How Has Pandemic Shaped the Data Science Roles for the Future?

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A report by IBM in 2017 had predicted that there’d be a significant rise in the data science industry by 2020 and indeed since then, there has been a 56% growth in the industry with data scientists becoming the ‘new sexiest job’ going around. While small to large businesses suffered a downfall due to the recession and ongoing Pandemic, one industry that did not suffer and only had a rise in demand was Data Science. One by one, as every business focused on digitizing themselves, all turned for the help of Data Science Professionals to succeed. Demand along with salaries, has increased as have the needed qualifications.

No More an Obsolete Job

The Pandemic has molded and reshaped many job industries including the Data Science industry, changing their very nature and proceedings. Most of us are aware how pre-Covid there had been multiple reports that assessed Data Science roles to be temporary and only a result of a fleeting trend for AI and machine learning. As reports claimed these data science roles would become obsolete in a few years as the role would be taken up by machines and not humans. The pandemic stirred up and changed this entire perception as businesses scattered to seek the help of the same data science professionals they didn’t have any regard for maybe a few years ago. Even if data science was to become obsolete, it wouldn’t be anytime soon until machines are humanized to their utmost.

Especially seeing the current situation of the data science industry and its steady rise of opportunities and rapid evolution with new emerging concepts and roles, Data Science is here to stay and that has become obvious.

Top 3 Emerging Data Science Roles Post-Pandemic

The rapidly evolving data science industry and its rising demands have led to the rise of more complex technologies and with it more complex and essential roles. Let’s see what these are one by one:

●       Data Engineer

Some of the responsibilities of a data engineer include creating clean data pipelines, attaining quick data transferences, working out data plumbing issues, sorting complex data ingestion, etc. Their work is to arrange the huge array of data into master tables, making the data accessible, cleansing and integrating it for analysis, and improving the organization’s big data systems.

Skills and Qualifications of a Data Engineer

A data engineer must have a bachelor’s degree in computer science/ engineering or in courses that are relevant or parallel to these.

As a Data Engineer one needs a strong grip on basic to complex programming languages such as Python, SQL, Spark, Java, Scala, etc. They also need to be acquainted with data warehousing solutions such as Amazon Web Services at least at the base level and also an understanding of working with ETL tools, i.e. Extract, Transfer, Load. In-depth knowledge in Machine learning algorithms and data structures is also required of data engineers.

Apart from these, important soft skills that a data engineer should have are:

  • Proper Communication Skills
  • Collaborativeness
  • Presentation
  • Leadership Skills
  • Conflict Management
  • Proper Work Ethic and Discipline

●       Data Analyst

While a data engineer works on making the data accessible, a data analyst’s work is to gather and study the data and use it to solve organizational problems. A data analyst is needed in a range of industries from finance, medicine, business, and even to the government. A data analyst is responsible for gathering data, cleaning it, modeling the data, interpreting it, and when needed presenting it.

Necessary Skills and Qualifications of a Data Analyst

  • A base degree in any relevant domain is needed. These may include mathematics, statistics, economics, business information systems, computer science, etc.
  • Relevant specialized courses for the needed data analyst skills and knowledge.
  • Structured Query Language
  • Microsoft Excel
  • Critical Analytical and Numerical skills such as in Linear Algebra and Calculus
  • Python or R
  • Data Cleaning and Visualization
  • Presentation Skills
  • Communication Skills.

●       Data Architect

Among the rising data science roles, another top-demand position is the Data Architect. They are specialists who manage and design data systems, coordinate several data sources in the organization, decide on policies on how data is stored, and update technologies into prevalent technology infrastructures. They are the intermediaries between the IT and the business departments within an organization by aligning collection and distribution policies and the objectives. They work with various members of the team like data engineers, data scientists, data miners, data analysts, etc. in various areas like data storage, data security, data collection, data security, data systems, and access.

Necessary Skills and Qualifications of Data Architect

  • Adept Business Skills
  • Knowledge of Programming Languages i.e. SQL, Python, Java, etc.
  • Data Modelling
  • Machine Learning
  • Natural Language Learning
  • Databases
  • In-depth knowledge of the Cloud Infrastructure
  • Using Visualization Tools
  • Critical Analytical Problem Solving
  • Mathematical and Statistical Application

The field for Data Scientists, Analysts, Engineers, and more is a complete minefield due to the high demand for these positions. However, the supply remains low since many fail to fulfill the needed qualifications or due to their lack of knowledge and preparation for said jobs. You, on the other hand, can benefit from the situation and prepare yourself with the help of the appropriate Data Science Certifications and enjoy the career benefits and high salary it comes with. Data Scientists, Engineers, Analysts, and Architects are today not only some of the top-paid careers but also offer utmost job satisfaction making them also high in demand.

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