How to Transition From a Data Analyst to a Data Scientist

Data analysis and data science are increasingly popular career choices in the field of 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 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:

If you want to know how to become a data analyst, you can explore Data Analyst courses that can help you build on skills you already have or help you learn everything you need to know in a bootcamp format. 

The answer ‘What does a data scientist do?’ is quite a broad one. Data scientists also work with data on a day-to-day basis. However, data scientists work with data 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, there is a key difference between a data analyst and a data scientist. 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, as well as in-depth knowledge of working with unstructured data and machine learning algorithms.

If you want to know how to become a data scientist using the skills you already have, there are steps you can take to move into this career field. 

The first thing to do is to look at the skills you currently have and examine whether you feel you’re ready to make this challenging transition. Although data analysis and data science are related, the level of expertise needed to succeed in a data science role is high. 

Think about large projects you’ve worked on before and assess the skill set you used to deliver those projects. If you feel that you could’ve gone further or offered even more insights but were unsure of how to make it happen, this could be a sign that you’re ready to do additional training to become a data scientist. 

If you feel that you’re one step ahead of your current role or feel that you’ve reached your limit of data analysis and data insights, it might be time to upskill in the field of data science.

If you’re thinking of becoming a data scientist, taking online courses in Data Science can be a good idea. Taking courses in IT is also a good way to cement your knowledge in data analysis before moving toward data science. 

If you’re keen to make the transition straight away, taking a Data Analyst to Data Scientist Pathway online course could be the ideal choice. This data science course will give you a thorough understanding of data architecture and statistics, as well as a deep understanding of analysis programming skills. You’ll get to grips with how to use programming languages such as Python and R. Using that knowledge. The course will also help you to process that data through data analytics software like Spark and Hadoop.

You’ll also learn how to operationalise large-scale datasets, all whilst keeping regulations and compliance in mind. Lastly, the course will teach you how to use data visualisation in a way that makes sense to others in your company or business whilst in your data science job. Overall, you’ll have a solid understanding of the fundamentals and soft skills, as well as technical skills, analytics skills, and data science skills needed to succeed within a data scientist role.

After you’ve gained your data science qualifications and skills, you’ll have a wide range of opportunities open to you as you begin your career transition. Data scientist prospects from a career perspective can be vast and lucrative. Data scientist salaries are around £70,000 a year in the UK. This can rise to even higher amounts if you choose to work abroad or on a freelance basis. 

If you’ve already got an established career as a data analyst and have chosen to study alongside your current job, becoming qualified in this area could open up new opportunities within your company and could even allow you to manage entire data management teams to deliver outstanding insights.

Getting your first data scientist job can be an exciting step in your computer science or analytics career. As data becomes an increasingly vital part of businesses worldwide, the job market for those qualified in this area is growing, and those who want to establish a successful data science career can get there through courses and experience.