What is data science?

Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.

Why is data science important?

Data science is important because it combines tools, methods, and technology to generate meaning from data. Modern organizations are inundated with data; there is a proliferation of devices that can automatically collect and store information. Online systems and payment portals capture more data in the fields of e-commerce, medicine, finance, and every other aspect of human life. We have text, audio, video, and image data available in vast quantities.??

History of data science

While the term data science is not new, the meanings and connotations have changed over time. The word first appeared in the ’60s as an alternative name for statistics. In the late ’90s, computer science professionals formalized the term. A proposed definition for data science saw it as a separate field with three aspects: data design, collection, and analysis. It still took another decade for the term to be used outside of academia.?

Future of data science

Artificial intelligence?and?machine learning?innovations have made data processing faster and more efficient. Industry demand has created an ecosystem of courses, degrees, and job positions within the field of data science. Because of the cross-functional skillset and expertise required, data science shows strong projected growth over the coming decades

What are different data science technologies?

1. Artificial intelligence:?Machine learning models and?related software are used for predictive and prescriptive analysis.
2. Cloud computing:?Cloud technologies have given data scientists the flexibility and processing power required for advanced data analytics.
3. Internet of things:?IoT refers to various devices that can automatically connect to the internet. These devices collect data for data science initiatives. They generate massive data which can be used for data mining and data extraction.
4. Quantum computing:?Quantum computers can perform complex calculations at high speed. Skilled data scientists use them for building complex quantitative algorithms.