- Data science combines domain expertise, mathematics and statistics, and programming to get value from data.
- Data scientists apply machine learning to numbers, text, images, video, and audio to produce insights and AI systems.
- Common applications include retail and customer analytics, credit scoring, and sepsis prevention.
- A data scientist analyzes data to answer business questions; a data engineer builds and maintains the pipelines that data scientist uses.
What is Data Science?
Data Science is an interdisciplinary field of study that combines the use of domain expertise, mathematics/statistics knowledge, and computer programming to obtain value from data. Data scientists can apply machine learning algorithms to numbers, text, images, video, and audio in order to extract actionable insights and produce artificial intelligence systems. These systems can perform tasks that traditionally require human input.
Applications of Data Science
Retail and Customer Analytics
Data science apps can be used to manage promotions and discounts in real-time. It can also scan social media networks to help forecast what products will be in high demand in the future.
Credit Scoring
An analysis of a customer’s banking history through data science can estimate creditworthiness. This helps banks with risk management.
Sepsis Prevention
AI can be used to predict treatment or dosage levels that are likely to be most effective based on patient history. Early detection and more precise care results in less aggressive and less costly treatments.
Why is Data Science Important?
Data science, artificial intelligence, and machine learning are helping organizations remain relevant in competitive markets. When implemented correctly, data science helps organizations mitigate risks by better navigating future market trends and consumer behaviors.
Data Science vs Other Technologies & Methodologies
Data science vs computer science
Data science is a specific field of knowledge within the study of computers, focusing on programming, analytics, and statistics. Computer science deals with building hardware and programming software.
Data science vs data analytics
Data science focuses on finding meaningful correlations between large datasets, while data analytics is designed to uncover the specifics of extracted insights.
Data science vs information science
Data science is the discovery of knowledge and actionable information in data. Information science is the design of practices for storing, retrieving, and interacting with information.
Data science vs data engineering
A data scientist analyzes data, answers questions, and provides metrics to solve business problems. A data engineer develops, tests, and maintains data pipelines and architectures, which the data scientist uses for analysis.
Data Science in H2O AI
H2O Driverless AI, AutoML, automates feature engineering, model validation, tuning, selection, and deployment for data science teams. H2O Driverless AI.