Data Science is a combination of different tools, algorithms, and machine learning principles with the objective to find out the hidden diagrams from the information. Data Science is especially used for decision-making and prediction making. It involves the developing techniques of recording, storing, and analyzing data to effectively take out the practical information. The objective of data science is to achieve insight and knowledge from any sort of data, structured, and unstructured.
The number of data scientist positions is expected to grow by 15% between 2021 and 2029. With such a scarcity of data scientists, many companies face a critical skills gap that cannot be closed by hiring. The costs for such a gap can be significant. In a data-driven marketplace, companies that lack the personnel to maximize the value of big data operate at a competitive disadvantage
The skills gap is a hole between what employers expect a specific target to be done or want their personnel to be capable to do, and what these employees can truly do when they are working on a specific task. With the increasing abundance of business organizations, data scientists are in warm demand. Data Scientists pushed by way of the widespread explosion in the digital science area, there is a primary issue of providing in the organization. Data Science is struggling from a desperate scarcity of capacity. Professionals demand an abundance of explanations for the widening competencies gap.
Unfortunately, there is a lack of professional data scientists in this digital world, who are required to perform data tasks. Changing technology on a continuous basis, and educational changes to meet the in-demand technology creates big skill gaps. Most of the professionals can't easily understand the requirements of the job because of insufficient experience, as the changing innovations are required to gain continuously and all the professionals are not able to do that. Also, there is a big gap between learning and professional life. You cannot execute everything exactly what you have learned as the nature of work is different in all organizations.
The colleges can bridge the skill gap that is increasing continuously. Changing the curriculum and education system is a good strategy but a slow process. Depending on a massive overhaul of educational institutions as the 'only strategy' may not serve the ever-evolving technology industry in its entirety.
Reskilling or upskilling is the need of the hour to keep up with relentless advancements. That's why it's important for professionals and companies alike to invest in learning and development to build their human capital. As an industry, it will need to upskill or reskill a significant number of engineers. It is not just the question about making entry-level people smarter and better, the key will be reskilling and upskilling the vast numbers of professionals with pre-existing skills in the industry.
Data Scientist's primary job role is to extract consumable information from structured and unstructured data with computer programming tools and processes. Their job also includes creating methodology and blueprint to present information to stakeholders. They are also supposed to maintain databases.
A Data Analyst has the responsibility of analyzing the data, identifying trends, and creating a predictive model based on data studied. Another critical responsibility of a Data Analyst is to translate findings into reports, which can be understood by the management, and help them accurately visualize the possible outcome. They are also supposed to maintain databases and data systems.
Data Engineers are required to study data, develop data set processes, prepare the predictive model, and build algorithms through which stakeholders can easily consume raw data. It may include developing dashboards and reports that can be accessed and used by all stakeholders. Data Engineers need to have strong communication skills to be able to understand clients' requirements and objectives.
The job of a Data Mining Engineer is mainly extracting data from an extensive database and analyzing them. They are also responsible for building and maintaining software and digital infrastructure to study big chunks of data.
Data Architect's role is to ensure that data used in creating a blueprint of a project is stable, secure, and available to all stakeholders at all times. The job role includes collating, organizing, centralizing, maintaining, and protecting a company or client's data.
This job role includes critical responsibilities such as extraction of data using statistical methodologies and analyzing, organizing, and contextualizing data and its subsets. A Data Statistician is supposed to conduct tests to determine the reliability and accuracy of data.
Data mining, extraction, testing, analysis, and application for creating a blueprint is a wide field of work that requires management to optimize the resources being used on a project. A Project Manager's role is to oversee and guide the execution of the project. They act as a medium between the team and clients to communicate requirements and changes in the project.
Experts believe Data Science to be the most future-looking skill set given the increased usage of data analytics and machine learning to make more informed business decisions and run their businesses. It has largely helped organizations to obtain meaningful insights from unstructured and raw data.
Data scientists' jobs mainly require them to help the organization make smart investment decisions, target the right consumers, assess associated risks, and contribute towards capital allocations.
After developing your data science skills and gaining years of experience, one can explore different domains like marketing, sales, data quality, finance, business intelligence, etc., and even serve as a consultant with leading data-driven firms.
Author: Dr. Santosh Sonavane
Pro-Vice Chancellor, Vijaybhoomi University
Dean, Vijaybhoomi School of Science and Technology
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