Data Science will be introduced in schools in the wake of the NEP 2020

Vinod Kakumanu
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In the wake of the National Education Policy 2020, areas such as AI, coding, robotics, Data Science will be introduced. CBSE has already announced the introduction of these technical areas. We have already discussed some of these in our earlier blogs, and in this article we will discuss Data Science.

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What Is Data Science?

Data Science, simply put, involves using raw data to extract cogent information and insights that can then be operationalized or acted upon. It implies that data science uses data to identify patterns and variables at play, and innovate by exploring possibilities embedded in systems and scenarios.

The key here is the data itself so let’s comprehend its centrality first. Who can deny that the world we now inhabit is being shaped by the information revolution, and data is one of its primary drivers?

A study from 2103 revealed that 90% of the entire body of data that lay at the disposal of mankind at that point was collected in just 2 years. The body of data amount to 2.7 Zettabytes in 2013 and by 2020 the figure stood at 44 Zettabytes. It is for a good reason that Data has been dubbed “Oil of the 21st Century”.

Data Science grapples with the questions like how do we use the massive body of data and how do we use it to improve the real world. It continues to explore possibilities that data presents us with and is increasingly gaining influence and application in industry, commerce, research, and day-to-day life.

We are aware that every digital act leaves a digital footprint and all of it is potentially data. All our Google searches, traffic navigations, social media posts, and feeds from our fitness tracking gadgets are potential data. Data scientists delve and surf through these humungous waves of data to glean information that is actionable and to change the systems for the better.

By now, you probably have an overview of the scope of Data Science, and now it’s time to delve deeper.

Data Science—the Nuts and Bolts

Data science is fundamentally a multidisciplinary field as the synthesis of raw data into actionable information for driving innovation is an inter-disciplinary exercise. Data scientists delve into and surf through an ocean of data and are well versed in disciplines ranging from statistics, mathematics to data engineering, computing and software architecture, programming. Data Scientists, in their search for efficiency and innovation in systems, come up with models and usage of algorithms, etc. This demands the application of fields like Artificial Intelligence and its subfields namely machine learning and deep learning.

The five stages in which the lifecycle of Data Science can be encapsulated are--

• Data Capturing—acquiring and entering the data, signal reception, and data extraction

• Data Maintenance-- Data warehousing, data cleansing, data staging, data processing, data architecture

• Data Processing-- Data mining, clustering/classification, data modeling, data summarization

• Data Communication or Conveying--Data reporting, data visualization, business intelligence, decision making

• Analysis-- Exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis

Applications of Data Science

The application of data science is widespread and has changed the game in several human endeavors by creating time and cost-efficient systems.

To list some noteworthy areas where the intervention of data science has brought about substantial shifts include--

• Detection of systemic Anomalies—by analyzing systems and identifying aberrant patterns, data science can help detect financial frauds, crime, diseases, and climatic shifts

• Driving Automation and facilitation of decisions—a rigorous analysis of data, pattern establishment, and predictive capacity inherent in data science helps carry out background scrutiny and eligibility for credit, etc. it can help automate several audit systems used in banking and commerce.

• Categorization of data—a simple application of this is in email server classification of emails as important, Junk, etc.

• Prediction—data science has immense predictive value and it finds application across businesses in sales and revenue prediction, customer attrition, and retention projections.

• Discerning and identifying Patterns—identifying and grasping patterns in the functioning of systems enables data scientists to catch anomalies, predict the future behavior of complex systems like the stock market and economies, weather, consumer behavior, etc.

• Recognition—combined with AI applications, data science enables facial, voice, and text recognition in security and other systems

• Service recommendation—once the consumer preference is analyzed through the processing of data of previous choices, applications can recommend services and products to consumers, for example, Netflix movie recommendation or Amazon product recommendation

Data Science has brought humungous shifts in major sectors of the economy and society. Listed below are important sectors wherein the enhancements and innovations brought about by data science are palpable include--

• Medicine

Health care professionals and policymakers have been greatly empowered by Data Science. It has found application in a variety of ways ranging from the usage of Electronic Health Record (EHR) and personalization of healthcare to predicting the recurrent waves of the COVID-19 pandemic.

The influx of data from the clinical databases and portable fitness trackers etc. has enabled data scientists to analyze it and in turn empower health care professionals in diagnosing and putting forth prognosis, planning new therapeutic interventions.

Besides this, the researchers have now data at their disposal that enables them to identify and prioritize areas of research.

• Driverless Automotive

Autonomous vehicles or self-driving automobiles have been made possible by the synchronous application of data science, machine learning, and predictive analytics. The real-time data of the navigation course is collected via multiple miniature cameras and sensors that are used to enable these autonomous automobiles to adapt to speed lanes, avoid abrupt lane switches and choose the best routes, etc.

• Logistics

The logistics and shipping giants have turned to data science to create time and cost-efficient and optimal routes for the delivery of goods across the world. The innovations that aid logistical efforts are based on statistical modeling and algorithms that identify best routes by analyzing patterns of weather, traffic and ongoing construction, etc. an apt example of the application of data science in logistics would be the On-road Integrated Optimization and Navigation (ORION) tool of the logistic giant UPS. The application is helping the enterprise economize on fuel in millions every year.

• Finance

International Finance and banking have cut down substantially their costs and time by application of data science and leveraging machine learning. A few examples that give us a perspective on the role of data science in finance include JP Morgan’s Contract Intelligence (COiN) platform. What underlies this platform of the international banking giant is Natural Language Processing (NLP) that mines and processes critical data related to thousands of credit agreements annually. The NLP-based system has saved more than a quarter-million manhours for JP Morgan.

The anomaly detection capabilities of data science is being used by Finance Technology companies including Stripe and Paypal to identify, detect fraud and build systems that can prevent it.

• Cybersecurity

Malware detection is one of the most useful capabilities of data science and it has become the lifeblood of International cybersecurity firms. These firms use data science and machine learning to zero in on thousands of malware samples every day. Data Science is critical in detecting and preventing cybercrime and generating instantaneous alerts.

Afterthought

It is evident that the knowledge economy and society, and the systems that underpin them are increasingly getting data-driven. Data science is the reason for the unprecedented surge in the demand for data.

The dependence of economic and social systems on data science is not just an analytical possibility, it’s rather a mathematical certainty.