You’ve got your degree, and you might have studied Machine Learning or Artificial Intelligence on Coursera. Perhaps you even have a PhD in a field such as astrophysics. You want to nab one of these coveted data science jobs, which you’ve heard are the ‘sexiest’ jobs around at the moment but you’re not sure where to start.

Tech companies naturally have a thirst for Data Scientists to make sense of their data, help them improve their workflow and generate more revenue. They’ll reward you handsomely for it.

What this article covers…

Why Work in Data Science?
How to become a Data Scientist
What coding skills do I need to become a Data Scientist?
Other useful technical skills
What Experience and Educational Requirements are Needed to be a Data Scientist?
Commercial Experience and Soft Skills
Data science bootcamps in the UK
Other cheap and easy ways to learn about data science online
Maths and statistics
Building data-centric side projects
Networking at data science meetups
Finding a data science internship
Data Science Graduate Schemes
What is the expected salary of a Data Scientist?
Data Science Jobs – London
Final Remarks

Why Work in Data Science?

First, understand why you want to work as a Data Scientist. If it’s just to earn money, you may be better off picking a different field – not least because of the competition you’ll face.

Currently, data science is a really popular area to work in. Searches for the term “Data Scientist” have increased by 6 times in 5 years, suggesting that many people want to work in this field. At the moment data science is ranked as the sixth best job on Glassdoor, namely because of its high rate of pay, career opportunities and availability of job openings.

Your interest in data science may have stemmed from another technical discipline, such as software engineering, database development or architecture, actuarial science, any kind of academia, mathematics, biology, astronomy or theoretical physics.

There are several exciting areas of data science…

Artificial Intelligence involves using machines in a way that simulates human intelligence; those capable of exhibiting traits such as reasoning and self-correction. It’s used in business to increase efficiency and enhance performance on a scale that human workers can’t reach alone.

Machine Learning is a practical application of AI that involves teaching computers to learn from data to make the system more intelligent. Businesses are just beginning to make use of Machine Learning to enhance their products. It’s a useful discipline employed in many fields, including game design, recruitment, medicine and customer service.  For the modern data scientist, Machine Learning is now so crucial as datasets have got so large.  It is now too big to simply take it from the database and do the analysis elsewhere.

Data Visualisation is the art of presenting and telling stories with data.

For a Data Scientist, the operative word is ‘scientist’; not engineer, analyst or anything else. Scientists design experiments to further our understanding of reality. This is what distinguishes a Data Scientist from a Data Analyst, Data Engineer, or any other variation.

How to become a Data Scientist

Industry professionals agree that there is no one straight path that will lead to this most sought after of professions. There are however definite desirables that will set you on the road to greatness.  The section below is designed to allow you an insight into the skills and experience you will need to display in order to pursue a career in this highly competitive arena.

What coding skills do I need to become a Data Scientist?

Data science is a hybrid of statistics, computer science and mathematics. As a Data Scientist, you’ll be working at the crossroads between business intelligence and programming. A Data Scientist is defined by their skills using algorithms to make sense of data for business.

You need to know how to program if you’re interested in becoming a Data Scientist. It’s used for writing software, automating tasks and running experiments. If you come from a STEM background, you are already likely to know one of the essential programming languages, such as R (from statistics),Python (from science) or SQL.

Open source languages such as Python or R will give you a broader appeal as these languages are free. Many tech companies are startups and are unlikely to be able to afford another proprietary programming language such as SAS or Matlab. Learn some Python libraries such as NumPy and matplotlib.

Hadoop, Scala and Spark are useful technologies to learn in relation to Big Data, though not necessarily essential. It would also be good to have a working knowledge of Cloud Computing. You’ll benefit from having experience with SPSS statistics, Apache Kafka.

Some of the most in-demand skills for those specialising in data visualisation include Tableau and the D3 Javascript library.

Other useful technical skills

Using Kaggle to compete in competitions is a great way to gain exposure to techniques used in industry.

You may want to look into advanced natural language processing. Natural language is a big part of modern datasets.

Knowledge of Deep Learning is useful for working with images or sounds. For example, if you’re working in a fashion tech company, you might want to categorise all images of clothing featuring red dresses.

Practice tests at home to prepare for your Data Scientist interviews as these will definitely be part of the recruitment process!

What Experience and Educational Requirements are Needed to be a Data Scientist?

Employers prefer you to have had a numerate education, ideally with exposure to Big Data, or large datasets.

Most Data Scientists have a PhD, a Masters or Bachelors degree. This could be in anything from statistics, mathematics, economics, operations research to computer science. Graduates who have taken subjects such as astrophysics are also popular applicants for hiring managers.

If you haven’t had this type of background, don’t fret. Many other people have taken the non-traditional route into Data Science, possibly by enrolling in bootcamps and/or completing online courses.

Commercial Experience and Soft Skills

Of course, the ability to analyse the data you are working with is crucial, however a great data scientist will also need to be able to identify the right questions to ask of the data in the first place.  For this, you will need to be able to identify the problem and understand how to look for a solution.  For this you will often need commercial experience.  You will also need to have the skills to communicate the problem and solution in a language your non-techy colleagues will understand.

Data science bootcamps in the UK

You may want to join a Data Science bootcamp. These are currently helping to fill the demand for Data Scientists since there is no formal training path as yet (although some Data Science degrees have been launched in the US). It’s important to do your research before committing any money to a bootcamp, and many do require a technical background to ensure you’re up to speed.

Here is a list of bootcamps according to location. Prices range from about £3,000 to more than £15,000. It’s a big investment and it’s wise to go for a bootcamp that doesn’t require payment until you get a job (after all, that’s what they’re designed for!).

Data Science Europe is a six-week residential bootcamp that guarantees you a job after completion or refunds the €7500 tuition fee. It is also available online for €5000 which, again, is refundable if you don’t get a job in Data Science.

General Assembly offers a part-time Data Science bootcamp in various locations including London. Completing this course takes a month, and costs £3000 for a place.

Science to Data Science offers Europe’s largest Data Science bootcamp in London, and it’s rated 5 stars on Course Report. It lasts for 5 weeks and is aimed at PhD and MSc level. It costs £800 and is also available online.

The Data Lab in Scotland has recently launched a Data Science bootcamp which is the first ever to take place in Scotland. It costs £7000 and is an intensive three-week program.

The Data Science bootcamp by Data Science Dojo takes place in London and is 5 days long. It was rated 5 stars on SwitchUp! and 4.8 stars on Course Report. Their most affordable package is $2899.99 USD

There are many more Data Science courses available online. Springboard offers an online bootcamp that refunds 100% of your tuition unless you get a job at the end of it. It received 4.9 stars through alumni reviews on SwitchUp!

For a taster, you can also take part in a two-day bootcamp like this one offered by Cambridge Spark for less money than a full-time intensive bootcamp.

Other cheap and easy ways to learn about data science online

Access to Massive Open Online Courses (MOOCs) means that the traditional path to Data Science (through university) has been disrupted. Though not enough on their own, MOOCs available through websites such as Coursera, Udacity, and EdX are a great introduction to Data Science.

MOOCs involve learning at your own pace with online access to help from tutors and being able to interact with other learners. Many offer a certification at the end if you pass.

Here’s a list of some popular Data Science courses:

Indeed, just a cursory Google search of the best data science courses brings up a plethora of opportunity. offers a range of courses that take just hours at a time to complete.   There are masses of options for the data scientist looking to improve their skill set.  These range from courses for the beginner and those of an intermediate standard who are hoping to access further learning.

Maths and statistics

Why would you become a Data Scientist if you didn’t love maths and statistics?

You’ll need to know multivariable calculus, linear algebra, probability distributions, statistical significance, hypothesis testing, regression, and more, including learning the techniques of data mining to generate new insights from pre-existing, large datasets.

For Machine Learning, you’ll need a thorough understanding of Bayesian statistics and other Machine Learning principles.

Data wrangling is important. Cleaning data is a large part of any Data Scientist role, ensuring it is fit for purpose, maintained and stored correctly. Consequently, you’ll definitely need to know how to use Excel.

Building data-centric side projects

Projects will provide evidence to prospective employers that you have the required data science skills.

For example, think of data that you need but is not available. You may want to know what skills are most important to have a particular job title on TotalJobs or to measure the influence of particular Twitter users.

This is particularly easy to do with data visualisation using other people’s data, and you can also publish your results on platforms such as Github. Github can add a lot of gravitas to your CV and future job applications, so it’s worth looking into.

Networking at data science meetups

No one likes to hear the term ‘network’, but it doesn’t have to be as scary as all that. If you’re truly passionate about your career path, you will enjoy connecting with other like-minded individuals in your field.

Go to Meetup events, and email people on teams you’re interested in working with. Meet up for a coffee and chat about your ideas. Find something you’re passionate about within Data Science to share with others.

Finding a data science internship

It’s really important to get an internship, ideally while you’re still studying if possible.

There can be a lot of variety in job postings for Data Scientists, with varying definitions and skill sets included. For example, startups may not need a Data Scientist, but a Data Engineer if they need someone to build their processes. Keep this in mind when choosing an internship.

Data Science Graduate Schemes

If you are looking to advance your career straight from University, or even use your degree as a stepping stone later on, you may wish to apply for a graduate scheme.  These are “on the job” learning opportunities, and such graduate training schemes will allow you to apply the theory to ‘real life’ projects.  In short, you can earn as you learn.

Here is what you need to know..

Places on these courses are highly sought after.  The Kubrick Group specialise in “Growing Data Experts” and offer such a scheme.  They receive 700 applications for their 15 intake scheme, and go on to suggest that only 24% of these applications are worth pursuing.

Graduate scheme salaries

You will be well looked after! You can expect to earn a salary in the region of £25,000 in your first year on schemes such as these, along with shares in the company itself and bonus opportunities.  Many offer an annual increase in the second year and incremental salary raises on the completion of each project or placement.  It is also of note, that should successful applicants need to relocate, then in most cases financial support is offered to do that too. On completion of the scheme, alumni are often locked into a period of employment with the scheme provider, but after that are free to go on to pursue other opportunities. Salaries following the scheme are estimated to be in excess and upwards of £50,000.

Entry requirements

As the name suggests, you will need to be educated to at least degree level and have been awarded at least a 2:1 qualification. However, it is worth mentioning that some schemes require a higher level of education from its applicants.  AXA for example offers a graduate program that requires at least an MSc or PhD.  They also specify that these need to be in data science itself, or another analytic discipline such as physics, maths or computer science.  Others are not quite so exclusive though.  While they do require a degree level qualification, applicants only need an A Level in maths or statistics.  

Some schemes are also challenging diversity within the field of Data Science and are opening up the application process to include graduates they may have missed if they had used a more blinkered application process.  Online testing challenges have been included in the early stages to assess the analytic skills of the applicants.  This approach allows grad scheme providers to evaluate skills in real time.  They go beyond “on paper”.

What is the expected salary of a Data Scientist?

PhD graduates straight out of university can expect salaries of £45,000.

However, if you are a PhD with a couple of years of work experience in research, you can expect to enter the private sector in London with a salary of around £60,000.

More experienced Data Scientists frequently earn in excess of £100,000.

Data Science Jobs in London

London provides a rich seam of opportunity for talented data scientists. Not only is the remuneration higher, there is also a higher density of employers that are up to some very interesting work.

From tech giants like Google, Facebook and Amazon, to innovative, agile startups, there are data science jobs aplenty in London.

When looking for your first data science job you must give serious consideration to London, nowhere else in the UK provides this level of opportunity.

Final Remarks

Becoming a Data Scientist is no easy task and you will likely have put in years of work on your chosen career path.

It’s challenging to switch from an unrelated career to one of data science because you need a mathematical background and other skill sets. Career switchers do best if they come from a related field such as programming or physics.

Of course, these things are always possible to learn but it takes time. Bootcamps are a great way to accelerate your learning but are no shortcut if you haven’t put in the legwork.

Contribute to open-source projects and try to gain some freelance experience if you can. All this will add to your experience and credibility as a future Data Scientist.

If you are currently finishing off your PhD or MSc and want some advice on the next steps on your road to data science, then please fill out the contact form below…


About the author: As a founder of Ignite Digital Talent, I lead our brilliant team to ensure we deliver time and time again for our clients. I also stay closely networked with industry influencers to ensure we are well placed to understand the issues and challenges our clients face.

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