There may be many reasons why you're thinking about a career as a data scientist. In this article, we’re going to look in more depth at becoming a data scientist. We’ll look at what they do, the skills you need and how to land your first job in the industry.

What is a data scientist?

One of the by-products of the new tech age is the emergence of big data. Connected devices produce data sets so large and detailed that we need new ways to analyse them, then turn them into something we can use. That’s where data scientists come in.

Data scientists take this raw data, apply analytical techniques, including algorithms, to discover insights and pinpoint trends. Businesses now use big data to drive strategy, so data scientists are essential to their growth plans. You’ll find data scientists working in all areas of the economy, from retail to finance, from telecoms to transport and more. In 2020, data science is essential to the growth of AI and machine learning. It’s the keystone of the Internet of Things – connected devices that make our lives easier. In the future, as low latency Edge Computing grows in popularity, real-time data analysis will become the norm. However, none of this is possible without the skills of data scientists.

Why become a data scientist?

Becoming a data scientist is an excellent career move for many reasons. The work is always interesting – you get to work in exciting industries making a tangible difference.

It’s also well-paid. The average yearly salary for a data scientist in London is £60,000. You may also have the opportunity to work flexibly, from home, or anywhere else you choose.

Data scientists are also in great demand. In fact, there is a skills gap. Over the last five years, demand for data scientists in the UK has more than tripled. There are not enough people with the right skills to meet that demand. If you have those skills, you are in a great position to build a long and fruitful career.

What skills do you need?

To get started as a data scientist and land a role in a business, you will need to demonstrate the right skills:

  • Technical – You will need to understand the programming languages that big data applications use. These include JavaScript, Python and R. It is also advantageous to know your way around database apps like Apache Hadoop and MongoDB. Things change in data science quickly though, so make sure you stay on top of new technical tools.
  • Mathematical – You need the right maths skills to be able to analyse the raw data and use it in the right way to help your employer. You also need to understand the specific problems of your industry, so you can use the data to solve them.
  • Articulation – You need to be able to articulate what you find in the raw data to people who aren’t data scientists. This includes building reports and presentations, as well as being able to tell top businesspeople what they should do next.  

To learn these skills, you can take a dedicated university course in data science and analytics. Although, many people will come to data science with computer science or mathematics degrees.

Who should I follow?

For more information on data science, there are some excellent blogs you can follow:

  • What’s The Big Data? – This blog looks at the practical impact of data science in the real world.
  • Data Science 101 – As the title suggests, this blog is essential reading for those just getting started in the field of data science.
  • Data Science Report – A hub for data science content curated by Starbridge Partners, a US recruitment firm.

How to land that job

Once you’ve built those skills, it’s time to land that all-important first job. To stand out as a data scientist without experience, the best things to do are:

  • Create a portfolio
  • Make connections
  • Start applying

Building a portfolio is a challenge if you have no track record, so you have to get creative. However, it is worth doing to put yourself ahead of your competitors when applying for roles. Build your own website where you can show off your coding skills in the relevant languages for data scientists. You can also spend time on GitHub – the industry-standard repository for code; or Kaggle, which is similar, but more specific to data science. You could also look for open source projects to help out on, which will help you forge valuable connections.

Connections are essential when starting out in data science. Because it’s exciting and relatively well-paid, there is competition for the best jobs. Unfortunately, it sometimes comes down to who you know, not what you know. Become a part of your local tech scene if there is one. Join groups on LinkedIn or Slack where you can network. Attend industry events and meet-ups. Meet as many people as you can – you never know where you will find your next opportunity. Finally, start applying for roles that grab your interest. Look on job boards, talk to recruiters, do whatever you need to do to get your foot in the door. Make sure you follow up on any applications (don’t wait for them to get back to you). Go to interviews - even if you don’t land the job, it’s all useful experience. Get as much feedback as you can and take it all on board. Your game-changing career in data science is waiting for you. Good luck!

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