Unravelling emerging trends in the Data Science in today’s industry.
With the COVID-19 pandemic disrupting organizations, the demand for rising innovations like data science has accelerated further. While the demand is high, there is a genuine shortage of ability in the sector. According to Deloitte, data scientists and AI/ML engineers are the most looked for after experts.
Data science additionally assists organizations with utilizing advanced devices and technologies to automate complicated business processes connected with extracting, analyzing, and presenting raw data.
It is important that India is among the main 10 big data analytics markets, and the big data analytics sector is relied upon to observe an eight-fold development by 2025, from the current $2 billion to $16 billion.
High Demand for Data Scientists
With the data science market developing at an enormous pace, data authorities are in high demand. As indicated by IBM, by 2020, the demand for data scientists will grow by 28%. The number of analytics jobs in India multiplied between April 2016 and 2017.
This implies that ﬁnding data scientists will be a strenuous task. Around 50,000 jobs stay open because of the absence of talented experts in the field. HR teams that train their mathematicians and analysts in data science courses will be better positioned to stay up with the competition.
Major companies like Amazon, Cisco are hiring data scientists amidst this lockdown. Amazon is looking for a data scientist who is experienced in applying machine learning strategies to drive business, prototyping solutions, building machine learning models, and test them for better performance.
Similarly, Cisco is recruiting a senior data scientist to work on an analytical platform, define and design the overall architecture. The hired individual will lead the data scientists and data engineers, and chalk out an implementation plan based on the business requirements.
Companies are looking for data scientists who are skilled enough in programming languages, scripting for data analysis such as Python and R. In addition, hand-on skills of Hadoop and Spark are appreciated.
Evolution of Big Data Analytics
Effective big data analysis certainly assists organizations with gaining a huge upper hand and encourages them to accomplish their essential goals. Today, companies utilize various devices and technologies, for example, Python, to analyze their big data. Making it a stride further, we see more organizations concentrating on identifying the reasons for specific occasions that happen at present. That is the place predictive analytics plays a significant job by helping organizations recognize trends and foresee what can occur later on.
Examples incorporate utilizing the predictive analysis to help recognize customer interests dependent on their buying as well as browsing history. Sales and marketing experts can analyze those patterns to make more focused strategies to pull in new clients and increase the retention rates for current ones
Organizations, for example, Amazon likewise utilize predictive models to stock warehouses given demand across neighborhoods.
Cognitive Technologies and AI are Reshaping Business Processes
As per Deloitte University Press, it is presently conceivable to automate the tasks that require human perception abilities. Cognitive systems, for example, IBM Watson, Stanford’s Deep-dive, and Google’s Deep-mind, empower companies to comprehend unstructured information through natural language processing (NLP). Bangalore-based Talview uses IBM Watson to speed up the way toward hiring for its clients. To exploit such cognitive technologies, HR and L&D pioneers need to reskill their workforce and put resources in an adaptive learning strategy for the practical application of cognitive technologies.
Increasing Demand for Data Science Security Professionals
AI and machine learning adoption will, without a doubt, offer growth to numerous new roles in the IT and innovative industries. This will create new demand for data science security professionals. The business market, as of now, approaches numerous experts who are capable in AI, machine learning, data science, and computer science. However, there is as yet a requirement for more expert data security experts who can examine and process data to clients safely.
So to perform in those capacities, data security researchers must be knowledgeable with the most recent innovations like Python and the other most commonly utilized languages in data science and data analysis. Having a clear understanding of Python concepts can assist you in handling the issues identified with data science security.
Cloud-first Strategy Adoption
By the end of 2020, at least 33% of all information will go through the cloud. Business pioneers who can efﬁciently analyze multiple sources of data can take advantage of different opportunities to support outcomes across functions. For example, Xerox utilized a cloud-ﬁrst strategy to efﬁciently investigate data and decrease the attrition rate by 20% at its call center. Organizations, for example, KPMG and IBM, are genuinely adopting the “cloud-ﬁrst” strategy.
According to the Confederation of Indian Industry (CII) and KPMG report, India, as a country of more than a billion, comprehends the significance and challenge of connecting the ‘Bottom of the Pyramid.’ Cloud can drive this comprehensive development plan by giving a platform to scale the reach of education, healthcare, ﬁnancial services, entrepreneurship, and governance, among other areas.