What are visualizations in data science?

Why is data visualization important for data scientists?

Data visualization is important for data scientists as it turns complex data into easy-to-understand visuals. Charts and graphs help people see patterns and trends quickly, making it simpler to communicate insights. This clear representation helps in decision-making, ensuring that everyone understands the key findings from the data.

By effectively visualizing data, we can better understand its diverse nature. A variety of data exists in data science. Some data is in the form of numbers, while some exist as categories. Different types of data need to be represented differently.

Types of data

Data can be broadly classified into two main categories: quantitative and qualitative.

• Quantitative data exists in the form of quantities or numbers. This includes the population of a country, the weight of a person, or the number of days in a week.

• Qualitative data exists as categories. It is non-numerical in nature. This includes categories of sex, daily weather, or types of degrees in a university.

Both qualitative and quantitative data can be subdivided into further categories. The illustration below summarizes the types of data:

Types of quantitative data

Quantitative data can be subdivided into two main categories: discrete and continuous data.

• Discrete data refers to data in whole numbers. They can take certain fixed values only. These include the number of days in a month, the age of a person, or the number of siblings.

• Continuous data spans a range of values. It is not fixed and can also have decimal numbers. Examples include a student’s GPA or a car’s speed.

Types of qualitative data

Qualitative data can be subdivided into two main categories: nominal and ordinal data.

• Nominal data does not have an order amongst it. It cannot be ranked in any way. This includes categories of sex or ethnicity.

• Ordinal data has some order within it. It can be ranked from high to low, good to bad, or vice-versa. This includes levels of education in school, survey responses on a Likert scale, or Yelp ratings.

Types of visualizations

Data can be represented using visualizations. Visualizations help in providing an overview of the data along with summary statistics. The diversity of data types and the specific nature of each problem require different visualizations to convey insights effectively. We’ll discuss some prominent visualizations below:

Comparison table

Here’s a comparison table that helps to quickly compare the different types of visualizations, their corresponding data types, and specific use cases.

This table provides a clearer comparison of the types of visualizations, what kind of data they represent, and the practical applications of each in the real world.

Applications of data visualizations in real-life

Data visualizations are used in many real-life situations to help people understand and make decisions based on data. Here are a few examples of how they are applied:

1. Business: Companies use data visualizations to track their sales, profits, and customer behavior. For instance, a streaming service like Netflix might use data visualization techniques like a bar chart to compare the performance of different genres and see which genres are the most popular. Or a store like Walmart can perform data visualization to observe which items are being sold better than the others.

2. Healthcare: In hospitals, visualizations like line charts and pie charts track patient recovery, the spread of diseases, and the effectiveness of treatments. Doctors can quickly spot trends and make decisions based on this data.

3. Navigation: Navigation apps like Google Maps use visualizations to show traffic conditions, routes, and distances between locations. Heat maps can show traffic density at a given time, helping users choose faster routes and avoid congested areas.

4. Education: Teachers and schools use data visualizations to monitor student performance. For example, a school might use a scatter plot to show the relationship between study time and test scores, helping students see how their efforts pay off.

5. Public administration: Governments use visualizations to communicate important information to the public, such as showing population growth with census data through maps and charts.

Conclusion

To sum up, data visualization simplifies the process of understanding complex datasets by turning them into easy-to-read visuals. This helps data scientists, even non-scientists, present their discoveries clearly, allowing others to grasp patterns and trends effortlessly. It, therefore, bridges the gap between raw data and actionable insights. This ability of data visualization to communicate data effectively is essential across industries, helping drive better decisions and meaningful results.