The emergence of big data has created a need for professionals who can take advantage of these large data sets. According to McKinsey Global Institute (MGI), the United States faces a significant shortage of people with deep analytical skills as well as managers and analysts with the knowledge to use big data to make effective decisions.

“Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers,” MGI said. The opportunity for savings with the creative and effective use of big data in a domain such as U.S. health care is more than $300 billion. Retailers that use big data can increase their operating margin by more than 60 percent.

The opportunity for savings with the creative and effective use of big data in a domain such as U.S. health care is more than $300 billion

Analytics professionals use data mining techniques to analyze, explore and provide insights into specific information. Yet there is some confusion regarding two of the most popular job titles in this new and growing field.

Data Analyst vs. Data Scientist: Key Differences

Ambiguity surrounds these titles because “data science” is such a new term and field, according to Thomson Nguyen, data science lead at Square Capital. “Complicating the problem is that lots of companies have different definitions of what a data scientist is and what they do,” he wrote. Kashyap Dalal, chief business officer at Simplilearn, added that he has seen some startups “actually use data scientist as a fancy designation to attract talent for their analyst roles.”

Data analysts typically are well-versed in SQL (language used in programing for managing data in database systems) and business intelligence tools and packages. They generally know some regular expressions (text strings for describing search patterns) and have a beginning-to-intermediate level of statistical knowledge.

Data scientists have machine learning and advanced programming or engineering skills. They can manipulate data, enabling them to build statistical models, and have advanced statistics knowledge.

Data Analyst vs. Data Scientist: Salary and Career Potential

The Bureau of Labor Statistics does not collect salary data and make employment outlook projections for data analysts and data scientists. These professionals are classified as operations research analysts or statisticians. Both positions earn a median annual wage of more than $78,000 and are projected to grow by at least 30 percent by 2024.

According to PayScale, data analysts earn an average salary of $56,634. Data scientists in information technology earn $91,588.

Median base salaries ranged from $96,000 at the lowest level of experience to $150,000 at the highest level of experience

Executive recruiting firm Burtch Works conducted a 2016 salary study of data scientists. Median base salaries ranged from $96,000 at the lowest level (level one) of experience to $150,000 at the highest level (level three) of experience. More than 92 percent of data scientists held an advanced degree; 44 percent had a master’s degree and 48 percent had a Ph.D.

The annual study found that 59 percent of level one (or early-career) data scientists’ highest level of education is a master’s degree, compared to 48 percent in the previous year. “These data seem to suggest that those interested in data science careers are seeking a faster route to the workplace,” authors of the study said. Due to data science-oriented master’s programs becoming more popular, they are able to pursue such opportunities.


Pursuing a Career in Big Data

The online master’s degree in analytics from Notre Dame of Maryland University prepares students for advanced roles in the growing field of big data. Graduates can pursue careers in positions such as data analyst and data scientist. In a convenient and flexible learning environment, students gain multidisciplinary competencies in knowledge management technologies, qualitative processes and economic principles of change risk management. This program is offered fully online.