For enquiries call:



HomeBlogBusiness intelligence and VisualizationTop Data Visualization Projects in 2024 [With Source Code]

Top Data Visualization Projects in 2024 [With Source Code]

09th May, 2024
view count loader
Read it in
15 Mins
In this article
    Top Data Visualization Projects in 2024 [With Source Code]

    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.

    What is Data Visualization Project?

    Data visualization transforms 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. You can learn about this further by going for Data Visualization with Tableau course.

     In fact, there are multiple ways of representing data visually, some use classic methods, while others adopt modern technologies. 

    List of Data Visualization Project Ideas

    Data Visualization Projects for Beginners

    We are discussing some cool data visualization projects for students or beginners. 

    1. Line graph

    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.

    2. Bar Graph

    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

    3. Histogram 

    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 GraphHistogram
    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 graphs, the y-axis represents numbers alone. 

    4. Box Plot Data 

    A box plot data visualization uses boxes and lines to display the distributions of one or more groups of numeric data.

    Data Visualization Projects for Intermediate Level

    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. 

    1. Heatmaps 

    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.

    2. Interactive Plots in Python with Plotly

    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. Libraries like Matplotlib, Seaborn and Plotly made Python a great tool for data analysis.

    Data Visualization Projects for Advanced Level

    1. Interactive Sunburst Charts

    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. 

    2. Interactive Time Series Visualization 

    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.

    Bonus Data Visualization Project Ideas

    Here are a couple of bonus data visualization project ideas that perhaps you can use as your data visualization final project: 

    1.  Microbial life represented as a heatmap
    2.  The covid-19 dataset 

    Data Visualization Project Examples

    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.

    1. Data Visualization Through Video

    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 fields) developed by Toucan Toco: Toucan Cocotte

    2. Real-time Data Visualization

    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.

    Steps to Build a Data Visualization Projects

    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: 

    1. Understanding the purpose behind the project
    2. Data collection
    3. Data refinement
    4. Selecting the right visualization medium
    5. Visualize

    1. Understanding the Purpose Behind the Project

    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 knows exactly what one is looking for.

    2. Data Collection

    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.

    3. Data Refinement

    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: 

    1. Eliminating all extra variables.
    2. Correcting any errors, like incomplete data.
    3. Standardizing all units.
    4. Eliminating blank spaces or missing information.
    5. Arranging the data logically and sequentially so that it is easy to visualize. 
    6. Grouping data in rows and columns or horizontally and vertically will help in data arrangement and also proper visualization. 

    4. Selecting the Right Visualization Medium

    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:

    1. How is one variable related to the other? 
    2. What sort of relationship exists between two different variables? 
    3. What kind of trend is the data following? 
    4. Can a dataset be divided into smaller parts? 

    5. Visualize

    Many software tools are available that can be used for visualization purposes. For example, Tableau and Microsoft Power BI are pretty popular. After basic visualization is done, necessary adjustments or changes can be made to arrive at the correct and final outcome.

    Data Visualization Use Cases

    • Healthcare: 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.
    • Science or SciVis: Data visualization projects help scientists and researchers gain greater insight from their experimental data efficiently and quickly.
    • Finance/Investment: 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 

    Here is a list of important programming languages used to create a data visualization projects:

    • Python  
    • R  
    • Matlab  
    • Scala  

    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.

    Importance of Data Visualization Project

    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: 

    1. It is an effective way to quickly communicate information in a universal way that can be understood by all. 
    2. Helps businesses to identify the factors affecting customer behavior and highlight areas needing attention or improvements.
    3. Make data easily understandable and memorable for stakeholders, business leaders and even customers. 
    4. Better visualization leads to faster absorption of information and leads to better insights and faster decisions, including important strategic business decisions.
    5. Enhanced ability to maintain the audiences’ interest with the information they can understand easily and remember as well.
    6. Improved and increased dissemination of information to all related in a uniform way.
    7. Eliminate the need to involve technical professionals or data scientists to derive information from data.
    8. Makes decision-making easy and speedy, thus bringing fast success due to quick action taken with fewer mistakes involved.
    9. Even investing time in learning data visualization techniques is worth it. For example, a Power BI Course or similar courses bring excellent data visualization skills in high demand across different industries and businesses. Because data visualization is becoming one of the most sought-after fields in data science.

    Purpose Behind Data Visualization Projects

    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. 

    Problems in Data Visualization Projects

    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: 

    1. Data Accuracy

    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. 

    2. Data Arrangement

    Proper grouping of data could be a huge challenge, especially when dealing with volumes of data arriving from different sources and in different formats. 

    3. Selection of the Correct Visual Metaphors

    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. 

    4. Availability Constraints

    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. 

    5. Lack of Prioritization

    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.

    Final Thoughts

    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; it 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.

    Data Visualization Projects FAQs

    1What is data visualization used for?

    To create visual stories from data for easy understanding of trends, making faster decisions. 

    2What programming language to use for your data visualization projects?

    Python is the ideal choice. 

    3How Do I Start a Data Visualization Project?
    • Understanding the purpose behind the project 
    • Data collection 
    • Data refinement 
    • Selecting the right visualization medium 
    • Visualize 
    4Can I use Tableau for my data visualization project?

    Most definitely. It is an awesome tool for managing large datasets.  

    5What is the best way to present data?

    Visually-diagrammatic presentation is the best.


    Mansoor Mohammed

    Business Agility Expert

    Mansoor Mohammed is a dynamic and energetic Enterprise Agile Coach, P3M & PMO Consultant, Trainer, Mentor, and Practitioner with over 20 years of experience in Strategy Execution and Business Agility. With a background in Avionics, Financial Services, Banking, Telecommunications, Retail, and Digital, Mansoor has led global infrastructure and software development teams, launched innovative products, and enabled Organizational Change Management. As a results-driven leader, he excels in collaborating, adapting, and driving partnerships with stakeholders at all levels. With expertise in Change Management, Transformation, Lean, Agile, and Organizational Design, Mansoor is passionate about aligning strategic goals and delivering creative solutions for successful business outcomes. Connect with him to explore change, Agile Governance, implementation delivery, and the future of work.

    Share This Article
    Ready to Master the Skills that Drive Your Career?

    Avail your free 1:1 mentorship session.

    Your Message (Optional)

    Upcoming BI & Visualization Batches & Dates

    NameDateFeeKnow more
    Course advisor icon
    Whatsapp/Chat icon