The sudden demand for data scientists has come about through the advent of ‘big data’ – sets of data that are so immense that current applications cannot handle them properly.
Professionals with programming skills and the ability to think analytically are needed more than ever to mine the data and interpret it to aid business strategy.
While the shortage is a worry for businesses, it’s good news for budding data scientists. With big data jobs expected to be created at a rate of around 56,000 per year until 2020, salaries are expected to be impressive.
According to global management consultants, McKinsey & Company, between 140,000 and 190,000 job postings in data science will still be vacant by 2018. But what is involved in data science, and what skills are needed to become a data scientist?
The role of a data scientist
A data scientist looks at raw data, combines it with analysis, and presents it in a way that assists organisations when making strategic decisions. A combination of skills and knowledge of algorithms, mathematics and human behaviour is essential, along with experience in the type of industry the data is for.
Sectors at the forefront of using big data have been e-commerce, retail and finance. However, as more companies are turning to big data to influence their decision making, data scientist job postings are appearing in other sectors, such as transport, energy and telecoms.
A solid understanding of programming languages used in big data applications is a requirement for the majority of junior positions. Python, Java, C# and R are the more common ones, but MongoDB and Hadoop are increasingly being used for big databases, so having knowledge of these will be an advantage.
Start by learning about general areas like databases and visualisation tools, and then specialise on one or two of the main languages. More senior roles will require strong experience and broad and deep knowledge of a range of technical platforms.
On top of the technical knowledge, a data scientist needs excellent analytical skills, with the ability to not only analyse raw data, but to identify objectives that will be profitable and provide recommendations based on the findings in context. Here is where industry knowledge is crucial, as it enables the data scientist to relate the analysis to real life issues.
When all the analysis is done, it needs to be presented coherently. The ability to listen to the stakeholders and understand the business problem is essential to help provide the right solution.
You may be given sample problems, so the interviewer can assess the way you think and deliver solutions. They may want to see how you would use big data technology to solve problems, and will be watching to see how you approach them.
The technologies surrounding databases continue to evolve, so it will pay to keep your knowledge up to date and current. With big data proliferating, now is a great time to get into data science.
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