Data Science Applications and Challenges in Engineering

The Global Big Data Analytics Professional market is anticipated to rise at a considerable rate during the forecast period, between 2023 and 2031. The market is anticipated to expand beyond the estimated horizon in 2024 due to the constant growth rate and increasing adoption of tactics by major players. Due to the widespread use of cutting-edge technology and the existence of major companies in Europe and America, the market is anticipated to have plenty of room to grow. An estimated 97,000 data analytics jobs are unfilled each year in India alone.

The Indian market has seen a massive 45% surge in jobs as a result of data analytics requirements. Because the industry demands a high skill set and a steep learning curve, there is a persistent imbalance between the supply and demand for these individuals. The schooling and training required for these positions has begun to garner traction in India.

Healthcare, IT & telecom, education, manufacturing, government, transportation & logistics, retail & e-commerce, and media & entertainment are the sectors into which the market is split.

IT and telecom dominate market share. Data analytics has helped the telecom business increase earnings by improving network performance, security, and customer pleasure. It has helped identify security concerns and fraud in call data analysis. Digital health records and the use of health-related technology are growing in the healthcare sector. As a result, a significant volume of health data is produced and kept on a daily basis. In the future, data analytics could have a significant impact on the healthcare sector.

Industry leaders including Google, SAP SE, AWS, IBM, Microsoft, and others are using various business strategies. They are using more data to better their tools and technology, generate new solutions, and increase their technology and analytics capabilities.

Engineers often face challenges with the quality and integration of data from disparate sources. Inconsistent and incomplete data can hinder accurate analysis and decision-making. Apart from this scalability is one more major concern needs to be addressed. Managing and analysing large volumes of data requires scalable infrastructure. Engineers must ensure that their systems can handle big data efficiently without compromising performance.

Since there is a growing need for engineers proficient in data analytics tools and techniques. Bridging the skill gap through continuous learning and training is essential. A solid foundation in fundamental data analytics skills is necessary to become a professional in the field. These include comprehending data structures, performing statistical analysis, and visualising data. It's also essential to understand the fundamentals of databases, including how data is stored and retrieved.

Programming is a key component of data analytics. Learn some of the most widely used languages in the area, such as R or Python. Strong libraries and frameworks for data manipulation, analysis, and visualisation are built into these languages. Hadoop, Apache spark, Power BI are the data analysis tools used in industry.

By Dr. Keerthana Prasad, Professor & Director, Manipal School of Information Sciences, MAHE, Manipal