Hey there, data enthusiasts (or maybe you’re just curious)!
Picture this: you’re drowning in numbers, spreadsheets overflowing, charts looking like alien hieroglyphics. You know there’s gold in there, valuable insights about your business, but how do you dig it out without getting buried alive in all this data?
In this blog, we’ve covered the benefits of data visualization Services, their uses for businesses, and a step-by-step data visualization process.
Data visualization is a method that uses static and interactive visuals to help people understand the large amount of data being collected. Data visualization is an important skill in applied statistics and machine learning. With some prior domain knowledge, storytelling with data will be helpful and insightful to you and your audience.
There are many benefits of data visualization that we talk about in this blog so stay tuned!
Due to complexity and large amounts, simply understanding data in numeric format can be challenging for anyone.
That’s where the importance and benefits of data visualization come in! By transforming numerical data into easy-to-digest visuals, data visualizations (or as we call it, “storytelling with data”) unlock valuable insights.
Data visualization is a potent tool for gleaning profound insights from complex data, informing impactful decisions, and fostering engagement across diverse audiences.
Understanding the benefits of data visualization:
So when you think of the benefits of data visualization, these should come to your mind 😎
Python has evolved extensively in every field, be it Automation, Machine Learning, Testing, Scraping, etc.
One of the benefits of data visualization with Python is that there are many visualization tools/libraries that provide excellent features and are easy to implement. It includes support for all types of visual, live, customized charts.
Worth mentioning, below are some of the most used Python libraries for data visualization:
The first and most important step of data visualization is gathering data in large amounts. Only after we have substantial data can we apply data visualization techniques to the collected data and get some helpful insights from it.
Data cleaning is an essential step to perform before creating a visualization. A bunch of data out of a large dataset that has inappropriate, empty, or false values may lead to adding erroneous visuals with anomalies in it.
The output received from a data cleaning process is usually a dataset that is free of errors and anomalies etc, which gives much more accuracy when data is processed. Data cleaning is pretty much dependent on the dataset domain that you’re working with.
Before choosing a visual chart or graph, it is important to understand your audience and choose a chart or graph that will best communicate the message.
Choosing a chart depends on what findings you need to convey to your audience.
Choosing a couple of these can help to select the charts that will be best suitable for you. This usually requires some playing around with different charts before choosing the best. These selections lead to showing that the benefits of data visualization are expansive.
To prepare the data before sending it further for visualization, determine the type of graph, chart, or any other visualizations you need to create and the supporting library you will be integrating for it. After the chart is finalized, it may be necessary to transform the data as per requirements.
Data preparation tasks include finding data columns that help make some decisions, giving meaningful insights about data, grouping data, creating aggregate values for groups, combining variables to create new columns, etc.
In the final step, you’ll have the required data to create visualizations. Now we apply visualization skills to the prepared data and represent the data in charts or graphs with meaningful insights. After we’re finished, we’ll present it to you so you can see what we’ve discovered!
This way, storytelling with data helps to put forth your understanding of the data in a simple & visual format.
Now that we understand how the data visualization process works, we can apply different data visualization types to their uses. As mentioned in the earlier section, by using those visualization libraries, we can create some visualizations as follows:
Line charts are used to display trends over time. The X-axis usually represents a period, and the Y-axis represents the quantity associated with the period on the X-axis. For example, an A-line chart can illustrate a shopping mall’s peak visit time for the day broken down by weekdays and hours.
An area chart is a line chart with the areas below the lines filled with colors. Use a stacked area chart to display each value’s contribution to a total over some time.
A bar chart also displays trends over time. In the case of multiple variables, a bar chart can make it easier to compare the data for each variable at every moment. E.g., a bar chart can compare the company’s growth year-wise.
A histogram represents data using bars of different heights. Usually, each bar groups numbers into ranges in a histogram. The taller the bars, the more data falls in that range.
It displays the shape and spread of continuous data set samples. E.g., we can use a histogram to measure each answer’s frequency in a survey question. The bars would be the answer: “bad,” “good,” and “best.”
Scatter plots are used when there is a need to find correlations. If a data XY exists, then a Scatter plot is used to find the relationship between variables X and Y.
The bubble chart evolved from a scatter plot. Unlike scatter plots, each data point is assigned a label or category and shown as a bubble. It is used to show and compare the relationship between the labeled circles. A bubble chart makes it hard to read with multiple bubbles, so it has a limited data set size capacity.
A pie chart is a circular graph representing the data set in which the pie slices are divided to represent a numeric proportion. Pie charts are used when there is a need to show the contribution of a data point inside a whole data set.
A gauge chart is evolved from a pie chart and doughnut chart. It is used to visualize the distance between intervals. Multiple gauge charts can be shown linearly to visualize the difference between multiple intervals.
Most of the data collected have a location variable, which makes it easy to plot on a map. , e.g., a map visualization is mapping the number of customers worldwide, country-wise, where each country would represent several customers. Location information can help businesses grow in a region where the business has not scattered compared to other regions.
A heat map is a visualization tool that uses color, like a bar chart’s height and width. Two dimensions are shown as the magnitude of a phenomenon. The heat map can identify whether the phenomenon is clustered or varies over space.
These show the flow of data between different categories. For example, this Sankey diagram shows the flow of migrants between different countries
Tracking COVID-19 Cases: Interactive maps showing the spread of COVID-19 cases by location, often color-coded based on severity, help people understand the pandemic’s geographical impact and guide public health decisions. (Example: Johns Hopkins Coronavirus Resource Center)
Financial Market Performance: Line charts and heatmaps display stock prices, market fluctuations, and investment opportunities, aiding financial analysts and investors in making informed decisions.
Streaming Service Recommendations: Streaming services like Netflix and Spotify use data visualizations to personalize recommendations. Scatter plots analyze your viewing history to identify preferred genres and actors. Bar charts highlight trending movies and shows, while heat maps suggest similar content based on your watchlist.
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Interested in harnessing the power of specialized tools for data visualization? Learn more about how you can leverage data visualization and the benefits of data visualization with tools like Power BI in our article on Data Visualization with Power BI.
Sandeep is a highly experienced Python Developer with 15+ years of work experience developing heterogeneous systems in the IT sector. He is an expert in building integrated web applications using Java and Python. With a background in data analytics. Sandeep has a knack for translating complex datasets into meaningful insights, and his passion lies in interpreting the data and providing valuable predictions with a good eye for detail.
Data visualization is the formal practice of translating complex data into readily interpretable visual representations.
Charts, graphs, and maps transform complex information into an accessible format, facilitating analysis, communication, and data-driven decision-making across various domains. This leads to storytelling with data which helps decision-makers make tough decisions more easily.
Data visualization is a potent tool for gleaning profound insights from complex data, informing impactful decisions, and fostering engagement across diverse audiences.
Why is it crucial in data visualization services?
The benefits of data visualization are:
The different types of data visualization charts are:
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