In 2012, IBM revealed that 2.5 quintillion bytes of data were being created per day — an enormous sum that humankind had never known before. Since then, the volume of the world’s data has not only continued to increase, but it’s arriving at a faster and faster pace.
However, data by itself doesn’t have much value. After all, a pile of numbers and data files is just that: a pile of numbers and data files. The real value of data comes from making sense of the abundance of information. That’s why businesses and organizations across countless industries are investing in forward-thinking data talent — to leverage its predictive power, craft smart business strategies, and drive informed decision-making.
The sharp and strategic people who do this job are data scientists, data analysts, machine learning engineers, and business intelligence analysts — among other titles — and these professionals are in high demand. In 2018, the jobs platform Glassdoor ranked data scientist as the Best Job in America for the third year in a row, with a median salary of $110,000 and more than 4,500 available positions. Additionally, five other data- and analytics-related roles made the list of the top 50 jobs, ranked by number of openings in the field, salary, and overall job satisfaction.
Companies are quickly recognizing the vital need for data knowledge, impacting a vast array of industries including eCommerce, health care, finance, and sales — to name a few. In order to stay competitive and grow their businesses, leaders are investing in their future by strategically training and hiring talent to ensure proficiency in key skills.
Three of the most prevalent technologies transforming how we understand and use data are SQL, Python, and machine learning — and all are great entry points into the field. The first two are programming languages used to gather, organize, and make sense of data. The last is a specific field in which data scientists and machine learning engineers, using Python and other technologies, enable computers to learn how to make predictions without needing to program every potential scenario.