Data Analyst vs Data Scientist Explained

Data analysis and data science are increasingly popular career choices in the field of big data, and for good reason. 

But what are the key differences between the two career paths, and how can you become qualified as a data analyst or data scientist?

A career in data science would particularly suit you if you enjoy working with numbers and statistics, are good at finding patterns and trends, are a logical problem-solver, and have a good knowledge of computer systems.

For the right person, a data career can be a dream come true, as it allows you to use your analytical skills to help businesses thrive, advance your career and be at the forefront of the latest developments in the thriving industry of big data.  

The world of big data is growing quickly, as businesses all over the world are acknowledging just how important data is, meaning that the data analytics market itself is predicted to create a huge 11.5 million jobs by 2026.

A data analyst collects data and uses their skills alongside data analytics tools to discover trends, organise the data and present their findings to businesses. Data analysts, therefore, need to be able to work with data, use data analytics tools and use data visualisation tools in order to not only work with data themselves but also present it to others in a succinct, understandable manner. 

The main duties of a data analyst are:

Data scientists also work with data on a day-to-day basis, however with a different focus than data analysts. Data scientists explore different ways of working with data, capturing data, and managing data rather than working with pre-structured data to provide business insights. 

Data scientists frequently use artificial intelligence, automation, or machine learning to find new ways of working with data, are experts in statistics and statistical analysis, and use their analytical IT skills to find the best methods of structuring raw data.

The main daily duties of a data scientist include the following:

Data scientists and data analysts both work in the growing area of data, using their skills to help clients gain insight into their business and help them make key business decisions using the analysis of data and data trends. 

The duties of data scientists and data analysts also cross over to a degree, as both do similar aspects of data management, such as collecting, merging, and analysing data to find trends and presenting their findings. 

However, the focus of data scientists is to find new ways of working with raw data, whereas data analysts often work with structured data and use pre-existing tools to work out trends and provide business insight. 

Data scientists can therefore create and enhance many of the tools used by data analysts in order to capture and analyse data in the most efficient way. Data scientists and data analysts are highly skilled professionals with a diverse skill set. Many of the skills required cross-over, for example, technical skills such as business analytics and data mining and soft skills such as decision-making and communication skills.

However, data scientist roles also require them to have expert knowledge of statistical models, machine learning models, and predictive models, in-depth knowledge of working with unstructured data, and machine learning algorithms.

If you have experience as a data scientist or data analyst and are keen to expand your career options, then data is a highly flexible area to do this. As data science requires more in-depth knowledge of raw data tools and programming skills, people usually begin with a career in data analysis before gaining more expertise and moving into data science.

Any background in data can help you branch into new career roles in data, and finding online guides that explain how you can transition from a data analyst to a data scientist is a great idea. There are also dedicated courses to help you make the shift from Data Analyst to Data Scientist. 

Whether you have had experience working with data or are just at the beginning of your data career path, gaining as much knowledge as you can about data and computer science is essential before embarking on your new career. 

Those with a bachelor’s degree, master’s degree, or advanced degree in a relevant subject such as computer science or statistics will find there are a variety of online courses which provide introductions to data. You can also discover how to use programming languages, the role of Big Data, Digital Skills, and much more to provide an excellent foundation for your career. 

Careers in data are becoming more in demand for businesses around the globe, from those in finance to those in healthcare, as being aware of current data trends and being able to manage data effectively can be key to the success of a business. 

As data experts, both data analysts and data scientists are in high demand, and these two roles often cross over in terms of duties and skills. Although these careers use data in vastly different ways, both provide vital, actionable insights for clients relying on the interpretation of data.

For those with skills in data analysis, exploring data science courses can be the best way to bridge the gap and move into a career within data science. Meanwhile, Data Scientists can explore data analysis courses to fill in any gaps in knowledge they may have.