Data Science and statistics

Find out about careers in statistics, data analysis and data science.


Data is everywhere. Cookies are monitoring our browsing history and advertisers are using them to suggest products to us. Streaming services such as iPlayer and Spotify record our viewing and listening habits, offering us recommendations of things we might also like.  Professional sports players wear technology that monitors their location, running and physiology, sending back data to coaches to help assess their performance. Rolls-Royce constantly monitor their jet engines on passenger aircraft, aiming to pre-empt any mechanical issues and rectify them with a quick repair before they turn into more expensive, larger repairs.  Google uses location and speed data to understand road traffic conditions and hold-ups, providing live updates on Google Maps.

Data isn’t new, but technology (notably the internet, mobile phones and social media) has allowed it to explode in scale and use.  Data from these multiple sources may be collected and connected to yield incredible insight in what is known as ‘big data’. As data of all types is collected, so people are needed to analyse it.  In growing numbers.  

What is Big Data


The data landscape

Looking at vacancies on MyCareerHub revealed that, in September 2021, 37% of graduate vacancies and 29% of internships included a mention of the word “data” somewhere in the job advert. While this does not mean they are all ‘data jobs’ per se, it highlights the significance of data. And of course, many are jobs in data; so opportunities abound!

Keep in mind that data isn’t only a commercial tool; it has significant environmental and humanitarian uses too and is crucial to improving health and for the government to use in informing policy. Find out more about potential uses for data on the following websites.

Data use for the environment

Data for children collaborative

Data for health

Data for policy


There has never been a better time to think about a career with data, but with so many opportunities on offer, it is sensible to give some thought to the following questions to help narrow your focus a little:

  • Do you want data to be a part of your job, or all of it?
  • What kind of data are you interested in analysing?  (Sometimes it’s the topic of the data you are analysing that creates the interest, rather than the process of analysis itself.)

You should then ask yourself:

  • Do I prefer to find and extract the data, to analyse it, or to interpret and present it?
  • Which sector would I like to work in? Opportunities are numerous in the private, public and third sectors.
  • How would you prefer to work? In-house roles where you use data within an organisation, or consultancy roles offering where you offer specialist advice or expertise with data for clients.


Data is significant for Edinburgh and surrounding areas. Its position as a seat of government, centre of research (four universities) and major centre for financial and business services has long created many job opportunities in data. More recently, The Edinburgh and South East Scotland City Region Deal (an investment of £1.3bn over 15 years by the UK and Scottish governments, designed to accelerate productivity and inclusive growth) has data-driven innovation (DDI) as a key aspect.  This has meant a huge investment in data in the city region, including the setting up of data hubs.

City Region Deal

Data Driven Innovation Programme

The Data Lab have a free community anyone can join. You can access events, network with peers and discover over 1000 job opportunities.

The Data Lab


Data Roles: Statistics Vs Data Analysis vs Data Science

As might be appreciated, there is no one ‘set’ job role working in data. Instead, roles sit on a spectrum from those including a little data work to those solely concerned with data work.  Some are more IT-based and require knowledge of programming and algorithms; others more analytical or around presenting data, needing skills in visualisation and/or presenting.

For this reason, alongside the rapid pace of development of data, there is much debate among professionals and scholars about what data science is – and what it isn’t. Does it deal only with big data?  What constitutes big data?  How is it different from statistics and analytics?

Statistician roles involve turning business/world problems into data problems and using the data to try to find solutions.  Typically statisticians look at sample populations and use them to try to gain an understanding of larger populations – hence roles are often based in government, public health and the pharmaceutical sector where they are trying to understand impacts on the whole population. Quantitative degrees may be preferred or required.

Statistician job profile

While similar, data analysis is more concerned with inspecting, transforming and modelling available data into useful information that can be understood by non-technical people. Data analysis can be used to provide data (including from multiple sources) for statistical analysis. Some roles also require a technical, numerate background but many don’t – especially if interpretation and presenting are key aspects.

Data analyst job profile

Data science is also related to data analytics, but is focussed on big data and so concerns using automated methods to analyse massive amounts of data (not samples) and to extract knowledge from them. To help them do this, data scientists design and implement mathematical algorithms, using statistical techniques, data mining, artificial intelligence and machine learning. Such roles often require knowledge of programming.

Data scientist job profile

With such automated methods turning up everywhere from marketing to finance, social media to scientific research, data science is helping to create new branches of science, and influencing areas of social science and the humanities. The trend is expected to accelerate in the coming years as data from mobile sensors, shop loyalty cards, behaviour on the internet and many more places, grows.

There is a useful diagram and blog post on the Ironhack website which seeks to explain the differences between data analytics and data science and the overlaps with maths, computer science.



How can I get work experience?

Work experience will help develop your skillset and commercial awareness as well as building a network of colleagues and contacts. As the skills required for a jobs in data are varied, internships and work experience in a variety of organisations will be applicable. Typical roles and industries could include:

  • Data science
  • Software engineering
  • Data analysis
  • Marketing
  • Business analyst
  • Consultancy
  • Finance

Internships are advertised on MyCareeerHub and many other careers sites. Learn more here:

Find jobs for while you are student

Work shadowing a professional gives you chance to observe their day-to-day activities and find out first-hand what the job involves.

Speculative applications and networking are key approaches to finding work experience:

Create your own opportunity


How do I develop my skills in data?

Data scientists are often required to have good computer programming skills so developing these will be very important. There are many free online courses and the following website has compiled 20 of the most relevant ones for computer scientists:

Computer Science education via MOOCS

Many online courses come in the form of MOOCs (Massive Open Online Courses) often provided by prestigious universities free of charge (although if you wish to sit an exam, they will charge a fee). You can search for MOOCs at:

Additional courses are available through LinkedIn Learning, to which the university provides free access for its students. Log into the MyEd portal and type “LinkedIn Learning” in the search box at the top of the page.  You can find resources covering data visualization, Power BI dashboards, Excel, data modelling, data science, data analysis, Python, Java and many other software tools.


Regardless of the role you’re applying for, employers are looking for applicants who can demonstrate commercial awareness and are receptive to new ideas. Check out our advice on how to develop commercial awareness and other aspects of becoming professional.

Become professional

Extracurricular activities and employment can be used to showcase problem-solving and communication skills e.g. resolving a customer complaint; overcoming unforeseen difficulties whilst planning an event; presenting to an audience. Learn more on our build experience pages.

Build experience

Taking part in sport and university societies provides opportunities to develop team work and communication skills.  Find out more about how to build experience and develop your skills.

Take part in activities    

Visit the Events section of MyCareerHub for details of relevant events. Find out more about how to meet and hear from employers on our website.

Meet and hear from employers

Get your organisational and time management skills recognised by taking part in the Edinburgh Award.

Edinburgh Award


Is postgraduate study necessary?

A postgraduate qualification is not always required but it may help you develop the necessary knowledge for this specific field.

If you do decide to pursue further study, there are lots of courses to choose from. Use the postgraduate study course search option on Prospects Web, Target Courses or FindAMasters:

The Data Lab sponsors data-related MSc courses at 12 Scottish universities, which also includes a paid 3-month industrial placement. The courses offered via the scheme reflect the breadth of data opportunities, ranging from GIS to data engineering; from AI to health data science. Find out more on the Data Lab website:

The Data Lab


Finding a job

Check MyCareerHub for vacancies. It’s worth looking at the Expired opportunities function to identify companies who have advertised vacancies in the past, then check their websites in case they have vacancies they have forgotten to tell us about – or apply speculatively.


You will also find data science jobs advertised on Data Elixir, DataScientistJobs, Statistics Jobs, Technojobs:

LinkedIn is increasingly important as a source of internships and jobs, but often overlooked is the opportunity it presents to access ‘the hidden jobs market’ – the vast number of job openings that are not formally advertised. Use LinkedIn to network and explore the career paths of people doing the type of job you want to do.

Using social media to find out and stand out