With the rise of big data comes the need for more highly skilled people to mine and interpret that data for businesses. This is the role of a data scientist, the job that Harvard Business Review called "the sexiest job of the 21st century" back in 2012.
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As technology continues to develop at a rapid rate, we’re seeing a number of key changes in how we work and what it means to be employed.
The quicker tech evolves, the quicker the skills needed to work with that tech become obsolete. This means a renewed focus on a candidate’s potential to learn and self-educate rather than just their existing qualifications and strengths.
One job that has greatly grown in prominence in recent years is that of the data scientist, a role Glassdoor has named the best job in the US for three consecutive years.
Generally, data scientists are highly skilled, can work in a number of different industries, have high earning potential and report high levels of job satisfaction in their roles. For example, UK data scientists earn an average of £50,000 per year.
And, according to a report by IBM, demand for data scientists will increase 28 percent by 2020.
What is a data scientist?
The role of a data scientist is to analyse and interpret large digital data sets and derive actionable insights from the findings.
This is different to a data engineer, whose primary role is to store and prepare that data, so someone with expertise setting up and maintaining large databases.
The skills required of a data engineer tend to be more technical, with knowledge of Hadoop, SQL and NoSQL databases.
Qualifications & skills
As with most emerging tech roles, there’s no prescribed career path or specific qualification required.
Most data scientists will have a background in maths, engineering or computer science with excellent knowledge of programming.
Statistics and probability are two fundamentals of mathematics which are key to the role of a data scientist and are necessary for analysing and interpreting data, predicting patterns, making hypotheses, etc.
Data visualisation is an important component of a data scientist’s role. Presenting complex ideas and findings in a coherent and effective way is crucial, bearing in mind that business leaders and decision makers won’t always be of a technical mindset.
Tools such as Tableau and Plotly are useful to showcase large data sets to the relevant stakeholders in the business and make the information easier to process. They also allow for team collaboration and interaction with the data.
GitHub and Kaggle are great resources for building technical experience by working on open source projects and competitions.
Data scientists need to know how to extract the most value from machine learning as it becomes more prominent in multiple sectors.
This involves pairing algorithms with the right tools to build efficient and replicable processes based on your business goals.
Key traits and soft skills
It’s not all about tech prowess, a data scientist also needs to be an accomplished problem solver and communicator.
The ability to clearly articulate findings and suggestions is a core skill here, don’t undervalue it.
An analytical mindset is a must, as is a curiosity about why things work in a certain way. As well as that you need to be flexible and adaptable so you can effectively incorporate new learnings and processes into your role.
Data scientists require an always-on learning mentality and a genuine flair for investigation and innovation.
Immersing yourself in the tech community helps to grow your career as a data scientist, learning from industry experts and peers.
Attend meetups, conferences and events and join online groups and discussions to make valuable like-minded connections.
One skill that is of growing importance in data science circles, and within the enterprise, is machine learning.
"Machine learning is a no-brainer to me. That is the true heart of data science," said Mike Ferguson, an analyst at Intelligent Business Strategies.
"People want to have a pattern detection and a view into the future, so the traditional career in reporting is no longer enough, which is a key reason machine learning is critical. The days of taking data out of a database and doing the analysis somewhere else is done, the data is too big."
Speaking with Computerworld UK, Nuno Castro, director of data science at Expedia said that a great data scientist must be "persistent, highly energetic and motivated".
His advice for any prospective candidates was to: "Follow lots of other data scientists on social media - Twitter - read blogs, learn a new data science technology, practice on Kaggle or possibly enrol in an intensive data science course.
"After you’ve done that, try to get a data science internship. Make sure that you’ll be working on a cool end-to-end data science project or a deep dive on a specific piece with a measurable output, e.g. a new algorithm that you can A/B test, rather than just doing what everyone else doesn’t want to do, e.g. unit tests, though you will still learn."
From a technical perspective, Castro said it was better to learn using open source technologies and skills, like Java, Hadoop and SparkML. This way "when the next technology buzzword arrives you will be ready. If you spend your days working with a proprietary technology with its own programming language and workflow, how transferable is that and how will that add value to your CV?"
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