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More about Data Science training

What is data science?

Data science is the process of using data insights to inform and make better decisions. Data science combines a range of different specialisms and skillsets, including AI, machine learning, computer science, statistics, and coding.

Data science plays an important role in the overall data pipeline, and is therefore strongly connected to data analytics and data engineering.

What does a data scientist do?

Data scientist is the primary role for those who specialise in data science and is one that continues to be heavily in-demand across various sectors and organisations.

The role of data scientist is to make key business data easily accessible for other areas of the business, understanding the ways to make this process as simple as possible.

Data scientists need a solid understanding of programming languages, such as Python and R, as well as the architecture and building of data bases, as well as an analytical and business minded approach.

How to become a data scientist

Data scientists often have a background in maths or computer science, from which there are various routes to a career in data science.

One route is through an apprenticeships in data or analytics, and another by progressing up from an entry-level role in data analytics.

Those wishing to becoming a data scientist can look to acquire a range of different skills, including programming qualifications, communication skills and project management, which can all help someone develop into a well-rounded data scientist.

As data scientists are in high-demand, data science may be a sound choice for individuals looking for a future-proofed role in the field of data, and one which provides the opportunity to develop an understanding of AI, data engineering and machine learning.

Is data science and data analytics the same?

Although there are similarities and crossover between data analysts and data scientists, there are also some key differences. Data scientists often need to work with large data sets, building algorithms and programs that make this data easy to manage and analyse.

This may require a deeper knowledge of programming and machine learning, with a focus on predicting future outcomes, rather than looking back on historical data. This requirement to create predictions and forecasts often requires more critical thinking and more complex solutions, and ultimately helps organisations to plan for the future.