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What is Data Science?

Today’s world is connected with digital networks and these are generating huge volumes of data everyday from different electronic sources and mediums.

It was difficult for businesses to manage, control, administer, and handle this volumes of data and give a proper sense to the data until the evolution of Data Science technology, but now the scenario is different. Data Science performs well in understanding and analyzing raw data and extract useful and meaningful information hidden in it. Business can refer to Data Science as a combination of various systems and processes that function systematically to analyze huge volumes of unstructured raw data and retrieve and deliver useful, meaningful and powerful information to businesses that strive for future IT strategies and developments in their business.

Brief Of Data Science

It is a multi-disciplinary combination of technology, algorithm development and data inference that simplifies and analyze the complex raw data and discharge useful information. There is significant difference between data and information. Data is raw,  unstructured and unorganized that needs to be processed and analyzed. When this data is structured, organized, processed and presented in a given situation is referred as Information. Data Science is all about converting the volumes of raw data into useful information and utilize this information for the future business developments and help organizations in predicting the future needs deriving a business value.

Before the evolution of Data science( also called as Data Mining), people who work on data were called as statisticians, data analysts, computer scientists, business analysts etc but now they are all called as ‘Data Scientists’. As mentioned, today’s digital world is generating massive volumes of raw data via disparate systems, organizations need to store this raw data in respective data warehouse systems in order to furnish a business value after the data mining process of raw data. Now, the two major functionalities of Data Science that are used to process the raw data from data warehouses are:

  • Discovery of Data Insight
  • Development of Data Products
  • Discovery of Data Insight

This functionality is all about discovering and identifying the insights in the raw data by plunging in at granular level to delve and understand the complex structure, behaviour, inferences and trends of the data. By which, organizations can make better decisions and construct suitable strategies for high business performance. For example:

How Do Data Scientists Unearth Insights?

The process initiates with data exploration, unknowingly at this stage data scientists work like a detective. They perform a thorough check on leads of data and try to sketch out the characteristics or patterns in the data. This action requires a deep-routed analytical DNA kind of intuitive thinking for a data scientist. They may adopt some quantitative techniques in order to get an in-depth understanding of the data such as time series forecasting, segmentation analysis, inferential models, synthetic control etc. The main intention is to derive the hidden data insights and determine data with a forensic view. The data-driven insight will be accessible to all channels of the organization and Data Scientist will act like a consultant or a guide at this juncture.

Development of Data Product

Data Product is a technical resource that utilizes data as input and processes that data and give back algorithmically-generated results. The typical example of a data product is Recommendation Engine, this consumes user data and makes customized recommendations based on that data. To name a few data products, have a look at below box.

Data Product is different from Data Insight, the outcomes of Data Insight provide information to business executives and help them in making better and smarter business decisions. In contrast, Data Product delivers a technical functionality that summarizes an algorithm and is designed in such a way that it integrates with the core applications of the organization directly. Here are few applications that are incorporated with Data Product off the screen: Gmail’s Inbox, Amazon’s Homepage and Autonomous Driving software.

Conclusion

There is lot of space for Data Science in today’s high-paced IT oriented to be furnished and loads of opportunities for Data Scientists aspirants and it better to undergo Data Science Training. IoT(Internet of Things) market is witnessing large amount of data generation from different devices- laptops, tablets, smartphones, virtual machines etc.  Aspirants getting trained in Data Science or possess it as a tech skill in their profile may gain a highly sought and valuable skill for their career.

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