Data scientists are responsible for discovering insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. The data scientist role in data analysis is becoming increasingly important as businesses rely more heavily on big data and data analytics to drive decision-making and as more businesses lean on cloud technology, automation and machine learning as core components of their IT strategies.
A data scientist’s main objective is to organize and analyze large amounts of data, often using software specifically designed for the task. The final results of a data scientist’s data analysis needs to be easy enough for all invested stakeholders to understand — especially those working outside of IT.
A data scientist’s approach to data analysis depends not only on their industry, but also on the specific needs of the business or department they are working for. Before a data scientist can find meaning in either structured or unstructured data, business leaders, departments and managers need to communicate what they’re looking for. As such, a data scientist must have enough business domain expertise to translate company or departmental goals into data-based deliverables such as prediction engines, pattern detection analysis, optimization algorithms, and the like.
Data scientist salary
In 2016, the Bureau of Labor Statistics reports that the average salary for a data scientist came in around $111,800 per year. It’s a fast growing and lucrative field, with the BLS predicting jobs in this field will grow 11 percent by 2024. Data scientist is also shaping up to be a satisfying long-term career path. In Glassdoor’s 50 Best Jobs in America report, data scientist ranked as the best job across every industry based on job openings, salary and overall job satisfaction ratings.
What does a data scientist do?
A data scientist’s chief responsibility is data analysis, a process that begins with data collection and ends with business decisions made on the basis of the data scientist’s final data analytics results.
The data that data scientists analyze, often called big data, draws from a number of sources. There are two types of data that fall under the umbrella of big data: structured data and unstructured data. Structured data is organized, typically by categories that make it easy for a computer to sort, read and organize automatically. This includes data collected by services, products and electronic devices, but rarely data collected from human input. Website traffic data, sales figures, bank accounts or GPS coordinates collected by your smartphone — these are structured forms of data.
Read the source article at CIO.com.