There is an old saying that a person remembers what he sees better than what he has heard or read. This is because they can see the stuff, visualize it, comprehend it in a better way according to their mental ability, and understand it well. And same is the case for data visualization in data science as well.
When you see the insights represented in a beautiful and interactive manner, you can easily understand them as compared to the insights derived in a textual, or tabular form. Therefore, data visualization is widely used in all data science projects to effectively communicate the findings in an easy-to-understand manner to the stakeholders.
Well, this is not the only thing data visualization is used for. It offers several other benefits as well that we will discuss in further sections. We will also learn about various types of data visualization, how to choose the right type and some popular data visualization tools.
Everything about Data Visualization
· What is it?
Data Visualization in Data Science is an effective method of converting complex information into easy-to-understand visuals. This makes communication of insights easier. Data visualization is also used widely by data scientists for data exploration, identifying trends, and making predictions.
· Different types of data visualization
There are different ways to represent data and each of these data visualization techniques has its own strengths and weakness. Below mentioned are some different types of data visualization:
- Bar charts
- Line charts
- Pie charts
- Scatter plots
- Heat maps
- Dashboards
- Infographics
· How to choose the right form of data visualization?
Depending upon the type of data sets and what you want to represent in the graphics, you will have to select the suitable form. You also need to consider the available tools and resources, who the audience is, the complexity of the findings, and the level of detail you want to show in your data visualization.
The data visualization can be enhanced by using clear and concise labels, consistent colors and fonts, and avoiding too much clutter.
What is data visualization used?
- Identification of patterns and trends
There are chances that many patterns and trends might be missed when exploring tables and charts of numbers. However, with the use of data visualization, the patterns can be accurately represented in the form of visuals. This helps decision-makers analyze the insights in a better way. It helps to spot seasonal sales trends, identify anomalies, recognize relations between variables, and many other things.
- Hypothesis testing and validation
Data visualization is also an important tool for conducting hypothesis testing and validation in data science. The data scientists can use the data visualization to test it against the data and the hypothesis they have created. Since visual representation can help to compare the distribution of actual data, it can help in validating or refining the hypothesis easier ultimately increasing the accuracy and reliability of data-driven decisions.
- Data exploration and preprocessing
Data exploration and preprocessing are the important steps to follow in the data science workflow. After this step, only the advanced data analysis and modeling can be performed. With the help of data visualization, the distribution of data can be well understood. It also helps in detecting outliers and assessing the quality of data. It can help to identify missing values, irregular distribution, and other factors that are important to note down before diving deep into analysis.
- Decision support
Data science has become the backbone of many industries that rely on data-driven decision-making processes. The decision-makers use the insights gained from the data science process related to marketing strategies, resource allocation, operations management, etc. And data visualization helps the decision makers to easily understand and convey the insights to the stakeholders.
- Real-time monitoring
Out of several advantages, data visualization can also help in real-time monitoring as well. In the fields like cybersecurity, finance, healthcare, etc., real-time data analysis is very important. With the help of data visualization tools, the concerned professionals can monitor the KPIs as they can easily comprehend the insights and respond promptly which otherwise would take time in reading reports, charts, or tables.
Conclusion
Data visualization is not just another process in the data science workflow, but it is an integral part of the entire data science project. Because of its ability to convert complex and unstructured information into easy-to-understand visuals, it has entirely changed the game in the field of data science. It helps to save time in analyzing the reports, makes identifying the trends and patterns easy and effortless, and also makes communication with other professionals, technical and non-technical, associated with the project easier. So, if you are going for a career in data science, you must pay due attention to learning how to perform data visualization. You must master the tools and techniques to perform this art.