multiple choice

• Data visualization is the practice of translating information into a visual context, such as a map or graph, to make data easier for the human brain to understand and pull insights from.
• The main goal of data visualization is to make it easier to identify patterns, trends and outliers in large data sets.
• Data visualization is often used interchangeably with others, including information graphics, information visualization and statistical graphics.
• Data visualization is one of the steps of the data science process, which states that after data has been collected, processed and modeled, it must be visualized for conclusions to be made.
• System 2 focuses on slow, logical, calculating and infrequent thought processing.
• This method is frequently used in day-to-day life and helps accomplish tasks such as reading the text on a sign, solving simple math problems like 1+1, identifying where a sound is coming from, riding a bike, and determining the difference between colors.
• System 1 focuses on thought processing that is fast, automatic and unconscious.
• Daniel Kahn and Amos Tversky collaborated on research that defined two different methods for gathering and processing information.
• This method is used in situations such as reciting a phone number, solving complex math problems like 132 x 154, determining the difference in meaning between multiple signs standing side by side, and understanding complex social cues.
• Data visualization is also an element of the broader data presentation architecture (DPA) discipline, which aims to identify, locate, manipulate, format and deliver data in the most efficient way possible.
• Data visualization is important for almost every career, including teachers, computer scientists, executives, and businesses involved in big data projects.
• Visualization tools are central to advanced analytics for similar reasons, as data scientists need to visualize the outputs of advanced predictive analytics or machine learning (ML) algorithms to monitor results and ensure that models are performing as intended.
• Data visualization provides a quick and effective way to communicate information in a universal manner using visual information.
• Data visualization can help businesses identify which factors affect customer behavior, pinpoint areas that need to be improved or need more attention, make data more memorable for stakeholders, understand when and where to place specific products, and predict sales volumes.
• Sales and marketing use cases for data visualization include tracking web traffic and understanding how marketing efforts affect traffic trends over time.
• Politics use cases for data visualization include geographic maps that display the party each state or district voted for.
• Data scientists and researchers use visualizations to understand data sets and identify patterns and trends that would have otherwise gone unnoticed.
• Finance use cases for data visualization include tracking the performance of investment decisions when choosing to buy or sell an asset, using candlestick charts as trading tools.
• Common uses of data visualization include sales and marketing, politics, healthcare, science, finance, logistics, and data science.
• Scientific visualization, also known as SciVis, allows scientists and researchers to gain greater insight from their experimental data than ever before.
• Logistics use cases for data visualization include determining the best global shipping routes.
• Treemaps are best used when multiple categories are present, and the goal is to compare different parts of a whole.
• Treemaps show hierarchical data in a nested format, with the size of the rectangles used for each category being proportional to its percentage of the whole.
• A variation of a line chart, this technique displays multiple values in a time series or a sequence of data collected at consecutive, equally spaced points in time.
• The science of data visualization comes from an understanding of how humans gather and process information.
• Population pyramids use a stacked bar graph to display the complex social narrative of a population, and are best used when trying to display the distribution of a population.
• A scatter plot takes the form of an x- and y-axis with dots to represent data points.
• Healthcare use cases for data visualization include choropleth maps that display important health data.
• Data visualization improves the ability to maintain the audience's interest with information they can understand.
• Data visualization facilitates easy distribution of information that increases the opportunity to share insights with everyone involved.
• Big data visualization often goes beyond the typical techniques used in normal visualization, such as pie charts, histograms and corporate graphs.
• Companies are increasingly using machine learning to gather massive amounts of data that can be difficult and slow to sort through, comprehend and explain.
• Data visualization increases understanding of the next steps that must be taken to improve the organization.
• Big data visualization projects often require involvement from IT, as well as management, since the visualization of big data requires powerful computer hardware, efficient storage systems and even a move to the cloud.
• The increased popularity of big data and data analysis projects have made visualization more important than ever.
• Big data visualization requires powerful computer systems to collect raw data, process it and turn it into graphical representations that humans can use to quickly draw insights.
• It is essential to have people and processes in place to govern and control the quality of corporate data, metadata and data sources.
• To get the most out of big data visualization tools, a visualization specialist must be hired.
• While these visualization methods are still commonly used, more intricate techniques are now available, including infographics, bubble clouds, bullet graphs, heat maps, fever charts and time series charts.
• Data visualization increases the ability to act on findings quickly and, therefore, achieves success with greater speed and fewer mistakes.