Why Data Science?
Published: 3 Jul 2018
“The ability to take data, to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it’s going to be a at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data.” – Hal Varian, Chief Economist, Google
Why Data Science?
Data Science was once a discipline associated only with technology companies, hedge funds and health care. However, the information age has made data gathering and analytics critical to virtually every sector of the economy. Publishing itself, once associated only with publishing houses and media companies, is now ubiquitous as companies and individuals write on blogs and social media. The reality in which we are living today is that we are all publishers, and data science is paramount.
Data Science allows individuals to extract knowledge of great value from what can appear to be simple data. Using semantic analysis, pattern matching, statistics, mathematics, and most recently, machine learning, data science is the discipline of analyzing large volumes of information to find patterns. Once patterns are found, they can be developed into unique concept signatures and used to make predictions and inferences. In this way, it is possible to anticipate problems and create different strategies to boost efficiency and revenue in business.
Data Scientists bring value to any type of company, and thanks to the emergence of Big Data there is a countless amount of data, both inside and outside of organizations, public and private. That data includes not only textual content but images, video, audio, and even “data about the data” (metadata).
Of course, all of this data is useless unless it is properly analyzed. But with increasing machine power, algorithms can convert data from disparate data sources into knowledge that is essential to any business.
Machine Learning and Big Data
While Big Data Analytics allows data scientists to identify patterns and relationships with the data, machine learning allows scientists to use those patterns to make better decisions. Using Artificial Intelligence (AI) algorithms, machines can analyze which patterns result in success and which in failure, isolating successful patterns to ensure that better decisions are made.