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Data science vs data analytics: Which field is right for you?

These two very similar career paths can be highly fulfilling, but there are subtle differences to take into account

Data has been the 'new oil' for roughly a decade, but we still haven't tapped into its full potential. In fact, the industry as a whole has barely scratched the surface for what properly harnessing data can help businesses achieve. This is where professionals who work with data, day-in and day-out, come in. 

Data, big data and data analytics, are considered the lifeblood of many companies, and roles working with data are plentiful if you have the right skills. Two of the most popular career paths in data are those of the data scientist and data analyst. While similar in nature, on the face of it, they have varying routes of entry – including data science courses, data science bootcamps, as well as university degrees. They also have differences in terms of roles and responsibilities, as well as varying prospects. 

What does a data scientist do?

With terabytes of data being produced on an hourly basis, there is a need for organisations collecting this data to do something with it. Data scientists lay the foundation for others to dissect and analyse the information gathered. The field of data science combines computer science, statistics, and mathematics, with individuals pursuing this career boasting a good grip of statistics, including statistical tests and distributions. 

It’s the data scientists who  figure out what questions that organisation should be asking of the data, and how to answer these questions. That’s why they work with businesses and other stakeholders to understand their objectives and define how data can be used to realise those objectives.

Data scientists obtain, process, analyse and interpret large quantities of structured and unstructured data from a variety of sources, making sense of these often-messy foundations. They then use machine learning, artificial intelligence (AI), data mining, algorithmic and statistical tools, to make this data usable by the rest of the business.

What does a data analyst do?

A data analyst’s job is to collect, clean, and interpret datasets to help in answering questions or solve problems for an organisation.

They use automated tools to unearth data from primary and secondary sources. They also eradicate corrupted data and fix coding errors and other problems. They can be responsible for developing data systems, reorganising data into a readable format.

Data analysts can be engaged anywhere in the data analysis process from developing an analytics system to delivering insights based on the data gathered. Analysts may even train people from other parts of the organisation in using a data analysis system.

Data analysts will also summarise and present data and conclusions to others in the business in a format those people can understand.

What skills do data scientists and data analysts need?

Data scientists are usually highly educated with may having at least a master’s degree or even a PhD. There are also technical and non-technical skills required to be a data scientist. 

In terms of non-technical skills, a data scientist needs to have the ability to think critically. This is important because, as well as discovering business insights, a data scientist should be able to structure questions and comprehend how those results relate to the organisation. They must also be able to communicate effectively and be proactive when it comes to solving problems.

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As far as technical skills are concerned, a data scientist should be able to prepare data for effective analysis, write efficient and maintainable code, apply maths and statistics appropriately, and use machine learning and AI. There are also several programming languages and databases that data scientists should be able to use, such as Python, R Programming, SQL, Scala, MongoDB, and MySQL, to name a few.

Data analysts need a high level of mathematical ability as well as the ability to analyse, model and interpret data. They should also be able to be accurate and have attention to detail. Soft skills such as interpersonal, teamworking, and written and verbal communications are essential to the role.

Among the more technical skills necessary for a data analyst are the ability to visualise data (i.e. presenting data findings via graphics), and data cleaning. This can be a major part of the job as uncleaned data can lead to confusing patterns.

Data analysts should also have skills in SQL, statistical programming languages, such as R or Python, machine learning, statistics, and data management.

What are the differences and similarities between data scientists and data analysts?

The work of data analysts and data scientists can seem interchangeable, as both tend to look for patterns in data and devise means for businesses to make more informed decisions about their processes. However, data scientists have more responsibility and are looked on as being more senior than data analysts.

Data scientists created their own questions about data, while analysts support others in the business that already have set aims in mind. Data scientists also spend more time creating data models, using deep learning, or programming in ways to discover and analyse data.

How do training and learning pathways, as well as career prospects differ?

When it comes to starting a job in data analytics, it's usually best to get into an entry-level data analyst role to help you get accustomed to using real-world business data to develop insights.

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A data analyst does not need to have a degree in mathematics or statistics, but it can be helpful. People already in the field can gain several qualifications to bolster their career opportunities too. Such certifications include the Associate Certified Analytics Professional (ACAP), which is an entry-level certification. More experienced analysts can train for the Certified Analytics Professional (CAP) qualification which is technology and vendor neutral. 

Data scientists will typically need a degree in a computer science, mathematical or science-based subject to work in this area. Data scientists can also obtain the CAP certification as well as taking more vendor focused certifications such as the Cloudera Data Platform Generalist Certification, Microsoft Azure Data Scientist Associate, and SAS Certified Big Data Professional.

According to Prospects, the entry-level salaries of data analysts range between £23,000 and £25,000, rising to between £30,000 and £35,000 after a few years’ experience. Top-level analysts can command £60,000 or more.

Junior data scientists salaries start at around £25,000 to £30,000, rising to £40,000 depending on experience, according to Prospects. With a few years’ experience under the belt, salaries can rise to between £40,000 and £60,000 and top out at more than £100,000 for lead and chief data scientists.

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