What is Data Science is a question that has plagued many in the last two decades. But the answer couldn’t be simpler.
Data Science reveals trends and generates insights that businesses can use to make better decisions and develop more innovative products and services. Well, not just businesses, but its extracted value extends beyond businesses and into academic and social pursuits as well. There is virtually, and arguably, no industry that can't benefit from it.
Data Science roles and their function are relatively new in the market. The primary data science job titles are Data Scientist, Data Analyst, Data Engineer, and Data Architect. The common thread in all of these roles is the love for Mathematics, Statistics, Physics, Psychology, and, most importantly, coding. We've tried to summarize these data roles and responsibilities, so you know what to expect from each role:
Data scientist roles and responsibilities include using machine models to solve challenging problems in all business areas. These professionals have mastery in using Natural Language Processing to mine unstructured data and extract actionable insights. They signifucantly work on structured data with advanced statistical methods and algorithms to perform analyses. They interpret the results and visualize the data to convey the best action points to the management and stakeholders to achieve its business goals.
Data Scientist is the highest paying job profile in the data science function with the highest education and experience requirements. Today, most data scientists are majors in mathematics, applied statistics, operations research, computer science, physics, and aerospace engineering.
A Data Analyst generally has to shuffle between strategic and operational initiatives. They extract data, analyze it, and convey data-driven insights to the decision-makers. The other two critical areas of work involved in this job role are developing predictive analytics models to support business initiatives and manage risk and compliance data to make it more understandable.
The seniority at which Data Analysts are placed varies from the -skillset and the experience they possess. But, to sum it up, the experience of working for real-world problems, exposure to advanced software programs, and knowledge sharing with experts will likely put professionals on the data analyst track.
Data Engineers are the people who ensure the data is clean, organized, and ready for analysis. They are the ones who lead big data initiatives — the large scale and complex ones. They collect, manage, analyze, and visualize large datasets and turn them into actionable insights using various techniques, toolsets, and cloud platforms. All that overwhelming data truly gets its shape at the hands of these data engineers.
Professionals looking to work in the data science field usually turn to Data Engineering as their common choice. It is said to be the profile that guarantees success for data science professionals in the future.
Data Architects are analytical and creative minds that are technical experts who adapt data ow management and data storage strategy. They create the database from zero; they design how data is retrieved, processed, and consumed. They also control access to the data and continually improve the way data is collected and stored. They continuously innovate ways to enhance data and reporting quality, reduce redundancies, and offer better data collection sources, methods, and tools.
A few other data science roles are BI Analyst, Database Administrator, Machine Learning Engineer, Statistician, and Data and Analytics Manager. More and more professionals from all over the world are entering this new field every day.