Top 10 Data Science Career Options That are on The Hype
Data science is one of the most appealing industries with a lot of features and opportunities. With humans using 2.5 quintillion data per day, the data landscape is at a dynamic space, almost mimicking the real global connectivity. New technologies to tackle data overwhelming are introduced year after year and the transformation is likely to continue into the coming decade. The rise for data-related practitioners in the fast-moving world is very real. According to a report, data related jobs post 2020 is anticipated to add around 1.5 lakh new openings. For the past four years, data scientist has been named the number one job in the US by Glassdoor. The US Bureau of Labour Statistics reports that the demand for data science skills will drive a 27.9% rise in employment in the field through 2026. Henceforth, Analytics Insight brings you a list of data science jobs that are on the hype.
Data scientists help in performing data preparation tasks like cleaning, organizing and many more that allows companies to take strategic actions. They handle large datasets and uncover useful patterns and trends in data. Data scientists are technological persons who are fluent in data analysis software and use them to predict market patterns. Data scientists practice advanced analytic technologies such as machine learning and predictive modeling which are examined through enormous amounts of structured, unstructured, and semi-structured data to identify definite patterns. The opportunities for data scientists with more skills are expected to rise in future.
Data analyst is one of the promising career options in the data science field. Data analysts transform and manipulate large data sets. They also assist higher-level executives in gleaning insights from their analytics. A data analyst is answerable for tracking web analytics, analyzing A/B testing, operating and altering large datasets to be lined up with anticipated analysis for businesses. They also work jointly with the management to develop a priority-based list of corporate and data requirements for their projects. Data scientists are already in huge demand in the data science landscape.
Machine learning engineers are responsible for creating data funnels and delivering software solutions. Besides, they are also responsible for exploring and applying suitable machine learning algorithms and tools. By learning and transforming data science prototypes and picking appropriate datasets and representation techniques, they also design machine learning systems from the beginning. Machine learning engineers often use other technologies like deep learning and artificial intelligence to create automation in data analysis.
