Organizations increasingly need someone to make sense out of all the “Big Data” they are generating. This course will teach you how to identify the signals hidden in your datasets and choose visualizations that tell compelling, interesting stories.
Data Visualization Designer is one of the hottest new careers in tech because organizations around the world are drowning in all the “Big Data” they’re generating. They need people with the skills to turn raw facts and figures into visualizations that tell a story and make an impact. In this course, Designing Data Visualizations, you'll learn how to use your creativity and intuition to find the insights in datasets, as well as basic data-analysis techniques, to create “enriched” data and choose the correct visual to communicate real meaning. First, you’ll see how to spot “signals” in the data – it turns out that human common sense and knowledge of context are not only still relevant, but absolutely crucial. Then, you’ll learn techniques like pivot tables, conditional formatting, and data joins and merges to generate clear insights, while avoiding the “Four Deadly Sins of Data Visualization.” Finally, you’ll explore what kinds of visualizations match up best with what the data is telling you. When you’re finished with this course, you'll understand how to look inside the data, pull out what your audience needs and is most interested in, and use that to design data visualizations that stick.
What are data visualizations?
Data visualization is an interdisciplinary field that deals with the graphic representation of data. It is a particularly efficient way of communicating when the data is numerous as for example a Time Series.
What are the prerequisites for the Designing Data Visualizations course?
Whether you’re new to designing data visualizations or looking to enhance your skills, this is the perfect place to get started. You’ll learn how to use your creativity and intuition to find the insights in datasets, as well as basic data-analysis techniques, to create “enriched” data and choose the correct visual to communicate real meaning.
Why is data visualization important?
Data visualizations make big and small data easier for the human brain to understand, and visualization also makes it easier to detect patterns, trends, and outliers in groups of data. Good data visualizations should place meaning into complicated datasets so that their message is clear and concise.
What are data visualization tools?
Data visualization tools provide designers with an easier way to create visual representations of large data sets. When dealing with data sets that include hundreds of thousands or millions of data points, automating the process of creating a visualization makes a designer's job significantly easier.
What are pivot tables?
A pivot table is a table of statistics that summarizes the data of a more extensive table. This summary might include sums, averages, or other statistics, which the pivot table groups together in a meaningful way. Pivot tables are a technique in data processing.
What is conditional formatting?
Conditional formatting is a feature in many spreadsheet applications that allows you to apply specific formatting to cells that meet certain criteria. It is most often used as color-based formatting to highlight, emphasize, or differentiate among data and information stored in a spreadsheet.
Course Overview Hi everyone. My name is David LaFontaine, and welcome to my course, Designing Data Visualizations. I'm a senior UX designer and researcher, although I also like to call myself a creative data scientist because I believe strongly in bringing together numbers and data and design and human factors. And this course is definitely going to help you learn how to use your creativity and intuition to transform raw data into compelling visuals that tell a story. This skill is in increasing demand as more and more organizations generate data that they need someone to interpret for them. Some of the major topics we're going to cover include discovering what we call signals in the data by using simple data analysis techniques like pivot tables, understanding how those signals are key to choosing the relationships to visualize, how to avoid the four deadly sins of data visualization, using interpolation and extrapolation to go beyond just what's in your data sets, and, finally, how to choose the visualization that best communicates what you see in your data. By the end of this course, you'll know the basic techniques that will help you turn raw facts and figures into visualizations that tell a story and make an impact. Before beginning this course, you should be familiar with basic Excel or spreadsheet functions, as well as common charts and graphics. I hope you'll join me on this journey to learn data visualization with the Designing Data Visualizations course here, at Pluralsight.