The Big Data hierarchy runs from the lowly data manager all the way up to bona fide data scientist. But what might one’s career path look like if they were determined to climb all the way to the top of the industry? Let’s take a look at how a career path in data analytics might unfold for the typical aspiring analyst.
At the bottom of the heap, the positions that could roughly be described as data management provide an entry point to the world of data analysis. Workers at this level will often be carrying out tasks that are little different from run-of-the-mill IT workers at most companies. Core skills include knowing one’s way around relational databases and query languages like SQL. The real goal at this level is simply to get one’s foot in the door, opening the way for bigger and better opportunities.
Business Data Analyst
The business data analyst will have developed more skills than the data manager. They will be looking at large data sets in order to tease out business intelligence and will do so using tools like MySQL, Excel and proprietary systems that are specific to their company. The business data analyst will have a solid background in statistics and will know how to massage large and sometimes unstructured data sets in order to discover patterns and knowledge that are relevant to the firm’s operations.
Machine Learning Specialist
A machine learning specialist may be employed in academia or at an individual firm. They will be expert in the use of a number of computer languages, such as Python and R, and they will have an expert-level knowledge of artificial intelligence, statistics, and machine learning. The machine learning specialist will often work in domain-specific areas and concentrate only on certain narrow classes of problems. However, they will have the generalized knowledge and the flexibility to quickly adapt to new challenges.
The apex Big Data worker is the data scientist. Data scientists develop groundbreaking, cutting-edge techniques for solving generalized classes of problems. They are most likely to be employed in academia or the largest corporations.