The Science behind Data Science
Data Science is an evolutionary step in an interdisciplinary field (Business Analysis) about processes and systems, which tells how to extract knowledge from the data in various forms, it can be either done in a structured or in an unstructured manner.
Data science uses automated methods for analysing large amounts of data and then extracting knowledge from them. This analysed information is used everywhere, which ranges from genomics to high-energy physics.
Now a days, data science is helping in creating many new branches of science, and it is also influencing the areas of social science and the humanities. This trend is likely to accelerate in the coming years as data from mobile sensors, web, is growing.
Data science in:
- Academic research: There are increasingly large number of traditional disciplines that are reproducingmany new sub-disciplines with the adjective “computational” or “quantitative” in front of them.
- Industry: Data science is transforming everything from healthcare to media.
Data Science for Research
Data science offers a new and a powerful approach for making discoveries. By combining the best features of statistics, computer science, applied mathematics, data science can turn large amounts of data into digital age and then into new insights and knowledge.
Social science: Data collection by sociologists through traditional method of Facebook and Twitter takes months or even years. A tool, called as Hedonometer; which is created by University of Vermont researchers, puts this collected data for social use. Around 50 million tweets per day is being pulled down and after matching these tweets against a database of words, it is assigned a happiness value. Higher the Hedonometer number on that particular day, happier is the collective mood of Twitter users.
Economics: How many products which are labelled as either “green” or “organic” get sold around the globe? Why does the price of certain goods stays the same, when economic conditions are changing? These are arising questions among the MIT’s Billion Prices Project, which are still being pondered upon.
Engineering: New methods have been employed on the various aspects of data science, which helps the engineers to design nanomaterials by using particular properties. Researchers aim to replace a huge and growing body experimentation data, with those approaches which are time-consuming or are on expensive trial-and-error design methods.
Medicine: Data science can also help to discover diseases which researchers don’t even look for. Like, a group of patients who are suffering from diabetes; it may be possible for them to get prone to a particular disease, while another group of patients who are also suffering with diabetes may be prone to some another disease. It might be possible that it may irradiate a new form of diabetes, which requires a personalized treatment.
Business: In every stage of a company’s operations, there is a need to generate data. For this, Big data may become a brand new type of corporate resource which will cut across business units and thus function as a powerful brand. For doing this, companies are depending upon data scientists for collecting, integrating, and analysing the data.
About the Author:
Vaishnavi Agrawal loves pursuing excellence through writing and has a passion for technology. She has successfully managed and run personal technology magazines and websites. She currently writes for intellipaat.com, a global training company that provides e-learning and professional certification training.
The courses offered by Intellipaat address the unique needs of working professionals. She is based out of Bangalore and has an experience of 5 years in the field of content writing and blogging. Her work has been published on various sites related to Hadoop, Big Data, Business Intelligence, Cloud Computing, IT, SAP, Project Management and more.