Data science is the use of scientific methods, algorithms and systems to extract knowledge and insights from raw information known as data. The idea behind it is to use data to help us understand certain phenomena.
Data science is a multidisciplinary science that uses techniques and theories from the fields of mathematics, statistics, information science and computer science.
Real-World Applications of Data Science
While data science tools are typically associated with helping us improve the way we do business, there are many other practical applications of data analysis models that help to solve problems beyond the boardroom. Some examples of this include:
Disaster risk management: Companies like Kat Risk use high-precision models to predict the risk of weather-related disasters like flooding.
Health care: Data can be used to help improve physical and mental health care. For example, Ginger.io analyses data from users’ mobile devices to form a picture of their emotional state. This behavioural analytics model uses data to provide users and their doctors with meaningful insights.
Child welfare: The non-profit organisation Case Commons creates software that uses predictive algorithms to assist social workers and administrators of social welfare programmes, by showing each child’s projected path.
Wildlife management: Biological consulting companies use data analysis to model the effects of tagging or habitat restoration on wildlife populations, to help optimise these efforts. Initiatives like this help to improve wildlife management and conserve wild animals in their natural habitat.
Data Science is Making a Difference for Shelter Animals
Data is now being used to help animal shelters in improving the way they operate. Here are two great examples of data science in action:
1. Reducing intake to shelters
Shelters typically operate with very little funding, which they need to care for resident animals, as well as animals in at-risk homes or on the streets. Reducing their intake of animals is critical, and data science can help to make this happen.
Big data can be used to map where the homeless animals are coming from, by compiling lists of where and when animals are most often abandoned. By making informed decisions based on this information, shelters can help to prevent more animals from becoming homeless unnecessarily. For example, they can look at implementing more preventative measures (like spay/neuter programmes or low-cost veterinary care) in areas that have been identified as abandonment hotspots.
2. Improving the adoption rates of shelter animals
Data science is helping shelters with their adoption efforts too. Analysts are using data sets to determine what makes certain dogs or cats more “adoptable”. While age, breed and coat are all contributing factors, data scientists revealed that interestingly, animals that have been given names are also more likely to be adopted. The data also confirmed that certain days of the week lead to more adoptions than others, with the majority of adoptions occurring over the weekend.
Using sound information like this, shelters can become more efficient in helping animals to find loving homes, by making sure each animal is named and sterilised, and focusing their marketing efforts on high-adoption days.
These are just two of the many ways that smart data analysis can make life better for animals, and ensure that more of them become beloved pets adopted into new “forever homes”.
If you’re dreaming of making a difference with a career in data science, then HyperionDev’s Introduction to Data Science online course is just the place to get started. Register today to begin learning about the true value of understanding and analysing data.