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.