Data Engineer, Data Analyst, Data Scientist: What’s the Difference?
If you want to make sure you don’t lose your job in the next five years, you probably want to know something about Big Data, or even switch to a data-related career. But what do Big Data jobs entail?
Speaking at last week’s Women of Silicon Roundabout conference in London, Dr. Rebecca Pope, the head of data science and engineering at KPMG, said you don’t need to be an excellent statistician or a high-class mathematician to work in data science or analytics. Nor do you need a lot of prior programming knowledge (although that always helps).
However, you do need an interest in statistics, you do need to be willing to learn how to code, and you do need to know how to do some high-level mathematical operations.
Pope herself didn’t study pure statistics (she’s a neuroscientist). Nor did she study programming. Instead, she learned how to program after graduating, and she attended “endless hackathons.”
“I started learning R. But my advice would be that if you are launching a career in data science you should specialize in Python… make Python the first language you learn,” said Pope.
Data scientists are not just statisticians, Pope added: “A statistician is interested in building a model that builds a relationship between a variable and an outcome.” A data scientist wants to do something more: predict. They train models that can predict the future as accurately as possible.
These kinds of jobs come in stages. A business use has to be established, and raw data must be wrangled; then the algorithms are written and tested on the available datasets. If they’re machine-learning algorithms, they learn to predict the future. Visualizations and APIs have to be created so that the business can engage with the resulting product.
Different sorts of professionals are engaged at these various stages. Alternatively, “generalist” data scientists are capable of serving in many roles related to information-gathering and analysis.
What skills do data engineers need? Basically, it’s a lot of software engineering and dataset preparation.
These engineers are tasked with “the representation and movement of data so that it is consumable and usable,” Pope said.
