Big data is a buzz word these days and with good reason. In 2015, 1.9 million data analytics jobs were created and another 4 million in support functions for those positions, according to Bernard Marr, the author of Big Data: Using Smart Big Data, Analytics and Metrics to Make Better Decisions.
The term “data science” was first used in 2001 by the statistician William H. Cleveland. A career in data science came to the forefront in 2012 when Harvard Business Review published an article, “Data Scientist: The Sexiest Job of the 21st Century.” Previously, statisticians had been doing the same kind of work: they compiled, processed, analyzed, and interpreted data to generate usable information. Today, the age of data was now being (?) recognized by the larger public.
Data-related work began at the end of the 20th century when people started referring to “Knowledge Discovery”, “Data Mining” and “Advanced Analytics” to discuss how computers could discover useful patterns from large datasets. The patterns came to be identified as cluster analysis for a grouping of data, anomaly detection for working with unusual data, dependencies for identifying associations by rule mining, generalizations for predictions of classification and regression models, and compact representations for summaries of the data set.*
“Up to 80 percent of the time and effort in big data analytic projects is spent on cleaning, integrating, and transforming the data,” explains Pete Ianace, chief strategy officer and executive vice president of No Magic Inc. Doing such work requires a thorough education in the languages, programs, and analytic capabilities that make data usable. Whether it’s a sexy job or not, it is undoubtedly one of the most valuable careers for the future.
Forbes magazine reported on research conducted across 2015 by WANTED Analytics, a job tracking and analytics company. They found that being able to work with data is lucrative; the median advertised salary for professionals with big data expertise was $124,000 a year. The top 5 industries hiring data analysts were:
- Professional, Scientific and Technical Services (30%)
- Information Technologies (19%)
- Manufacturing (18%)
- Finance and Insurance (10%)
- Retail Trade (8%)**
Jobs in this category include: Software Engineer, Big Data Platform Engineer, Information Systems Developer, Information Security Analysts, Management Analysts, and Data Quality Director. Sales representatives in many fields need data analysis capabilities.
Statistical and quantitative analysis is at the heart of data analysis but so is data mining, machine learning, a variety of programming languages, and creativity. Being able to use older languages like SQL is as important as developing aptitudes for more recent languages, but it’s the constant innovation that means a creative, problem solving mind is truly key to success in this growing field.
NEIT helps prepare tomorrow’s IT leaders with a course on Data Warehousing and Data Analytics in the Master’s of Science in Information Technology degree. Find out more about the course and your future options here.