Data Visualization Or Visualization Data

Data Visualization or Data Visualization is a form of graphic presentation made with the aim of providing information from data or the results of analysis carried out based on the data provided. Data Visualization or Data Visualization in its use is very helpful for its users, especially someone who works with and manages data such as a Data Scientist and Data Analyst to understand patterns, trends, relationships, and outliers hidden in a large and complex data in a given dataset. Usually in the end result Data Visualization can be represented in various types of forms such as graphs, maps, diagrams, and others. Data visualization is one of the important steps or techniques used in analyzing data in data science, especially for someone who wants a career in the field of data such as Data Scientist and Data Analyst.

Data visualization can help someone understand data in various types of patterns and trends located in a complex, large, and many datasets. In data visualization, a Data Scientist and Data Analyst can choose various types of graphs or charts that match the type of data owned. Some types of charts that are usually used to do data visualization or data visualization include the following, bar charts, line charts, scatter plots, and pie charts

The tools or tools that are usually used to help a Data Scientist and Data Analyst in visualizing data include Tableu, Microsoft Excel, Google Data Studio, Microsoft Power Business Intelligence (BI), and so on.

*For example, so that we can understand the use of tools in visualizing data so that it is easier to do, here is an example of an explanation of the introduction of Microsoft Power Business Intelligence (BI) to visualize data in the form of learning videos from MySkill, let's listen together to help us better understand it: https://myskill.id/topic/microsoft-power-bi*

Benefits of Data Visualization

1. Better Understanding of Data: One of the main goals of data visualization is to help understand the structure of data and its various characteristics better than just looking at tables or numbers. Data visualizations such as graphs, plots, and diagrams can summarize data in a more efficient way making it easier to understand.
2. Identify Patterns and Trends that are happening: Visualization allows a Data Scientist and Data Analyst to quickly perform and identify patterns or trends that are happening. This certainly helps in making a more appropriate decision.
3. Information Communication: Graphs and data visualization are very useful tools for effectively communicating information to others. Of course, this allows a Data Scientist and Data Analyst to explain the results of data visualization to colleagues, management, or other stakeholders in a more efficient way.
4. Detecting an Anomaly: Data visualization can easily help in detecting an anomaly or unusual data that may need further analysis. For example, such as visualizing data in the form of plot outliers which is certainly useful in this case.
5. Helps with Comparison and Evaluation: By visualizing data a Data Scientist and Data Analyst can easily compare various aspects of data, such as comparisons between time, location, or other parameters that are commonly used and found in datasets. This certainly helps in conducting evaluations and comparisons so that it can produce a better understanding.
6. Identify Opportunities: Data visualization can help a Data Scientist and Data Analyst identify business opportunities, market trends, or areas where improvements are needed based on the dataset provided. This allows a data scientist and data analyst to take more proactive actions.
7. Real-Time Supervision: A Data Scientist and Data Analyst who assist in managing data in applications involving real-time data, will usually utilize data visualization that allows better monitoring and understanding of the current situation, such as in the management of Information Technology infrastructure or the timing of events.
8. Assist in decision making: By visualizing data, a Data Scientist and Data Analyst can make and produce a better and factual decision (because it is based on data / based on data), so that a Data Scientist and Data Analyst can improve the quality of business decisions or other types of decisions.