Data has become a vital part of our daily lives, from online shopping habits to social media interaction and so on. Today’s Technological advancement, particularly computer languages, and tools for data collection, manipulation, and interpretation, is also contributing to the growing popularity of data science.
The International Data Corporation predicts global data will increase by 61% to reach 175 zettabytes by 2025. As a result, organizations across all industries struggle to keep up with the unprecedented volume of data generated daily. With the data surge comes the need for professionals who can analyze, interpret, and derive value from it. This is where the data scientists and analysts come in. The two roles may be similar but different. Fortunately, UNSW Online has highlighted the major differences between the two roles.
Data scientists use key skills, including artificial intelligence, computer science, and coding, mathematics, and statistics, data set analysis, and machine learning, for data processing and interpretation to make valuable business predictions. On the other hand, a data analyst sources analyze and identifies data trends to solve present business challenges. They employ key skills like business intelligence, data modeling and visualization, ETL tools, and business consulting.
The data growth and accompanying value extraction requirements emphasize the long-term demand for data science and analytics jobs. Businesses across industries are adopting data-driven approaches as a fundamental function, leading to a growing demand for competent data experts, resulting in shortages. Industries will require hiring more specialists to keep their businesses running and profitable.
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