HomeBlogBusiness intelligence and VisualizationMost Interesting Data Visualization Projects in 2023
Data visualization has made a long journey, from the simple cave drawings showing a successful hunt to the present day's intricate dashboards to present raw data understandably. Before the seventeenth century, data visualization existed mainly in maps, displaying land markers, cities, roads, and resources. With the growing demand for more accurate mapping and physical measurement, better visualizations were needed, which we find in our latest innovation in visualization techniques and tools. The present article will discuss in detail the importance of data visualization, tools, use cases and various data visualization project ideas for different levels of participants
Data visualization is transforming data or information into graphics to make it easier for the human brain to comprehend and get insights. The purpose of data visualization projects is to identify patterns, trends, and anomalies or deviations in large datasets/big data (the main data for visualization projects); that otherwise would have been impossible. This is the final step of the data science process and data presentation architecture (DPA) discipline that finally leads to deriving insights from the visualized presentation to be used for efficient understanding, making business decisions, or drawing conclusions.
Various tools like Tableau, Grafana, Chartist, FusionCharts, Google Charts, Infogram, ChartBlocks to name some very popular ones besides plenty of others are used in any data viz project. Tableau is perhaps the most popular of the lot or most widely used, not only for its scalability and speed but gives simple to very complex visualization choices with tons of customization options. People learn this from Data Visualization with Tableau Course.
In fact, there are multiple ways of representing data visually, some use classic methods while others adopt modern technologies.
“Inception”, an American film (directed by Christopher Nolan, released in 2010) could be cited as one of the best data visualization project examples to be mentioned. It uses a very simple yet colorful and interactive data visualization process that offers a detailed explanation of the film’s complex plot! We will mention some more sample data visualization projects later on in this article.
Professionals from this sector often use choropleth maps (these display divided geographical areas/ regions that are assigned a certain color to a numeric variable), to visualize important health data to notice how a variable, for example, the mortality rate of heart disease, changes across specific territories.
Data visualization projects help scientists and researchers to gain greater insight from their experimental data efficiently and quickly.
Finance professionals need to understand the performance of their investment decisions for which they supply datasets for data visualization projects to analysts. Candlestick charts are typically used as trading tools for the purpose of analyzing price movements over time or displaying important information on securities, derivatives, currencies, stocks, bonds and commodities. By seeing the visual representation of how prices change over time, future trends can be detected.
Programming Languages Used for Data Science Visualization Projects
Data Visualization Tools
Businesses or many departments use data visualization software to track their own activities or projects. The marketing department for example of any company may use such software visualization tools to monitor the performance of its marketing campaigns, track metrics like open rate, click-through rate and conversion rate etc.
The most popular tool names include Tableau, QlikView, Microsoft Power BI, TIBCO Spotfire, Sisense BI. Those interested could learn them by attending Business Intelligence Courses.
Most careers nowadays need data visualization. For example, it can be used by teachers to show test results, computer scientists/data scientists in the field of artificial intelligence or business analysis; researchers, scientists even political parties use data visualization. Businesses need them regularly to understand market trends, competitor and customer behavior to get actionable insights from the huge volumes of data they collect. The reason is, visualizing complex algorithms is a lot easier to understand than numerical outputs.
Here is a run-through of the uses of projects on data visualization:
This section will be especially useful to those who are willing to enter into a career in data visualization projects. The steps of the data visualization process include:
A clear understanding of the goal of the research and the topics of research is required because a lack of clarity here will lead to erroneous results finally. One cannot be correct unless one is aware of exactly what one is looking for.
For collecting data for a data visualization project, locating the correct data sources is crucial as it is collecting the right data that would be relevant to the purpose of the project. Plenty of resources are available to collect data from, including internal or external sources. Depending on the project goal and purpose, past or historical data should also be considered.
Not all collected data would be equal or useful or relevant either. There could be redundant data, incomplete data, or erroneous data. Therefore, refinement of the collected data is required, which is also called data cleaning, to get rid of unnecessary data. For this, data parameters should be set which are appropriate to find the correct outcome. Data not fitting into those parameters should be discarded.
The steps of data cleaning are:
Clarity of how different variables depend on each other will be needed to determine the type of visualization best suited for visualizing the data. The following questions might help in this:
Many software tools are available that can be used for visualization purposes. For example, Tableau and Microsoft Power BI are quite popular. After basic visualization is done, necessary adjustments or changes can be made to arrive at the correct and final outcome.
We are discussing some cool data visualization projects for students or beginners.
The easiest to work with, especially for students or beginners. As described in the chart, each value point (vertical line reference) that is plotted on the graph connects with the next point and thus a trend is seen over the horizontal time reference.
For practice, you can start off with the Spotify music dataset.
A Bar graph is the most common data visualization medium as most of the plotted data takes the shape of a Bar graph and is very intuitive and versatile to visualize for any categorical data. For your practice, you can use the Kaggle Digimon Database.
This looks somewhat similar to the bar graph discussed above, but there are differences, and we will cover that. Histograms can be used to visualize continuous or discrete numerical data that falls within a specified category and are very useful when plotting a large number of data and calculating the frequency of each data.
The chief dissimilarities between a bar chart and a histogram are:
| Bar Graph | Histogram |
|---|---|
| Equal space between two consecutive bars. | No space, all bars are attached to each other. |
| The X-axis can represent anything. | The X-axis should represent only continuous and numerical data only. |
The similarity: in both the graphs, the y-axis represents numbers alone.
A box plot data visualization uses boxes and lines to display the distributions of one or more groups of numeric data.
This section will explore data visualization projects that go beyond the basic level and discuss more interesting data visualization projects that use creative data visualization styles and more complex features.
Heatmaps offer an unparalleled option to identify problem areas or areas of interest using colors that are easier to distinguish and comprehend than numeric values. Due to this, they find extensive use in anything that matters, from analysis of shopping patterns and population maps to flight delays and hordes of other events.
The flights.csv of the 2015 Flight Delays and Cancellations dataset on Kaggle will be excellent for your practice.
This is a fantastic option to capture visitors’ or audiences’ attention and can be used anywhere, including a website homepage, making infographics more appealing by including interactive features and many other places. Python, the open-source, free-to-use, is used here. The libraries like Matplotlib, Seaborn and Plotly made Python a great tool for data analysis.
Sunburst charts also known as Ring Charts or Radial Treemap are used to represent hierarchical data. Interactive features can be incorporated here and these charts are quite easy to understand and nicely explanatory.
You can use dataset on Amazon's Top 50 bestselling books from 2009 to 2019 which contains 550 books with data categorized into fiction and non-fiction using Goodreads for practice.
As the name suggests, these visualizations are typically used to plot the parameters over time. Including interactive features is especially useful a) while plotting a long period or b) to observe the changing trends in detail by zooming etc.
For practice you can use the Temperature Time-Series for some Brazilian cities Kaggle dataset.
Here are a couple of bonus data visualization project ideas that perhaps you can use as your data visualization final project:
The picture was the first language of prehistoric men. Since then, our brains quickly capture what we see, especially colors and patterns (in place of the bulky and boring statistical data), and process visuals and pictures fast. The maps, charts, graphs, etc., that data visualization includes help to deliver the information very effectively and fast no matter what kind of data is used or irrespective of the business or industry types.
It is almost impossible to decipher correct knowledge and derive insight from the trillions of arbitrary data or information without data visualization.
Data visualization projects work with a huge amount of information and data set, not just only 4 or 5 pieces of information. Hence it has its challenges. Some of these are:
Since data is the main raw material of any data visualization project, improper or erroneous data selection will lead to incorrect visualization. Though it sounds easy, data accuracy could be a tough task if a project deals with huge volumes of data or big data.
Proper grouping of data could be a huge challenge, especially when dealing with volumes of data arriving from different sources and in different formats.
The right choice of graphs, color or even charts could be a factor because effective grasping by the brain happens if the visual UX (user experience) is proper. If this is not done correctly, the end users might get confused. The challenge is to keep the design clean and minimalist and avoid eye movements back and forth.
End users (for example, CEOs or other stakeholders) are often not directly engaged while defining needs for visualization projects. Either they are not always available or often ignore the importance of the data visualization project with an assumption that others would cover them. Whereas it is extremely critical to include the end users in work and gather their business perspectives so that those can be incorporated into the visualization project. This is truer for large projects dealing with big data.
A visualization project might have various competing options. Often the end users develop a sense of insecurity or the fear of missing or something going unaddressed, when asked to prioritize. This is often seen in the case of the first visualization project of a company, where the urge becomes to fit an all-in-one basket.
We discussed one example at the beginning of this article. Here are some cool data visualization projects.
Following is a fun example of gastronomy in pictures, that uses data visualization relating food and wine.
It is not necessary to have static data visualization. Videos can also be used for this purpose. Here is a link to visualize the things you need to know about the planet earth.
Some more data visualization examples can be seen from the application (shows the number of women who work in various different fields) developed by Toucan Toco: Toucan Cocotte.
As the name suggests real-time data visualization makes it possible to update charts and graphs in real-time. Many big data visualization tools like Lumify, Apache SAMOA,Tableau offer a dashboard with data visualization in real-time.
The advantage is, it allows everyone to see how several data sets are related to one another, allowing people to quickly identify and extract patterns and trends. Otherwise, those might have been hidden in raw data or would have been difficult to comprehend otherwise
Increased use of big data and data analysis by businesses and governments across the globe has made the data visualization process gain more traction. Ultimately visualization is what gives meaning to data; makes it easy to understand, explain and evolve further ahead by making intelligent and fast decisions with the help of insights received from the visualized data.
Big data visualization has evolved further going beyond the typical techniques used in usual visualization like histograms, pie charts, and corporate graphs. It uses more complex techniques like fever charts and heat maps, and they are going to evolve further offering more features to slice and dice data. Because with limited tools and techniques, it is easy to start valuing what can be measured instead of measuring what is valuable.
To create visual stories from data for easy understanding of trends, making faster decisions.
Python is the ideal choice.
Most definitely. It is an awesome tool for managing large datasets.
Visually-diagrammatic presentation is the best.
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