Photosynthesis

• Oxygen is produced as a byproduct of these reactions.
• 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.
• 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.
• 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.
• Daniel Kahn and Amos Tversky collaborated on research that defined two different methods for gathering and processing information.
• System 1 focuses on thought processing that is fast, automatic and unconscious.
• 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.
• System 2 focuses on slow, logical, calculating and infrequent thought processing.
• 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.
• Logistics use cases for data visualization include determining the best global shipping routes.
• Healthcare use cases for data visualization include choropleth maps that display important health data.
• 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.
• 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.
• Politics use cases for data visualization include geographic maps that display the party each state or district voted for.
• Scientific visualization, also known as SciVis, allows scientists and researchers to gain greater insight from their experimental data than ever before.
• Treemaps are best used when multiple categories are present, and the goal is to compare different parts of a whole.
• Data scientists and researchers use visualizations to understand data sets and identify patterns and trends that would have otherwise gone unnoticed.
• Common uses of data visualization include sales and marketing, politics, healthcare, science, finance, logistics, and data science.
• A scatter plot takes the form of an x- and y-axis with dots to represent data points.
• Sales and marketing use cases for data visualization include tracking web traffic and understanding how marketing efforts affect traffic trends over time.
• The science of data visualization comes from an understanding of how humans gather and process information.
• 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.
• 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.
• The increased popularity of big data and data analysis projects have made visualization more important than ever.
• 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 ability to act on findings quickly and achieve success with greater speed and fewer mistakes.
• To get the most out of big data visualization tools, a visualization specialist must be hired.
• It is essential to have people and processes in place to govern and control the quality of corporate data, metadata and data sources.
• Data visualization increases understanding of the next steps that must be taken to improve the organization.
• Data visualization improves ability to maintain audience's interest with information they can understand.
• The insights provided by big data visualization will only be as accurate as the information being visualized.
• Data visualization allows for quick absorption of information, improved insights, and faster decision making.
• Visualization offers a means to speed up data analysis and present information to business owners and stakeholders in ways they can understand.
• 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.