Data Science – an Interdisciplinary Framework set to dictate the Future Businesses

“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 — that’s going to be a hugely important skill in the next decades.”

– Hal Varian, Chief Economist, Google & UC Berkeley Professor of Information Sciences, Business, and Economics

Data science, as an interdisciplinary field, continues to evolve as one of the most promising and in-demand career paths for skilled professional. It is considered to be a strong applications that encompasses and uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. The Data Science applies the concept of deep study of a large quantity of data, involving extracting some meaningful from the raw, structured, and unstructured data.?

Extracting the?meaningful information from large volume of data,?requires?the?use?of?statistical techniques and algorithm, scientific techniques,?different other technologies, etc. Thus, Data Science impacts and affects the kind of applications in most of the areas including Search Engines, Transport, Finance, E-Commerce, Health Care, Image Recognition, Targeting Recommendation, Airline Routing Planning, Gaming, Medicine and Drug Development, Delivery Logistics etc.

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It is an essential part of many industries now days, given the voluminous data that are produced, and is one of the most debated topics in technology domain. Over the years,?its popularity has grown, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. There have been lots of discussions and deliberations about how the data science works and what framework it provides to meet its objectives, which may vary depending on context, organizational structure, needs and the way businesses are carried out but one this is common, it is now imperative for businesses to give a space to Data Science in some form in their set-up and functioning to leverage its benefits. The simplest way to understand the broad functioning of Data Science, is to understand it core activities. For better understanding if we decompose the entire process involve in to Data Science to meets its objectives, it follows, primarily, a five-stage data science life cycle including – Capture, Maintain, Process, Analyze and Communicate as depicted below:

Capture:

Data acquisition, Data entry, Signal reception, Data extraction

Maintain:

Data Warehousing, Data cleansing, Data staging, Data

processing, Data Architecture

Process:

Data Mining, Clustering/Classification, Data Modeling, Data

Summarization

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Analyze:

Exploratory/Confirmatory, Predictive Analysis, Regression, Text

Mining, Qualitative Analysis)

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Communicate?

Data Reporting, Data Visualization, Business Intelligence, Decision

Making

Data Science makes use of several statistical procedures. Statistics?is?the?primary?asset of every?Data?Scientist due to its powerful analytical tools and capabilities, which covers data?transformations, data modeling, statistical operations (descriptive and inferential statistics),?and machine learning modeling.

It?is?essential?to?grasp?the?underlying?patterns?of?the?data?model?in?order?to?gain?predictive?responses?from?the?models.?Moreover, optimization techniques?may?be utilized to meet?business requirements.

With increased volume, variety with increased velocity of data, it is becoming very important for organizations and businesses to keep the process of processing data to find out the novice patterns that can help them not only to keep their functions more effective , productive and efficient but also make our the future prediction for business growth and directions to take precautionary and important decisions well ahead of time to ensure their survival and sustainability in this tough times and competitions. This is why Data Science is emerging and establishing itself as fourth stream of Research and sciences. According to the U.S. Bureau of Labor Statistics (2021), the data science and computer information research field is?expected to grow by 22% from 2020–2030?which is triple the rate of the average profession.

In recent years with changing requirements of industry and businesses, a lot of work at many Universities and Colleges are going on to understand the improved and enhanced applications and areas where it can be implemented. The big question still needs to be answered is “Will data science as an area of emerging research and development evolve into being its own discipline, which is claimed by well-known Computer Scientists and Turing Award winner Jim Gray,?or be a field that cuts across all other disciplines?” One could argue that Computer Science, Mathematics, Humanities, Statistics and Visual Designers, each are their own discipline, but they each can be applied to every other discipline which we all have been witnessing in last about 1 and half decade.

These reports, projections and expected growth has attracted the experts to explore and develop competency to get into the role of Data Scientists, one of the most sought after profile now a days. It?is important for?data scientists?to identify relevant questions,?gather?data from a?variety?of?sources, organize?it,?turn?it?into solutions, and communicate their findings in a way that positively?influences?business decisions. These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies.

However, in next decade or so, how Data Science would evolve and place itself, the answer to these question is in with researchers and educators. This is for sure that to advance and take the study and applications forward would require a comprehensive plan and committed efforts towards understanding and learning the vocabulary, methods, underlying technologies & tools from multiple disciplines and integrating them to strengthen and develop by applying the acquired knowledge that will take patience, but can be exciting.