What are the differences between data analysts and data scientists?

The terminology used around big data can be confusing for people new or unfamiliar to the field – particularly when it comes to the roles available for professionals with data-driven skills.

However, if you’re considering a career in data, it’s important to have a clear understanding of the positions and opportunities that are applicable to you.

Two in-demand hires for organisations are data analysts and data scientists, and it’s not uncommon to see their responsibilities get confused. However, despite certain similarities, there are key differences that set both apart.

Read on to learn more about each role and how to determine which position is the best fit for you.

What is a data analyst? What is a data scientist?

A data analyst routinely examines large data sets to identify and evaluate trends within their organisation. They present their findings via straightforward visualisations to help business stakeholders make strategic, data-driven decisions.

A data scientist will often be required to perform the same tasks as a data analyst. However, they will also be responsible for designing the way data is collected, stored, and analysed. They may accomplish this by creating algorithms, predictive models, and data modelling processes.

Essentially, a data analyst works on interpreting existing data, whereas a data scientist will also look to find new ways of gathering and examining data for their organisation.

How do their skills differ?

As you may have guessed, the overlap in responsibilities between these two roles also means that there’s an overlap in their skill sets.

When applying for a role in big data, it’s always helpful to go over each job description carefully, as different companies will have their own expectations for each position, which contributes to the amount of crossover seen between the two.

As a general guideline, the skills requested from a data analyst could include statistical analysis, database management, and data querying. Typically, a data scientist will be expected to have more experience with machine learning, data mining, data cleaning, programming languages (such as Python or R), and developing big data architecture.

Which role is for you?

Data analysts are typically entry-level roles, but you can progress into higher positions if you wish – whether that’s a senior data analyst, an analytics manager, or even a data scientist. You can also choose to advance into a more specialist career path, such as a financial or digital marketing analyst.

Since being a data scientist requires more expertise, they are typically higher-level positions. Similar to being a data analyst, you have the scope to specialise in an area of interest and choose from a number of different industries. Alternatively, you can explore moving into data architect or data engineer positions as well.

Depending on the experience you already hold, you should be able to gauge which role is the most feasible starting point for you. Remember though, as both roles have elements in common, there’s no stopping you from pursuing either, even via unconventional routes. Spend some time considering where you’d like to establish your career, and then start working towards that future.

If you’re ready to take the next step towards a career in big data, our online, part-time courses can help you develop the specialist knowledge you need. We offer CPD-accredited data science courses for professionals with programming knowledge as well as for those without – allowing you to choose your own progression path and earn a valuable new qualification in just six weeks:

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Topics: become a data scientist

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