What distinguishes the job responsibilities of a statistician from those of a data analyst?

There might be differences in academic credentials of statisticians and analysts but the lines are getting blurred in the industry, especially in the healthcare world. Nowadays, many of the analyst roles are filled by biostatisticians, epidemiologists and health economists. These candidates upgrade their skills according to the job requirements. Most of them know SQL, Python, R, SAS, and Tableau. Where statisticians bring clinical trial design and analysis skills, epidemiologists top it with observational studies and population health analysis, and economists supplement it with healthcare economics and policy expertise. Therefore, today’s complex healthcare problems require solutions originating from amalgamation of multiple expertise.

Both statisticians and data analysts must be communicative, investigative, analytical, practical, and honest people. They use a different set of skills to find answers to questions, and solutions to problems. From my perspective, a statistician may influence the handling of the data with more flexibility.

Statisticians have a wider spectrum of specific needs for theoretical foundation and specific domains. For example, i am a Biostatistician and i need to know Biology and Statistics and how to design, interpret and help Biologists and Clinicians in effective communication with them with decision making in addition to performing the data analyses. So Statisticians are Data analysts partially too, but they also have other specific skills and educational backgrounds specific for both theoretical, mathematical and domain based knowledge.

Good question indeed ! What is a statistician ? This question gets more and more interesting with possible nuanced answers, with ever blurring borders of statistics, data science, machine learning and artificial intelligence. Though I would say impossible to come up with an accurate answer, I would say "any expert understands and applies concepts of stochastic theory"

I think people need to understand that statisticians don't just do the mathematical side of the analysis but they are subject matter experts, epistemologists, and experts in experimental design.

It is not to provide data for other people. In my opinion, an analyst's role is to provide insight from data. These insights should be there to help stakeholders make clearer decisions. If you are only making dashboards, gathering data for other people, and not providing insights, you are not analyzing the data. An analyst analyzes.

The statisican can be a data analyst but the opposite is not true. A data analyst may be anyone who has a background in statistics, economics, business , who can use data to support business operations, marketing strategies and decision making processes.

They have a lot in common but one differentiating aspect is that statisticians usually focus on how much bias and variance there can be in a model that describes the data, whereas data analysts usually are responsible to measure what there already is in the data and describe it as faithfully as possible. Depending on the size of the problem, it requires a totally different skill set.

An analyst will help you fool yourself with data. A statistician will try to stop you. The problem statisticians face is that fooling yourself with data is easier and more fun so no one likes having them around.

Short answer and highly personal one : a data analyst should be a good statistician at first. So, without internalizing the fundamental concepts of statistics and stochastic processes, it is practically impossible to be a good data analyst.

At least in in the pharma world, the distinction is generally as follows: Statisticians are technical experts that focus on the design, collection, statistical methodology, and interpretations/conclusions that can be drawn from data. Analysts are typically more focused on hands on execution of the relevant analyses and database structures (ensuring regulatory compliance), in conjunction with the statistician to ensure correct methodology.

Does anyone sit down and ask themselves "Hm, should I become a statistician, or should I be a data analyst?" I'm skeptical. Typically, statisticians choose to be statisticians before or during their academic training. Either they know beforehand that this is what they want to do, or they have a substantive interest in a particular area -- like education or health -- and pursing that interest takes them into statistics. In my experience, data analysts choose a career as an analyst when they first hit the job market, or when they are unhappy with their previous path. With my present employer, the data analysts I've worked with have included an engineer, a roboticist, a criminologist, an epidemiologist, a medical doctor, and a dentist!

One could say that statisticians are usually holding a degree that specializes them in a field near to statistics, such as mathematics or physics. It might also be worth noting that the role of a data analyst can be interpreted very broadly: While some data analysts might require an in-depth knowledge of statistics, many focus on rather simple descriptive statistics and visualizations with a business case in mind. Because the role of a data analyst is interpreted in a broader sense, it often serves as an entry role to those who want to work with data, aspiring to be analysts, data scientists or machine learning engineers.

In my opinion, Statisticians are built within the wall of academic institutions while data analysts are built in the market place. Statisticians are love to work with figures and make inferences with them but Data Analyst are driven by their passion to solve problems by studying trends and patterns.

Very important. While there are differences between Statisticians and Data analysts, their performance regardless of education levels, depends on their implementation and customization skills. For this programming languages such as R, Python or SAS languages are essential. Even if you are a CTO or a Statistics team lead, you still need to understand these to be able to lead teams effectively.