Data Visualization Projects

Data visualization is vital in abstracting data from raw numbers and statistics while removing the jargon and complexity from overall conclusions. In addition to helping data scientists analyze and understand projects better, data visualization also makes the analyses accessible, especially in business intelligence-heavy industries.

Data accessibility is critical for groups in which not all members have data science experience. For example, when communicating findings to stakeholders, it is easier to give them a visual representation of what has already or is going to happen.

You can create data visualization projects using data visualization datasets and assessing which technique to use to present them properly.

• Finance and Economic
• Health Data Visualization Projects
• Environmental and Socio-Political
• Social Media and Services

The finance and business industry is a field that heavily relies on business intelligence and data visualization. Since the field requires input from various teams and stakeholders, data visualization unifies and directs opinions, creating an efficient workflow. Here are some helpful finance and economic datasets to work with.

S&P 500 Stock Data Visualization Project

The Standard and Poor’s (S&P) 500 is a stock market index listing the top 500 performing companies in the United States. What makes the S&P 500 critical is that, more often than not, the changes within the index reflect the trajectory of the US economy.

Below are 2 data visualization datasets for the S&P 500:

The first dataset contains 5 years of stock data up to 2018. Each stock includes daily information, including opening price, closing price, lowest price of the day, and highest price of the day. Contrastingly, the second dataset contains more updated information for generating stock forecasts.

Nevertheless, you can utilize both datasets for data visualization by using boxplots as the appropriate technique since they account for anomalies such as the highest and lowest prices and as a result, can holistically represent the data.

Because boxplots are non-parametric, you can also use them for exploratory and explanatory data analysis aside from data visualization.

1. Hotel Booking Data Visualization Project

One of the functions of data visualization is to ensure that data analysis projects involving resource usage prediction are appropriately relayed and that their conclusions create actions that dictate efficiency. As such, this data visualization dataset from Hotel Booking Demand Datasets is suitable for predictive models that assume the hotel booking demand.

The dataset has been thoroughly cleaned for easier use and contains information sourced from 2 hotels wherein sensitive and identifying information is removed and censored. Each of the hotels has specified the following information:

• Booking date
• Customer demographics (number of children, adults)
• Length of stay
• Parking spaces available
• Number of cancellations
• Other related information

Use a bar graph to compare both hotels and their values for datasets such as this since the values are discrete. Moreover, stacked bar graphs are a practical choice for approaching how to differentiate cancellations from successful bookings.

2. World Economic Outlook Data Visualization Project

With this bi-annual world economic outlook dataset from the International Monetary Fund, you can explore specific financial data that can be sorted either by country, by region, or within a particular time frame. Because of this dataset’s size, you can choose to capture a snapshot of the dataset to represent it visually.

For a world economic data visualization project or any data visualization project that involves more than 3 countries, it is best to use a geographical map to present the data. To present economic rigor comprehensively, you can also use colors to represent stronger economic performance.

However, for comparing the performance of a single country over time, a line graph is the best data visualization technique to use as it can represent downtrends and uptrends without complications.

Revenue Data Analysis

Unlike standard business analytics tasks, analyzing in-app purchase data for a mobile game involves unique challenges, particularly when tracking revenue generation and user behavior across multiple devices. The dataset in question contains three key tables: account, account_device, and transactions. These tables provide a comprehensive view of user profiles, device usage, and in-app purchases, respectively. This data is crucial for understanding revenue patterns, user engagement, and device preferences.

This dataset includes information on in-app purchases made by users on different devices, along with the associated revenue, dates, and device types. As this data is cross-sectional, careful consideration must be given to both the temporal and device-related dimensions of user activity.

Like other revenue analysis tasks, aggregating revenue over time can reveal trends and peaks, while a comparison of device usage, such as iPads versus iPhones, can highlight user preferences. A pie chart might be most effective for displaying the iPad/iPhone split, particularly within specific countries, while a bar chart could effectively show the total revenue produced on a specific date.

With the recent pandemic, the need for accurate, digestible, and easy-to-access health data is crucial. Data visualization allows data to be disseminated in digestible, accessible pieces while remaining authentic and valuable. Below are 2 healthcare data science projects that you can use for data visualization.

COVID Data Visualization Project

The COVID-19 pandemic has been going on for over 2 years, and while the general public’s concern for the virus has decreased over time, there is still a need for community-friendly, accurate data. You can use data visualization techniques to expose at-risk communities and predict economic activity.

This dataset collated by Our World in Data contains COVID-19 information including vaccinations, excess mortality, hospitalizations, variant information, testing data, and other related statistics.

There are a few concerns regarding this dataset, however. For example, while numbers like cases, deaths, vaccinations, reproduction rate, and policies are still updated daily, tests and positivity rate data are no longer updated.

Another COVID-19 dataset from G.h data contains detailed information from over 100 million anonymized COVID-19 cases.

For this dataset, it is better to have a geographical map to represent the cases and vaccinations but a line graph to display the movement in the reproduction rate.

Monkeypox Data Visualization Project

Unlike COVID-19, the public’s concern for the monkeypox virus has grown over time, especially since, as of July 2022, the Monkeypox virus is now considered an international health concern by the World Health Organization, creating a vital need for proper data visualization.

This dataset contains monkeypox cases as well as information regarding the worldwide case tally, case detection timelines, and a daily count of cases per country. Since this dataset is updated daily, you will need to update your data accordingly to provide time-relevant data visualizations.

Like the COVID-19 dataset, a geographical map is most useful for comparing cases from country to country, while a line graph can display the case trends globally, as a unit, or for anywhere from 1 to 3 countries simultaneously.

As socio-political and environmental issues develop over the years, awareness is key for creating a progressive and collective impact. One method of raising awareness is by creating a compelling and thought-provoking data visualization project. You can get started with the following datasets.

US Election Data Visualization Project

The 2020 US election was one of the most critical elections in recent history, dictating which president would take office during the ongoing pandemic. The candidates for this election were Joe Biden for the Democratic party and President Trump for the Republican party.

This dataset contains information regarding the 2020 US election that you can leverage to create clever and visually appealing data visualization projects.

You can use a geographical map to determine which states are predominantly Republican or Democrat and a scatter plot to determine which age group votes Democrat or Republican.

1. US Racial and Ethnic Diversity Data Visualization Project

Datasets that explore the socio-economic divide between racial classes are ideal for generating policies or support systems that alleviate disparities. The United States, as a cultural melting pot, is one of the countries that requires deep exploration to prevent the racially motivated policies of the 1900s from affecting modern communities.

This dataset explores the racial and ethnic diversity in the United States using data from the 2010 and 2020 censuses. While this dataset alone might not be able to reveal biases, it can be combined with income, education, and crime data visualization projects to depict tendencies.

Cross-reference this data visualization project with the following datasets:

• This dataset contains the income and poverty prevalence in the United States.
• This dataset that shows the national crime victimization distribution.
• The US COVID-19 dataset that showcases which communities are heavily affected by the virus.

2. Carbon Emission Data Visualization Project

As globalization prompts an increase in global demand for virtually anything, delivery services (i.e., food, goods, groceries) have gained popularity. As such, the global carbon emission rates have risen to alarming levels.

Data visualization projects highlighting the severity and urgency for renewable energy resources are integral for a robust green movement. This dataset contains information collected by Our World in Data and displays greenhouse gas emissions, energy mix, and other related data. The numbers are sorted per year and are updated accordingly.

Because you want to highlight the drastic change in carbon emission, a line graph may resonate more profoundly with viewers. For the energy mix, a pie chart will best display the usage share of each energy source.

3. Land-Ocean Temperature Index Data Visualization Project

Rising temperatures have been observed by scientists ever since the 1600s. Here is a dataset gathered by NASA about the land-ocean temperature index, showcasing the temperature growth over time.

For a dataset like this, a line graph showing the uptrend of rising temperatures would provide a retrospective insight into how bad global warming has become.

Social media datasets can be used to provide perspective on how social relationships work, especially in digital spheres. They can also provide insight into how these social clusters grow, are affected by external changes, and spring out from a singular node of interest.

Additionally, digital services such as Netflix and Apple Music have datasets that can help visualize user behavior and preferences.

1. Stanford Network Analysis Platform (SNAP) Social Media Data Visualization Project

The SNAP platform contains social media datasets containing nodes and graphs that help visualize social connections and circles. For example, on Facebook, your friends are considered your social connections.

The SNAP platform has the following datasets available for graph visualization projects:

• Twitter social circles
• Google Plus social circles
• Facebook page network
• Twitch social network
• Facebook social network
• Reddit Hyperlinks network

For this data visualization project, since you are assessing the graph data structure, you should employ graph visualization.

Graph visualization (link visualization) is vital for evaluating and grading the connections between nodes (or users/pages in this instance). Moreover, instead of reviewing raw user data that may be confusing, link visualizations simplify edge representations between nodes.

2. Spotify Data Visualization Projects

The Million Playlist Dataset by Spotify is a dataset for machine learning applications such as recommendation systems that allow models to predict which songs would fit a specific playlist. While not intended for data visualization, you can utilize this dataset in the following ways:

• Graph visualization - by treating each playlist as an “entity,” or a node, while the edges are the songs that can connect one playlist to another
• Graph visualization - by treating each artist as a node, with the nodes being the playlists. Whenever one artist’s song co-exists with another on a single playlist, they are connected with edges.

While not specifically tailored for data visualization, there are also other datasets from Spotify available here:

• Spotify English-Language Podcast Dataset(needs admin request)
• OpenMIC Audio and Crowd-Sourced Instrument Labels
• The Music Streaming Sessions Dataset

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