Designing Data Visualizations
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.
What you'll learn
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.
Table of contents
- Intro: Finding the Hidden Meaning in All the Numbers 6m
- Before You Start Telling Your Story ... 7m
- Avoiding the 4 Deadly Sins of Data Visualization 6m
- Performing an Exploratory Data Analysis (EDA) 8m
- Hyman's Maxim & Null Hypothesis: How Do We Know Our Data Is Real? 5m
- Performing a Null Hypothesis Reality Check 5m
- What Is Interpolation and When Should I Use It? 6m
- What Are the Risks Associated with Interpolation? 4m
- Hands-on: Basic Interpolation in Excel: Filling In the Blanks 6m
- Dealing with Interpolation Dangers 5m
- Hands-on: Quick and Dirty Interpolation in Excel 4m
- What Is Extrapolation and Why Does Everyone Like It So Much? 4m
- Faulty Forecasts: Logical Risks Inherent in Extrapolation 3m
- Hands On: Experimenting with Extrapolation In Excel 7m
- Outliers: Good, Bad, or Just Weird? 6m
- Hands-on: Detecting Outliers in Large Datasets 4m
- Introduction to Creating Enriched Datasets 3m
- Combining Multiple Data Series Using Merges 2m
- Hands-on: Performing a Data Merge 5m
- What Are Data Joins? Inner, Left, Right, Full Outer 3m
- Choosing the Right Type of Join for Our Data 7m
- Hands-on: Data Joins in Excel Using XLOOKUP 4m
- Choosing the Appropriate Visualization for the Context 4m
- Hands-on: Creating an Appropriate Visualization for Enriched Data 7m
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.
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.
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.
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.
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.
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.