10 Data Visualization Project Ideas for All Levels

Data Visualization Projects for Beginners

Beginner projects focus on building foundational skills and getting started with understanding visuals. 

Focus on learning tools like Excel and basic Matplotlib with easy graphs such as scatter plots, line charts, and bar charts. Starting with easy-to-understand visuals that support what you know about the data is a great way to build foundational skills in data visualization.

At this stage, consider taking a beginner's course like Understanding Data Visualization and working on some projects.

Project 1: Plotting flight costs by day of the week

This project idea involves creating one or more scatter plots using Excel to visualize airline ticket prices across different days of the week. You can work with a dataset containing flight costs for the days of the week, possibly creating multiple charts for different routes or airlines. Plot the prices using Excel's scatter plot feature, with days of the week on the x-axis and ticket costs on the y-axis. 

This project is an excellent starting point for beginners. It introduces key concepts of data visualization using the widely accessible Excel software. It allows newcomers to work with real-world data that's both relatable and potentially useful in their own lives. 

The project provides hands-on experience in transforming raw numerical data into a visual format that can reveal patterns in flight pricing, such as which days tend to be cheaper or more expensive for travel.

The skills and tools learned or reinforced by completing this project are:

• Using Excel to visualize data
• Data entry and organization
• Understanding of different chart types and their appropriate uses
• Data exploration and pattern recognition

       Exploring data on airline prices from TripAdvisor. Image source: NYCDataScience

Project 2: Create Phyllotaxis Art with R

If you’d like to be guided, the “Creating Floral Patterns Inspired by Phyllotaxis” project explores the intersection of mathematics, nature, and art through data visualization in R. Using the concept of phyllotaxis—the arrangement of leaves on plant stems—you’ll create visually stunning spirals that mimic natural patterns found in flowers and plants. Phyllotaxis is a prime example of how mathematical principles such as the Fibonacci sequence and the golden angle can describe natural beauty. 

By the end of the project, you’ll have crafted your flower art, utilizing data visualization techniques to showcase the elegance of these natural patterns. This project is perfect for those who want to mix creativity with data science, all while deepening their understanding of mathematical models in nature.

Data visualization skills learned in this project are:

• Creating visualizations in R using tools like ggplot2
• Understanding and implementing the mathematics behind natural patterns
• Enhancing your R coding skills by generating and manipulating data to represent beautiful shapes
• Learning how transformations like rotations and scaling can be applied to create intricate designs

Project 3: Visualizing the history of Nobel Prize winners

The “Visualizing the History of Nobel Prize Winners” project explores historical data surrounding one of the world's most prestigious awards. It involves analyzing and visualizing data on Nobel Prize laureates across different categories, years, and demographics.

This project is an excellent choice for beginners because it combines data manipulation with meaningful visualization tasks using an interesting and manageable dataset. 

The project allows you to practice various visualization techniques while engaging with real-world data that has cultural and historical significance. It also introduces the important concept of using data visualization to uncover and communicate potential biases, teaching critical thinking alongside technical skills.

The project is available in both Python and R.

Key data visualization skills learned in this project are:

• Creating time series visualizations to show trends over decades
• Designing bar charts and pie charts for categorical data (e.g., prize categories)
• Developing heat maps or choropleth maps for geographical analysis
• Using color effectively to represent different categories or highlight disparities

Intermediate Data Visualization Projects

Having mastered the basics, it's time to elevate your data visualization skills with more challenging projects. This section introduces intermediate-level ideas to expand your toolkit and deepen your analytical abilities.

Project 4: Comparing baseball player statistics

The “Compare Baseball Player Statistics” project offers an engaging opportunity to apply and expand foundational data visualization skills in Python using real-world sports data. 

Utilizing Statcast data from Major League Baseball, you will analyze and compare home runs hit by two baseball players. This project allows you to work with multi-dimensional data, combining spatial information (hit locations and trajectories) with numerical data (exit velocity, launch angle). The appropriate visualization types must be chosen to effectively represent this complex data, including scatter plots, heat maps, and possibly even 3D visualizations.

Additionally, working with sports data adds an element of storytelling, encouraging you to use visualizations to narrate the similarities and differences between the players' home run patterns.

Key data visualization skills learned and practiced by completing this project include:

• Data cleaning and preparation of complex sports datasets
• Crafting data-driven narratives through visual storytelling
• Working with spatial data and geographic visualizations
• Understanding different chart types, such as heat maps and scatter plots, and their appropriate uses.

Project 5: Analyzing flight delays and cancellations

The “Analyzing Flight Delays and Cancellations” project offers an exciting opportunity to explore real-world aviation data from the Pacific Northwest. This project analyzes a comprehensive flight information dataset to uncover patterns and factors contributing to delays and cancellations. 

You will use data wrangling techniques in Python to clean and prepare the data. Then, using Matplotlib, you will apply various visualization methods to reveal insights about airlines, airports, weather conditions, and other variables affecting flight disruptions.

This project builds on foundational data visualization knowledge by introducing more complex, multidimensional data analysis. It requires you to simultaneously work with time series data, geographical information, and multiple interrelated variables. 

If you take this guided project, you’ll create advanced visualizations that effectively communicate the relationships between various factors and flight disruptions, potentially including interactive elements for deeper data exploration. This project also introduces the challenge of handling and visualizing large datasets, encouraging you to use data aggregation and summarization techniques effectively.

Key data visualization skills that this project will develop are:

• Creating time-series visualizations to show patterns in delays and cancellations over time
• Developing interactive dashboards to explore multiple aspects of flight data
• Integrating statistical analysis with visualizations (e.g., correlation heatmaps)

Project 6: Visualizing heart rate and heart disease

The "What is Your Heart Rate Telling You?" project uses the Cleveland Heart Disease dataset to explore the important issue of heart disease. In this project, you'll use R to explore the relationship between maximum exercise heart rate and heart disease risk while accounting for the effects of confounding variables. Through the application of multiple logistic regression, you'll uncover valuable insights about this complex health condition.

Learners are required to create visualizations that not only display raw data but also illustrate statistical relationships and model outcomes. You'll need to design visualizations that communicate complex statistical concepts effectively to a general audience, transforming data analysis into visual storytelling.

The project also introduces the challenge of visualizing the effects of confounding variables, pushing learners to develop more advanced, multi-variable visualizations. These visualizations will highlight the nuanced relationships between different variables and heart disease risk.

Prerequisites include knowledge of the Tidyverse and an understanding of hypothesis testing in R.

Key data visualization skills developed:

• Visualizing relationships between variables to show trends and patterns.
• Illustrating relationships among multiple variables at once, providing a clear overview of how they interact.
• Displaying model results to communicate the strength and direction of relationships from the logistic regression model.

Project 7: Global life expectancy with ggplot2

Explore gpplot2 through the “Exploring global life expectancy trends” project. Understanding ggplot2 is an integral part of being a data scientist, as it contains many foundational concepts shared in other visualization tools, such as Matplotlib. 

Global life expectancy data covers a wide range of demographics, countries, and time periods. A wealth of information is waiting to be visualized in various beautiful scatter plots and diagrams.

Use this project as an opportunity to build on your understanding of ggplot2. Focus on the mechanics of creating graphics, such as the plot area, faces, and grid. Build beautiful and impactful graphics by manually adjusting graphs to tell a more complete story. 

Due to this project's in-depth technical nature, you should understand ggplot2’s foundations and have some experience cleaning data with R.

This project focuses on the following key skills:

• Advanced ggplot2 customizations
• Building reusable graphics for other projects
• Understanding the mechanics of how plots are made, such as facets and grids
• Telling a story by adding more layers to simple graphs

Advanced Data Visualization Projects

Shifting from intermediate to advanced combines increasing technical abilities into more nuanced tools and better storytelling. These advanced projects focus on applying similar concepts from above to new technologies such as GeoPandas combined with more sophisticated interactivity and storytelling. 

With more skill, there is more freedom to create visualizations that best fit your story. As such, these ideas will be less prescriptive and provide broader options.

Project 8: PowerBI Sankey diagram for tracking subscription flow

The Sankey diagram is a powerful tool for showcasing the flow of a population. This graphic is particularly challenging to make with traditional coding methods, so utilizing a tool such as PowerBI greatly simplifies the effort. While significant data wrangling is required to build out the proper data model, the program easily generates the Sankey chart.

This example will show how initial contacts may become subscribers without using a trial period. The focus here is building up our PowerBI DAX skills. It requires unpivoting data, creating dynamic data models, and formatting data to generate the desired graphic.

Example Sankey chart. Image source sqlbi

The skills focused on here are:

• Data wrangling
• PowerBI DAX functions
• PowerBI data modeling
• Generating Sankey charts 

Project 9: Spotify Tableau dashboard

A Spotify Tableau dashboard is a great way to develop advanced skills in Tableau due to the richness of Spotify’s music data. This project builds on the knowledge gained from the intermediate project, where we classified the music. 

A project like this focuses on crafting an exciting story using Spotify’s music metadata. This is a good chance to practice collecting data through Spotify’s API, organizing it, and creating a fun dashboard. It should contain selectors for filtering out by genre, artist, year, and timbre. Add a page in the Tableau story focusing on a particular song and generate a radar chart. Practice many different Tableau skills to create a fully-fledged dashboard that can interest music lovers.

The key skills practiced are the following:

• Advanced Tableau mechanics, such as radar charts and actions
• Creating Tableau stories
• Collecting data via an API
• Storytelling through data

Project 10: Ridership visualization with GeoPandas

One advanced visualization technique is utilizing spatial data. A lot of data is inherently spatial, from customer zip codes to GPS trace data. Learning a specialized package like GeoPandas in Python can give you a unique skill set. 

Try a project using GeoPandas to create maps, visualizations, and spatial analysis. For instance, using a dataset like CitiBike Data can provide a great opportunity to understand how to create maps, the different kinds of map projections that exist, and how to look at data from a different perspective.

Because this is a more specialized skill, focus on analyzing geospatial data in Python first to understand how geospatial data is generally structured. 

Start by making simple maps and plotting station locations using the appropriate geographic projection. Then, add data such as ridership information and busy hours to those maps. Just like our other graphics, start with the foundation and build up by adding more layers. You can even create heat maps showcasing hotspots in ridership.

The skills being honed by this project are:

• Geospatial analysis skills
• Knowledge of GeoPandas in Python
• Creating maps and understanding how maps are generated in Python

Summary Table of Data Visualization Projects

Here is a quick summary of each project and the skills developed.

Conclusion

Building a data visualization portfolio is important for developing your technical and storytelling skills. Employers are looking for data professionals with the potential to generate complex analyses and effectively communicate them with stakeholders! 

Keep these skills fresh by trying the projects mentioned above and brush up on your skills with various DataCamp resources:

• Understanding Data Visualization
• Data Visualization with Python
• Data Visualization PowerBI
• Introduction to Matplotlib
• Excel Data Visualization
• Dashboards in Tableau