Data is among the most valuable assets many organisations have, and if you have the right training there's a world of high paying, challenging work waiting for you – two of the most prominent are data scientist and data analyst.
And while the two have some similarities, the common pathways into careers in each area can be quite different, and candidates have a wealth of choices around types of study. The responsibilities and workloads of each are different, as are the further career prospects.
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 career prospects.
What does a data scientist do?
Online information systems are coming into being and growing so fast, the need to analyse data is exploding. In 2023, we're set to produce 120 zettabytes of data, up from 97 zettabytes last year and set to be outstripped next year with 147 zettabytes, according to Statista.
But companies and organisations that collect that data have to store and action it in particular ways for it to have any value. Enter the data scientist, who sets up frameworks for others to synthesise and extract insight from the data that's been gathered.
A data scientist combines computer science and mathematics to do this, with a particular focus on statistical tests and distributions. The companies that collect data need to ask it questions, and the data scientist figures out how best to frame the question and translate the answer. Like any business asset, data can be used to provide a financial or social advantage, and the data scientist helps to reach that goal.
Data science is the art of collecting, collating, processing, analysing and interpreting data in both structured and unstructured environments, creating frameworks that standardise it for further interrogation. Their arsenal includes machine learning or AI, data mining, statistical algorithms and more to 'smooth' data into a comprehensible form.
What does a data analyst do?
A data analyst takes a dataset from multiple sources, cleans it, and reorganises it for analysis. The tools of the trade include automated systems for unearthing unstructured data as well as stripping out or fixing corrupted or garbled files.
Whether it's designing and building an analytics system or delivering results from the analysis performed, a data analyst can enter the process anywhere along the workflow (or perform it entirely), including training other staff in conducting analyses that relate to their own business units.
A data analyst also needs to be a presenter, putting reports and summaries of results and interpretations into formats executives or other stakeholders can digest.
What skills do data scientists and data analysts need?
Data scientist skills
As with other scientific fields, a qualified data scientist is usually at least a holder of a Master's degree or PhD, and typically an expert in both technical and non technical principles.
The non-technical skills include critical thinking – clients and employers will have specific questions the data needs to answer, and the data scientist must structure the workflow so the available information can do so in direct relation to the industry or company concerned.
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Data scientists also need to be competent communicators, able to put arcane concepts into relatable language for other business units, and the ability to think on your feet to solve problems as they come up is critical.
When it comes to the more technical aspects of the job description, the data scientist needs to know how to prepare datasets for the best analysis tools and methods. They need to know how to write effective code to do so, applying maths, statistics, and machine learning on a daily basis.
It also calls for a thorough understanding of database programming languages like Python, R Programming, SQL, Scala, MongoDB, MySQL, and more.
Data analyst skills
The responsibilities of a data analyst have some overlap with those of a data scientist. The data analyst similarly needs to be proficient in maths, which is applied more to the modelling and analysing of data once it has been prepared for treatment (possibly by a data scientist).
Every data analyst needs to have a deep knowledge of the various techniques needed to clean data, so analysis produces the best possible results, calling for high attention to detail.
When a project is complete, they then need similar interpersonal skills as a data scientist in order to communicate and present work to colleagues, particularly the ability to create visual or graphical representations of findings.
Formats and frameworks data analysts need to know include statistical programming languages like R or Python, data management, machine learning/AI, and systems like SQL.
Data science vs data analysis: Training pathways and career prospects
It's easy for outsiders to confuse the two professions because both are concerned with properly preparing and interrogating data for organisational insight. But there are quite stark differences.
Firstly, the data scientist is a more senior position, with more generalised responsibility in applying programming knowledge to create data models used for multiple purposes. A data analyst, by contrast, designs examinations of the data according to the established aims of other business units.
A career in data science
Becoming a data scientist will usually start with a degree in a computing, statistical, or scientific field, which will prepare you for the generalised knowledge of data science.
Depending on your area of focus, industry or the employer(s) you want to target, data analysis certifications like the CAP or vendor-specific study (Cloudera Data Platform Generalist Certification, Microsoft Azure Data Scientist Associate, SAS Certified Big Data Professional, etc) can be a good next step to specialise further.
According to Payscale.com, the salary range of a data analyst in the UK in 2023 is around £22K-£44k, with an average of £29,699. For data scientists, the range is £28k-£62k, with an average salary of £41,483, although seniority or further specialisation can put you over the £100k mark.
A career in data analysis
To many, the best way into data analysis is to get an entry-level job in the field, giving you invaluable exposure to datasets and the tools you'll apply to them from real-world business processes.
You don't strictly need qualifications in maths or statistics to find work as a data analyst, but it certainly won't hurt to have them. Once you have a foot in the door, further training to boost your knowledge is common, and that can only expand your career advancement possibilities.
That might mean anything from the entry-level Associate Certified Analytics Professional (ACAP) to the Certified Analytics Professional (CAP), a vendor and technology-agnostic qualification that's more specialised and suits data analysts further into their careers.
