of which use a secondary visualization to complement a pie chart. Finally, you'll create donut charts to visualize composition using multiple concentric donut rings to represent points in time.

Once you grasp how to work with the scope of standard Excel chart types, you can expand into more complex visualizations. For example, you can use box-and-whisker plots to convey a wealth of information about the statistical distribution of a variable and identify outliers in a data series. You can use sunburst charts to visualize hierarchical data with differing levels of detail, waterfall charts to show the cumulative effect of positive and negative values, and Gantt charts to illustrate progress toward a goal involving multiple parallel tasks. Additionally, you can avail of band charts to quickly eyeball the trend in a line chart, scatter plots to uncover the relationship between two variables, and waffle charts to visualize progress towards KPIs. In this course, you'll create all of these charts either via Excel's built-in tools or by building them manually using nifty workarounds.

Final Exam: Data Visualization with Excel will test your knowledge and application of the topics presented throughout the Data Visualization with Excel track of the Skillsoft Aspire Data Visualization Journey.

Infogram, a cloud-based tool for creating easily comprehensible presentations and reports, is a useful tool for roles in all industries. In this course, you'll learn to use this tool from scratch, moving from installation to building a project. You'll start this course by setting up an Infogram account. You'll then spend some time cycling through the different types of projects in Infogram and catch a glimpse into the variety of built-in templates. Next, you'll create a basic Infogram project. After which, you'll examine different element types that can be added to projects, including text, icons, shapes, arrows, and background images. You'll move on to formatting data in an external tool before visualizing it in Infogram charts. Lastly, you'll briefly outline the use of Microsoft Excel and its pivot table feature for data preparation.

Once you've learned how to set up a basic project in Infogram, there are several ways you can ensure the information in your infographic stands out. In this course, you'll work with Infogram's many advanced data visualization features. You'll start by using the bar race plot feature to animate bar charts that change over time. You'll then create candlestick charts to visualize variations in data over set periods and waterfall charts to convey the cumulative effect of positive and negative values. Next, you'll create dashboards to convey information using various visual elements, including a treemap, a series of line charts, a streamgraph, and a forecast line chart. Moving on, you'll use an infographic project type to convey several fun bits of information related to James Bond films. To do this, you'll use a Gantt chart, a line chart, and some animated components.

Visme is a visualization tool that facilitates data delivery by transforming content into visually appealing formats like infographics and presentations. In this course, you'll achieve a broad overview of Visme's capabilities and use cases. You'll begin by identifying how to create different types of visuals in Visme using templates for concise infographics, social media posts, as well as detailed reports. Moving on, you'll examine how to work with some of these visuals and start by building an infographic. You'll then recognize how to incorporate design elements such as icons, shapes, images, and text within visuals to enhance their appearance. Finally, you'll investigate how to include a video within a visual and make your project stand out by animating various elements of your visuals.

Charts and graphs help convey information visually, which enables viewers to draw meaningful conclusions from the data. Visme allows users to create and modify different types of charts and use them within other visuals like infographics, reports, presentations, and social media graphics. In this course, you'll explore the use cases and configuration methods of different bar charts. You'll start by recognizing how to build a simple bar chart and alter its appearance. You'll then examine how to visualize diverse forms of data by implementing horizontal bar charts and stacked bar charts. Next, you'll investigate how to convey proportions in data using pie charts and donut charts. You'll also identify how to work with line charts and area charts to analyze changes in values within a dataset over a period of time.

A presentation often requires powerful graphic elements in order to convey information effectively. Visme's versatile design elements can help you create an engaging and visually impactful presentation. In this course, you'll explore how to work with Visme's design elements and configurations to create a visually striking presentation. You'll begin by recognizing how to create a presentation from an existing template and modify it to suit a specific visual theme. You'll then examine how to configure presentation backgrounds by formatting text and icons, incorporating layers, and using a grid to align different elements. You'll also work with a map chart to highlight specific countries in the world that are relevant to your presentation. Finally, you'll discover how to collaborate with another Visme user by sharing your project.

Final Exam: Creating Infographics for Data Visualizations will test your knowledge and application of the topics presented throughout the Creating Infographics for Data Visualizations track of the Skillsoft Aspire Data Visualization Journey.

Matplotlib is a Python plotting library used to create dynamic visualizations using pyplot, a state-based interface. You'll learn how to correctly install and use Matplotlib to build line charts, bar charts, and histograms in this course. You'll create basic line charts out of randomly generated data. You'll learn how to use the plt.subplots() function, import data from a CSV file using pandas, and create and customize various line charts.
Additionally, you'll create figures holding more than one axes object, learn why and how to use the twinx() function, and create multiple lines in the same line chart with different y-axes for each line.

Moving on, you'll construct histograms that visualize multiple variables and approximate the cumulative probability density function. Lastly, you'll create some bar charts to represent categorical data.

Matplotlib can be used to create box-and-whisker plots to display statistics. These dense visualizations pack much information into a compact form, including the median, 25th and 75th percentiles, interquartile range, and outliers. In this course, you'll learn how to work with all aspects of box-and-whisker plots, such as the use of confidence-interval notches, mean markers, and fill color. You'll also build grouped box-and-whisker plots. Next, you'll create scatter plots and heatmaps, powerful tools in exploratory data analysis. You'll build standard scatter plots before customizing various aspects of their appearance. You'll then examine the ideal uses of scatter plots and correlation heatmaps. You'll move on to visualizing composition, first using pie charts, building charts that explode out specific slices. Lastly, you'll build treemaps to visualize data with multiple levels of hierarchy.

An interactive visualization library, Bokeh allows users to create diverse graphics and highly interactive dashboards and data applications. In this course, you'll achieve a foundational knowledge of using Bokeh to build simple graphs and visualizations. You'll start by exploring how to install Bokeh on your local machine, display charts inline within your Jupyter notebooks, and create an interactive visualization. You'll then recognize how to save Bokeh charts as HTML and PNG files. Next, you'll investigate how to visualize categorical data using bar charts, stacked bar charts, and clustered bar charts. You'll also identify how to implement pie charts and donut charts to represent compositions in your data. You'll finish the course by examining the ease of interactivity and granular customizations that Bokeh offers.

Bokeh facilitates the creation of high-performance charts allowing users to build impactful web-based dashboards and applications. In this course, you'll investigate how to visualize your data using complex charts in Bokeh. First, you'll identify how to visualize relationships that exist in your data using scatter plots, discover the function of jitter in viewing individual data points, and configure scatter plots where both axes represent continuous values. Next, you'll outline how to represent relationships between pairs of variables using heatmaps. You'll then recognize the use of line and area charts to visualize time-series data. Finally, you'll explore how to visualize data structures in the form of nodes and edges using network graphs. When you have completed this course, you'll possess the skills and knowledge to build simple as well as complex interactive visualizations using Bokeh.

Plotly is Python's browser-based graphing library, which provides users with online graphing, analytics, and statistics tools. In this course, you'll explore how to use Plotly's declarative APIs to build interactive graphs and visualizations. You'll start this course by getting familiar with the components of the Plotly library. You'll identify the role of the high-level library (plotly.express) in creating visualizations and the low-level library (plotly.graph_objects) in creating granular customizations of your charts. Next, you'll investigate the use of box plots in visualizing the statistical properties of a continuous data series. You'll also discover how to represent additional categorical data by creating separate box plots and customizing their color. Finally, you'll examine how to implement a candlestick chart to reflect the trend of stock price performance over a period of time and visualize sequential data in a linear process using funnel charts.

Using data visualizations during exploratory data analysis is an important part of the data science process. The Plotly graphing library helps with this by allowing users to investigate their data through interactive charts. In this course, you will explore the construction and applications of advanced charts in Plotly for varied use cases. You'll begin by identifying how to present multi-dimensional ordinal data along different axes using radar charts. You'll then recognize how to use sunburst charts to visualize multi-level hierarchical data. Next, you'll implement Gantt charts to visualize schedules and timelines in a project and then move on to exploring how to represent the flow of data between entities using Sankey diagrams. You'll finish the course by investigating the use of geo plots and choropleth maps in visualizing geographical data and plot locations.

Final Exam: Data Visualization with Python will test your knowledge and application of the topics presented throughout the Data Visualization with Python track of the Skillsoft Aspire Data Visualization Journey.