Data Analysis vs Data Science – What is the difference?

Demand & Salaries 

It’s worth mentioning at this point that there is a fair amount of overlap between these two roles. There is, however, more of a stark difference in the actual work done than maybe the descriptions would lead you to believe. 

A Data Analyst is one of the fastest growing roles globally. Jobs in this area are expected to grow by 300% over the next 5 years, according to CompTIA. This is due to the increasingly obvious benefits that data has brought to businesses over the last ten plus years.  There are no distinctions, now, between a company's decision-making process and the story a team of data analysts has to tell. 

Data Science is also growing, but for a different reason. With the advent of AIs most recent incarnation, the demand for Data Scientists has grown exponentially as they often have the pre-requisite knowledge and skills to progress naturally into the Artificial Intelligence sphere. It is, in fact, quite difficult to get a job in AI without some experience of working in data to some capacity. 

The salaries are good, too. If you’re starting out as a Data Analyst then the median salary is almost £40,000 a year. If you’re more experienced in the industry and moving into Data Science, then the median salary is over £65,000! 

Data Collection 

Both a Data Analyst and a Data Scientist collect and process data.  

The datasets a Data Analyst uses will often have been collected by a company from its users, such as a bank’s customers or subscribers to a newspaper. This is data that can be easily tabulated and charted within software such as Excel or PowerBI to analyse. 

Data Science is a little more complicated; the datasets are often much larger and far more complex. If Data Analysts are using relatively simple tables, then Data Scientists are comparatively collecting information on the beginnings of the universe from the James Webb Telescope or the Large Hadron Collider. These are huge amounts of data that it often takes months or years to properly collect and process.  

Data Analysis & Modelling 

A Data Analyst, not so surprisingly, analyses data. So does a Data Scientist, though, so where’s the disparity? This is primarily down to the level and type of analysis completed.  

Leading on from the collection, Data Analysts have a simpler, if not easier process. Once you know what you’re looking for, it’s a case of syphoning through the data to find what you want to present. This isn’t to take away from the role of a Data Analyst! It still takes great skill and understanding of a business to provide a compelling, storytelling presentation to stakeholders using comprehensive graphs and charts. 

On the other hand, Data Science might not always know what its outcomes are. There is theory or hypothesis, and it’s the role of a Data Scientist to prove or disprove it using the information available. Due to the complexity and size of the datasets, too, far more advanced statistical and machine learning methods are needed. Think of all the different recommendations you get across your streaming services that are provided by machine learning algorithms and then apply that to a learning system like ChatGPT. The opportunities are endless, but the challenges are huge!  

Collaboration 

The ability to communicate is key, regardless of whether you’re a Data Analyst or a Data Scientist. If you’re unable to relay your findings and analysis concisely, then you’re not going to impart the importance of what the data is showing you. Concurrently, there’s far too much information out there for one person to do it all themselves. Whether you’re working for a finance company or a space agency, you’ll need to work as part of a team to bring your project together properly. 

Where these two careers differ is the time and the manner of collaboration. Whilst a Data Analyst will likely have regular meetings with their colleagues on a month-long project, a Data Scientist could be liaising with professionals from various disciplines around the globe, on a project that could last for years. 

The right tools for the job 

There are innumerable databases and software that a Data Analyst can use, varying on the sector they’re in and the company they work for. They could use any one of hundreds of data storage entities, not least Google Cloud, Azure, Oracle, or AWS. Then there are the programmes needed to analyse the data, from Excel to PowerBI and Tableau. These all require training and experience to be able to use them properly and deliver the results needed in a timely manner for a business to operate from. 

A Data Scientist can use databases that have alternative structures to the norm, and will typically use more advanced, bespoke data modelling software to visualize their results. Even Excel, as it’s grown over the past 20 years, will struggle to create predictive models for the weather patterns and ocean currents you see on your weather app! This is where machine learning and AI, in one of their many forms, really comes into the Data Scientists' remit. 

The world of data has grown and changed dramatically over the last ten years and, as it continues to evolve to meet the demands of the modern world, the importance of Data Analysts and Data Scientists will adapt with it.  

So, whether you’re just starting out as a Data Analyst and discovering the complexities of the finance sector, or if you’re upskilling to a Data Science role to probe the mysteries of the universe, there are a vast number of opportunities for you.  

If you think a role in data is for you, register your interest and one of our consultants will discuss your different options with you and the benefits of a career with a CompTIA Data+ certification.