What is Data Visualization?Data visualization is a graphical representation of data. It presents data as an image or graphic to make it easier to identify patterns and understand difficult concepts. Technology allows users to interact with the data by changing the parameters to see more detail and create new insights. Show
The Importance of Data VisualizationWhy is data visualization important? Data visualization is an effective way to universally share complex concepts that may otherwise be difficult to convey. For example:
How Does Data Visualization Work?If you’ve ever looked at a pie, bar or line chart, you’ve seen data visualization examples. Without this visual way to see data and abstract its patterns, the data itself could be incomprehensible — just a myriad of numbers. If seeing is believing, understanding how data visualization works starts with how our eyes and brain inform us. In everyday life, we’re able to interpret our surroundings instantaneously just by looking around. Our brain constructs metaphors to help us understand concepts. Likewise, data visualization uses metaphors. Consider how a pie chart shows the relationship of a piece to the whole “pie.” A line chart describes continuity and a bar chart shows us categories. The greatest power of data visualization comes from its ability to illustrate things we normally couldn’t see — like a bird’s-eye view. Data visualization helps us see patterns and order in an otherwise chaotic-looking array of digits. How Does Visualizing Data Improve Decision Making?Data visualization, similar to data science and data exploration, can improve a company’s decision making by helping it discover potential patterns in the data and make more data-driven decisions. Analyze lots of data at one time
Find patterns
Find correlations between business operations
Identify upcoming trends
Better collaboration
Who is a Data Visualization User?There are three kinds of users of data visualization: companies, individuals and academics. Companies use it to analyze performance and adjust their internal processes. Individuals use data visualization to turn data into information and communicate insights to others. Academics uses data visualization to assist in the pursuit of knowledge. Even if an academic’s visualizations aren’t seen by the public, they guide research for information that they ultimately publish. In companies, users of data visualization include: C-level executives
Sales teams
Marketing teams
Customer service teams
Human resources teams
What is No-Code Data Visualization?Data analysis is no longer the exclusive domain of data scientists. It has been democratized, being used by CEOs, sales representatives, marketing strategists, policymakers. Everyone knows that data analytics can provide actionable, fact-driven insights. Squeezing those insights from huge, heterogeneous, ever growing datasets is another matter. Extracting useful visualizations from modern datasets still requires the efforts of technical specialists, data experts, and computer programmers. Before the data is presented in a visual form, it runs through a ‘data pipeline’, a system of infrastructure components, tools, scripts and programs. These data pipelines form the backend of business intelligence (BI) tools and systems. The frontend of the system is the user interface, where data is presented to users as a visual dashboard. The mismatch between backend and frontend results in user experiences that are usually slow, non-interactive, and often inhibit spontaneous exploration and discovery. No-code data visualization eliminates this inefficiency, enabling non-technical users to run scenarios without competing for scarce data science skills. No-code analytics enable business users, who may not know how to write code or even use a command-line interface, to construct rich, interactive visualizations across large, often fused datasets. Business users may not know specifically what they are looking for--insights can emerge simply through ‘free-swim’, formulating and testing hypotheses in real-time. No-code data visualization helps all users in an organization easily tap into the wealth of insights waiting to be found in the data. What are Some Data Visualization Techniques?The basic data visualization techniques include: Relationship (scatter plot)
Timeframe (line graph)
Composition (pie chart)
Comparisons (bar chart)
Interactive maps
OmniSci has developed a number of advance data visualization techniques that include: Multisource Dashboards
Multilayer Geo Charts
Geospatial Context
Cross Filtering
Data Visualization Examples:
What is Big Data Visualization?Big data and data visualization rely heavily on each other in order to uncover the meaningful insights about trends, correlations and patterns that exist within big data - otherwise known as big data analytics. Big data visualization tools like HeavyImmerse enable analysts and data scientists to easily visualize and interact with massive datasets so that analytics insights can be uncovered in a variety of graphical formats. How is Machine Learning used in Data Visualization?Data visualization can better depict and explain algorithms common in machine learning — such as the decision tree and the neural network. This allows users to see a neural network in action in ways that a written or verbal description could never capture. Machine learning is considered hyper-intensive programming, which can be difficult to execute and comprehend. Data viz helps the human eye and mind better appreciate and fathom how a machine learning algorithm works. What is a Data Visualization Engineer?A data visualization engineer works collaboratively with data scientists, businesses and other software engineers to create dynamic data visualizations to help clients make more informed decisions based on a variety of data. A Brief History of Data VisualizationThe history of data visualization goes back to prehistoric rock carvings called petroglyphs. Using pictures to tell a story about data is as old as human existence. Data visualization became a profession for map makers in the 1600s. The pie chart first appeared in the early 1800s. Charles Minard took data visualization to a new level in 1869 when he created a groundbreaking statistical graphic that mapped Napoleon’s 1812-1813 invasion and retreat from Russia. The chart simultaneously shows geography, time, temperature, number of troops and direction of the army to illustrate how and why the campaign ended in disaster. Minard’s graphic map sparked a “golden age” of data visualization techniques that lasted until the early 1900s. The first half of the 20th Century didn’t see much data driven graphic innovation. Statistical models became the rage, using exact numbers without use of visuals. But the revolution in computers in the 1960s led to new ideas about how to display data. The rapid growth of computing power and data collection from the 1970s onward took Minard’s hand-drawn concepts past limits that were imaginable in his time — like visualizing the entire human genome. Massive amounts of data can now be shown in milliseconds as data visualization software brings together data scientists and artists to change how we look at the world. The data visualization renaissance of the late 20th and early 21st Centuries included pioneers like David McCandless, founder of Information Is Beautiful, and Jesse Thomas, CEO of Jess3. In 2011, Todd Mostak helped solve the problem of quickly visualizing vast amounts of data. Mostak was studying the role of social media in the Arab Spring for his thesis at Harvard. The research required looking at millions of tweets, and tying them to a specific location. Each time Mostak ran a query on the giant data set, it would take many hours to get a result. Often, he had to let it run overnight. This problem caused Mostak to wonder: “What if I created my own query engine that uses the massive parallelism of GPU cards to accelerate the queries and visualize the results?" Mostak eventually created software at MIT’s Computer Science and Artificial Intelligence Laboratory, which eventually became the OmniSci analytics platform. Mostak initially called his company MapD Technologies, and then OmniSci - and now HEAVY.AI. Now with HEAVY.AI, it focuses on pushing the limits of scale and speed in big data analytics to let users visually explore data at the speed of thought. The next innovation wave in data visualization techniques will combine the objectives and methods of machine learning, interactive visualization and business intelligence. The concept of business intelligence was popularized in a 1958 article by IBM computer scientist Hans Peter Luhn. He described a “business intelligence system” that was “an automatic system…developed to disseminate information to the various sections of any industrial, scientific, or government organization.” Luhn’s business intelligence (BI) concept became more feasible with the growth of computing power, driven by Moore’s Law on the density of integrated circuits and the software developed to harness that power. This led to BI that could not only produce data and reports but organize and visualize it. Today’s BI dashboards, however, are very different from HEAVY.AI technology. While a BI dashboard can query and visualize tens of millions of rows of data, HEAVY.AI's real-time data visualizations are able to do the same with billions of records, with far faster performance. Michael Friendly, of York University in Toronto, wrote a paper called “A Brief History of Data Visualization” that outlined the greatest developments in data visualization examples and techniques of the past 30 years:
Friendly also noted the most important advances in theoretical and technological infrastructure that allowed for the creation of new visualization methods:
What is Data Visualization Software?Data visualization software creates the dashboards that allow for easy interpretation of data, trends and key performance indicators (KPIs). With data visualization software at their disposal, users can build visuals such as charts, graphs and maps that track and measure metrics. Data visualization software benefits:
What is Scientific Data Visualization?Scientific data visualization, also known as scientific visualization, is a subset of data visualization that refers to the process of representing raw, scientific data as images, providing an external aid to improve scientists’ interpretations of large data sets and to gain insights that may be overlooked by statistical methods alone. Explore Interactive Visualization via DemoGet hands-on with HEAVY.AI's Interactive Data Visualization Demo and imagine the critical insights your team could uncover using your own data. What is most important when creating a visualization?What 2 things are most important in creating elegant visualizations? Focus on what is relevant. Remove anything which isn't adding to the figure. Use a unique style.
Which feature would you use to represent numeric data visually in Excel?Charts are used to display series of numeric data in a graphical format to make it easier to understand large quantities of data and the relationship between different series of data. Excel supports many types of charts to help you display data in ways that are meaningful to your audience.
How do you make data interesting?Writing about data: 3 ways to make it more interesting and.... Use real images. Visual pictures are the friend of the data writer. ... . Write short sentences and paragraphs. Today's writing is meant to be digested on phones and tablets. ... . Infographics. Infographics may cause you to dispense with writing altogether.. What tool in Excel allows you to rearrange and organize data based on categories?Click on the "Data" tab in your toolbar and look for the "Sort" option on the left. Click on the small arrow to the left of the "A to Z" Sort icon. Then, select "Custom Sort" from the menu.
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