What is Data Science?
This is the era of the Big Data, Big data doesn’t just mean piles and piles of data, it’s mainly based on three parameters – Volume, Variety, and Velocity.
Volume– Volume addresses the exponential increase in the size of the data every day.
Variety– Variety manifests the range of different data types that are emerging.
Velocity– Velocity refers to the rate at which this Data is generated.
At first, the problem used to be storing the Big Data at the velocity it is generating. With the advent of Hadoop and other frameworks, the storage problem has been addressed but the businesses have started to realize the significance of data analysis and real power the Data possess. Now, Data science is a “concept to unify statistics, data analysis, machine learning, and their related methods” to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science. To make it simple – Data science is the field of study that combines domain expertise, programming skills, and knowledge of math’s and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems that perform tasks that ordinarily require human intelligence. In turn, these systems generate insights that analysts and business users translate into tangible business value.
A recent statement that depicts the significance of Data Science is
“Data is the new oil for all the industries but data science is the electricity that powers the industry.”
Why Data Science?
The below lines clearly define the importance of data science. With this rapidly changing world, there is a massive explosion of data generation. The key to success is handling and managing this data through Data Science.
“Where there is data smoke, there is a business fire” – Thomas Redman.
Data is the biggest asset to the organization. No organization can afford to overlook the benefits of the data. Today data is collected by organizations in every possible way. From digital clicks to our mobile usage and interaction to social media, every single aspect of data is being recorded. Which is then used to rise above the competition.
- Though the scope of data science is very wide. It is crucial to understand what you can do with the data.
Where Do We Need Data Science?
- Business optimization– Businesses can now more clearly understand the customer base. This aids in increasing the no. of customers, serving the customers better and efficiently. Cost-cutting technology is highly preferred to reduce unnecessary costs and ignore cost overheads.
- Generating insights from data– Every organization has a bulk of data that is to be handled with due diligence. Data Science helps in converting the raw data in a valuable form, with the application of various tools and techniques. This helps in finding the hidden information from the data and taking critical decisions. It mainly helps in focusing on the target market, customer segmentation and evaluating the future trends in the market.
- AI is the future– Automating the transportation and Robots taking over dangerous jobs is one of the biggest contributions made bt Data Science to the world of technology. Speech Recognition(Siri and Amazon-Alexa) makes it possible to reduce human intervention in technical work.
- The core of Decision Making– Data Science is responsible for solving business problems by analyzing the data. This assists in taking tough decisions in the long run. It enables companies to create new business opportunities, predict future trends, generate more revenue and expand the empire.
- Pattern Discovery – This is one of the great significance of Data Science. It helps in identifying the patterns and trends in data. The present patterns are matched with the patterns in the past to analyze the reoccurring of the event. This is highly used in the field of Weather Forecasting.