Data Science

TL;DR

Works with both structured and unstructured data to produce actionable insights.
Encompasses tasks from cleaning and organizing raw data to developing machine learning models for prediction and pattern detection.
Applied across domains to inform decisions and improve outcomes (for example, product recommendations and environmental monitoring).

Definition

Data science is a field that involves using various methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. This can involve anything from cleaning and organizing raw data, to developing machine learning models to make predictions or identify patterns.

Explanation

Data science brings together techniques and tools to transform raw data into usable information. Typical activities include preparing and organizing data, then applying statistical or machine learning algorithms to detect patterns or produce predictions. The results are used to help organizations interpret the large volumes of data they collect and support informed decision-making.

Examples

Customer purchase data and product recommendations

A company may collect data on its customers’ previous purchases, browsing behavior, and demographic information. A data scientist can use this data to develop a recommendation algorithm that suggests products to individual customers based on their unique characteristics and preferences. This can help the company increase sales and improve the customer experience.

Satellite imagery and environmental monitoring

Using satellite imagery and geospatial data, a data scientist can apply machine learning algorithms to automatically identify and classify different land use types in satellite images, such as forests, urban areas, and bodies of water. That classified data can be used to track changes over time, monitor ecosystem health, and support decision-making around conservation and natural resource management.

Machine learning