In today’s business climate, data is the one thing everyone is looking to as a means to compete and drive better decision making across business units. But who in your organization is actually working with data and putting it to work? You’ve likely seen an abundance of job listings for data analysts and data scientists alike or may even currently be in one of these roles yourself. These positions are becoming increasingly essential across industries, the Harvard Business Review even deemed data scientist as the “sexiest job” of the 21st century.
However, the lines can often be blurred between the roles of data scientists and data analysts. So now that we’ve established the rising demand and importance of these common positions, let’s take a closer look at understanding each. Let’s explore what it means to be a data scientist or an analyst as well as some key distinctions to keep in mind.
What do Data Analysts do?
Data analysts are versatile in their role and are predominantly focused on driving business decisions. It’s common for data analysts to start with specific business questions that need to be answered.
Some common job functions for data analysts include:
- Identify trends and analyze data to develop insights
- Design business intelligence dashboards
- Create reports based on their findings
- Communicate insights to business stakeholders
What do Data Scientists do?
While a data scientist also works with data thoroughly to develop insights and communicate them to stakeholders, they commonly apply more technical aspects to their work. This includes things such as coding, building algorithms, and developing experiments. Data scientists must know how to collect, clean, and handle data throughout the pipeline.
Data scientists typically require more experience and advanced qualifications, specifically when it comes to their knowledge of statistical computer languages such as Python and SQL. However, there is far more to a data scientist’s role than merely their technical expertise. They have to be able to ask the right questions and streamline all aspects of the data pipeline.
Some common tasks and responsibilities for data scientists include:
- Build and manipulate data models
- Optimize data collection from disparate data sources
- Clean data and create processes to maintain data quality
- Develop algorithms and predictive models
What’s the Difference?
While both roles have data in common, the primary difference between the two is how they work with data. Data scientists focus on the entire data lifecycle, from collection and cleaning to final insights and interpretation. A data analysts’ role is weighted at the end of the pipeline, this being the interpretation of data and communicating findings to business units.
It’s not uncommon for data analysts to transition into the role of data scientist later on in their careers. Many view data analysts as a stepping stone, where analysts are able to practice the foundational tools of working with data.
How Much do Data Scientists and Data Analysts Make?
Your second thought after “show me the data” is probably “show me the money!” Now that we’ve reviewed the similarities and differences in each role’s responsibilities, let’s get down to the numbers. According to Glassdoor, you can expect to earn an average base pay of around $113,309 as a Data Scientist. This is nearly double the average base pay for a Data Analyst which comes in at around $62,453 per year. The seemingly drastic pay difference primarily reflects the variation in technical expertise and years of experience needed.
Overall, there is no predetermined definition of what it means to be a data analyst or a data scientist. Each role is unique and will vary depending on the industry, there are also a number of other factors specific to each organization. Though, it’s important to remember that there is room to make the position your own and define either job title for yourself.