Data Engineer, Data Scientists, Data Analyst, and Business Analyst — What are the differences and which career should you choose?
Data science came about as a compromise between research science roles and business analyst roles. The former used powerful methods but only indirectly influenced business decisions while the latter directly influenced business owners but wielded limited tools to do so. Data scientists make the most impact when they combine both sides together, mixing deep domain knowledge with the right statistical and engineering tools to make better decisions businesses are facing.
“Data Analyst,” “Business Intelligence Analyst,” the ever-so-vague “Business Analyst,” and my personal favorite “Strategic Business Analyst” (as if it makes the role of Business Analyst any less vague). If you actually look at the descriptions of these jobs, many of them have overlapping roles and responsibilities, and these four titles are almost never consistent across companies. After pouring over hundreds of job requirements, it seems that these roles will always fall into one of two buckets: a person who provides data and a person who provides insights. In other words, are we using Data as a Product (DaaP) or Data as a Service (DaaS)? Some people believe that this is the core difference between hiring a “data analyst” and hiring a “business analyst,” and for the sake of this post, I will refer to traditional data analysts as one’s intended to provide “data” and traditional business analysts as one’s intended to provide “insights.”
What makes a great data scientist? Having the creativity to unleash the power of data to transform the world.
Data Analyst vs. Data Scientist
With all that in mind, you might be wondering about another prominent data role — the data scientist. While it’s safe to assume there is some overlap in the type of work they do, there are significant differences between data analysts and data scientists.
Since the role of a data scientist is relatively new and sometimes nebulous, those in the field have worked to define and differentiate it from that of the data analyst. Let’s break it down based on skills and job duties.
Data analysts:
- Have moderate math and statistical skills
- Have a strong business acumen
- Have moderate computer science / coding skills
- Develop key performance indicators
- Create visualizations of the data
- Utilize business intelligence and analytics tools
Data scientists:
- Have strong math and statistical skills
- Have a strong business acumen
- Have strong computer science / coding skills
- Identify trends with machine learning
- Make predictions based on data trends
- Write code to assist in data analysis
Though data analysts and data scientists have different backgrounds and strengths, keep in mind that these roles can be a little squishy in how they’re defined. This means responsibilities may change depending on the organization.
