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How to Become a Data Scientist

8 May 2018
Peter Dunn

How to Become a Data Scientist

Someone being a Data Scientist is a relatively new phenomenon, but it has quickly become a highly sought-after and incredibly lucrative position.  There is a huge demand for data scientists, the best of which can now command handsome salaries.

At its core, data science is about transforming data into business value using mathematical algorithms and machine learning techniques. The barriers to entry in the data science world are mostly determined by your experiential and/or academic background. If you’re fortunate enough to have a degree in a relevant subject, such as statistics, AI, or engineering, how can you make the transition into data science?

One of the biggest hurdles to overcome on your journey to data scientist-superstardom is that fact that you need a long list of skills. It’s crucial to be have the analytical capabilities of a scientist while also having the business expertise of an executive all while being a data guru. Simple, right?

Many companies have differing expectations of what a data scientist in their employ would be doing, so it’s a good idea to thoroughly research any company you are interested in joining. Find out what the other employees do, look at how they communicate (their Twitter, Facebook, LinkedIn, webinars, and so on), and understand what their definition of a data scientist is and whether this matches your own.

How to Become a Data Scientist

Some data scientists spend the majority of their time in research and development with a strong focus in mathematics. This can involve anything from developing and testing new algorithms, to writing mathematical proofs, to reducing data problems. Others focusing more on data analysis and writing up results. This can include working on forecast models, predictive models of metrics, and data mining. Most use R and Tableau for projects, though Python, Matlab, and SAS are occasionally helpful.

Many corporate companies are crying out for data scientists, especially those that rely heavily on consumer data or crunching large data sets and social media streams. Being able to apply advanced statistical modelling techniques to help solve important business problems could save them enormous amounts of money, or even generate significant income.

It's important to work on your soft skills as well as your technical ones. Conducting your own SWOT analysis can be an insightful way to map out your career and where you want to go next. Making sure your behaviour in work is condusive to career development is very important as well.

The data field is constantly evolving with scientists taking businesses in new directions, solving complex algorithms, and developing world first processes. This evolution in data, coupled with the huge range of skills that data scientists possess, makes the role of a data scientist exciting, challenging and very relevant to the 21st Century. If you're interested in this field of work, or are looking to recruit a Data Scientist, feel free to contact our IT team.


Peter Dunn
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