If you've ever gone through a job search in the data/analytics world, you've probably seen these two job titles (and slight variations on them) quite a bit: Data Analyst and Data Scientist. But what exactly is the difference between these two roles?
The short answer is it depends. In reality, these job titles are quite fluid. A Data Scientist at one organization might have roughly the same job description as a Data Analyst at another organization. Sometimes a Data Analyst may "cross-over” and do work typically reserved for a Data Scientist, or vice versa.
On top of that, there are other job titles that, depending on the organization, can overlap partially or completely with typical Data Analyst or Data Scientist roles. For example, a Product Analyst at a tech company could effectively be a Data Analyst that primarily focuses on product-related work.
That said, we can still draw some general distinctions between Data Analysts and Data Scientists. None of these distinctions will be true everywhere, but hopefully they’re enough to help you determine whether you’d rather pursue a Data Analyst or Data Scientist career path, or whether you should hire a Data Analyst or Data Scientist, if your organization is looking to incorporate data into your product or decision-making process.
Data Analyst: Use data to generate insights that can help the organization make better decisions. For example:
(I’m using examples from tech and sports because, well, that tends to be most of my audience. That said, the core skillsets of Data Analysts and Data Scientists have applications across a number of other fields)
Data Scientist: Build, maintain, and improve models and algorithms that, in many cases, become a part of the organization’s product. For example:
Data Analyst: Between the two roles, Data Analysts tend to work on less technical projects. Because of that, you’re more likely to see folks without highly technical degrees in these positions (e.g. There's probably a higher incidence of Data Analysts with Economics or Business degrees than Data Scientists with Economics or Business degrees).
That said, working with data naturally requires some technical skills. Here are a few of the most important for a Data Analyst:
Data Scientist: Data Science can be a highly technical profession, with a skillset that begins to resemble a Software Engineer’s. Because Data Scientists are often building models that will become a part of the product, they typically need to be more comfortable interacting with the product’s codebase. Here are some of the most important technical skills to do that work:
Data Analyst: Business/Strategy Consultant
Data Scientist: Software Engineer
Data Analyst: Probably less than a Data Scientist of equivalent experience.
Data Scientist: Probably more than a Data Analyst of equivalent experience.
High tech skillsets tend to demand higher salaries, so the more technical nature of data science means Data Scientists will tend to get paid more, all else equal.
Because there's a lot of overlap between these two roles, it's not unusual to move between them. I've known Data Scientists who later became Data Analysts and vice-versa.
Similarly, the skillsets for both of these roles are transferable across industries. That means you can cut your teeth in sports analytics and transfer those skills into data science at a tech company. Or you can get your start as a Data Analyst in academia and later move into a quantitative role on a political campaign.
Either way, you'll have highly transferrable skills and experience, and that's tough to beat.