IMPORTANCE OF DATA SCIENCE AND RESPONSIBILITIES OF A DATA SCIENTIST
- September 5, 2019
- Posted by: iNurture
- Category: Blogs
With a huge volume of data generating in breakneck speed every minute, the digital world is producing over 2.5 Quintilian data on a daily basis. By 2020, the global production of data is expected to be 50 times more than that recorded in 2011. Data warehouses keep flooding in with raw data every day, which if not deciphered and structured into functional formats will go worthless. No wonder, Data Science is the favorite buzzword in the current industrial space.
WHAT IS DATA SCIENCE?
Data Science is the technology that unravels the real value of data by fine-tuning structured and unstructured data into valuable resources. It unlocks patterns, trends and inferences hidden in a particular piece of data, drawing a comprehensive conclusion relevant to the data.
Data Science is a multidisciplinary approach involving Mathematics, Statistics and Computer Science, which leverages advanced techniques like machine learning, data mining, cluster analysis, etc. to assist in making important business decisions. The massive increase in the volume of data and the rising demand for insightful conclusions from data have played a crucial role in swelling the Data Science market. According to a report by Market and Market, the global Data Science market is projected to reach USD 101.37 billion by 2021 from USD 19.58 billion in 2016, at a compound annual growth rate of 38.9%.
A BRIEF HISTORY OF DATA SCIENCE
Although Data Science is an emerging technology, its history can be retraced to the 1960s when Peter Naur used it as the substitute for Computer Science. The term Data Science was first used by Peter Naur himself in his study, Concise Survey of Computer Methods, as a collective terminology for data-processing methods. However, the technology did not find its true meaning until 1996, when the International Federation of Classification Societies held a conference titled Data Science, Classification and Related Methods. Data Science branched out as an independent discipline later in 2001 owing to efforts of William S. Cleveland.
IMPORTANCE OF DATA SCIENCE IN THE INDUSTRY
Renowned computer scientist Jim Gray has stated Data Science as the ‘fourth paradigm of science’ in his book named ‘The Fourth Paradigm: Data-Intensive Scientific Discovery’. The demand for Data Science is increasing exponentially in every industry. Nearly all the industries are powered by data-driven decisions now. Here are a few of the many reasons why such a nascent technology is gaining such unparalleled popularity:
Strengthens the Decision-Making Capability of an Organization:
Data Science leverages quantifiable evidence-based data and channels organizations toward better business decision-making.
Empowers an Organization to Adopt Best Practices:
Data-driven decisions enable a methodical workflow in an organization, which enhances the performance and productivity of the organization.
Gauges the Impact of Business Decisions:
Data Science not only assists in the decision-making process but also evaluates the repercussions of an implemented decision, furthering the efficiency enhancement of the product or service.
Facilitates Customer Acquisition:
Data Science aids in identifying potential customers for a particular service/product by analyzing customer behavior and requirement.
Aids in Product Innovation:
Data Science helps in innovating a product by analyzing the conventional designs and evaluating market requirements.
Responsibilities of a Data Scientist
Data scientists require perseverance and the right tactics to understand the pattern hidden in the data. Followings are some of the responsibilities that data scientists undertake:
• Identifying and collecting relevant structured or unstructured data sources and data sets for business needs.
• Processing, cleansing and validating the integrity of data used for analysis.
• Discovering hidden patterns, trends or insights in the analysed data
• Data mining using advanced methods.
• Preparing the analysed data for predictive and prescriptive modelling
• Presenting information obtained from the data in an understandable format using data visualization techniques.
• Proposing strategies to tackle business challenges.
• Recommending cost-effective changes to existing procedures.
• Methodically executing analytical experiments to help solve various problems.
• Exposing frauds and anomalies in the market.
• Linking different data to build products that meet the aspirations of the target customers.
Why is Data Science the “Sexiest Job of Twenty-First Century”?
Harvard Business Review has labelled Data Science as the “Sexiest Job of Twenty-First Century”. Here are the reasons why:
Highly Coveted Careers:
As per LinkedIn, Data Science is the most rapidly growing job sector, which is predicted to create over 11.9 million jobs by 2026.
Lucrative Career Opportunities:
With an average annual salary of $166,100, as per Glassdoor, Data Science is ranked among the highest paying jobs today.
Prestigious Job Roles:
As companies rely on data scientists for smarter business decisions, data scientists hold highly prestigious positions.
Including banking, healthcare, e-commerce, entertainment, education, and so on, Data Science is associated with almost all the domains.
Usage of machine learning techniques in Data Science enhances product quality in real-time, making them customizable and of better quality.
From a few data-intensive industries, Data Science has now dispersed to the broadest extent possible. The sector is one of the highest revenue bidders in the global front, besides being a robust job provider. The diverse product portfolio adopted across different industries, the mushrooming demand for advanced methods of business decision-making and the astronomical growth of the Data Science industry are all intricately interlinked.
Although with the rapid advancement of the sector more and more professionals are expected to join the Data Science sector, there are currently 97,000 Data Science-related vacant job posts in India. This demand–supply gap is a result of the dearth of quality talents in the country, which pinpoints the incompetent curricula followed in most of the educational institutions or the lesser number of educational institutions offering Data Science courses in India than the calculated prerequisite.
iNurture is one such institute that offers Data Science course as a specialization for undergraduate programs in different parts of the country. The Programs are delivered through a set of industry-oriented curricula, which impart world-class knowledge to master the technology.