Every industry generates data daily and relies highly on them for their business continuation. The evolution of information technology and internet penetration makes data science more powerful. This article explains why you should choose data science as your career and explain various career options in data science. Please read on if you are aspiring to become a data scientist.
Why should you become a Data scientist?
The pandemic has moved the business world into digital platforms making it more beneficial and reaching many consumers. Therefore businesses, irrespective of their size, had to process e-commerce and business-related data and utilize strategies to optimize them to process their future business plan.
Data scientists formulate patterns, identify trends, analyze data and solve business-related challenges using predictions and patterns. Therefore the role of a data scientist works around humungous data and preparing them. Also, the evolving technology makes the learners learn and upskill their technical abilities continuously.
Career options in Data science:
Data science is a vast space that provides various options to explore. As there are several functionalities, Data science offers carrier options which are passionate about building their technical and non-technical skills related to data science:
Data architects and administrators:
Statistics estimate over 180,000 job vacancies will be for data architects and administrators by 2030.
The data architects are responsible for designing and developing the database and organizing the data for proper representation. Data architectures should ensure the database’s scalability, performance, and reliability. However, they don’t take care of backup, recovery, troubleshooting, or other continuity procedures.
Data administrators are responsible for the operation of the database and ensuring database security. Other key data administrators’ responsibilities include managing database recovery and backup procedures.
Data engineers access and process real-time data and play a significant role in any technology-driven company. They are responsible for creating data pipelines and analysis. In addition, they are responsible for maintaining data volumes and infrastructure that uses programming languages such as Python, Java, advanced SQL, and No SQL.
Analysts work around raw data and with marketing teams, sales, customer support, and other finance-related domains. They create reports, study data, and help make crucial business decisions. In addition, they make use of tools such as Tableau and Excel and develop strategies.
Machine learning engineer:
Data science and machine learning are often mistaken as synonyms. This is because they both are so closely related but have significant differences between them. Machine learning engineers combine the work of software engineering and data science in the most appropriate proportion. Machine learning engineers develop software and models, and thus they are employed as seniors.
To take up Data science as the career option, you can begin by taking a Data Science certification course that helps you develop technical and programming skills such as R programming, SQL, Python, Java, and C. The course should also teach you the platforms such as Apache Spark, Hadoop, and significant data visualization tools.
Data science careers have evolved from analytics to predictions and from statistics to decisions. Therefore Data science is applied across every industry vertical. As there is a sky-rocketed demand for data scientists, becoming an expert in programming languages and developing advanced technical skills makes you shine in a data science career.