How To Kickstart Your Career In Data Science

The financial services, retail and eCommerce sectors are leading the charge in hiring data scientists to help them better understand different audience segments and target them with products and offerings that are tailored to their tastes.

The recent surge in data, connectivity and computing power - combined with powerful, new analytics tools - has sparked huge interest in data science. Thus, "Big Data" has emerged as a growing trend, used to describe data sets so large and disparate that traditional data applications don't have the capabilities to process them. In response, forward-thinking businesses are looking at how they can come into the business; mine and interpret the required datasets, identify patterns and gain deeper insights into their customers and organisation to make better, more informed strategic decisions.

Britain is expected to create an average of 56,000 big data jobs a year until 2020. But, with big data talent in short supply, companies are increasingly willing to pay sky-high salaries to bring in the right skill sets, with many individuals commanding six-figures. McKinsey & Company reports that by 2018, there will be 140,000-190,000 data scientist job postings that go unfulfilled. Worse (for the employers), there will be 1.5 million managers needed to optimise available data. Therefore, the next three years mark a veritable gold mine for data scientists.

So, what role can a data scientist play in an organisation, and what skills are required to get your foot in the door?

Data science explained

A data scientist takes raw data and marries it with analysis to make it accessible and more insightful. To do this, they need a unique blend of skills - a solid grounding in maths and algorithms and a good working knowledge of human behaviours and the industry they're in to put their findings into context. From here, they can unlock insights from the datasets and start to identify trends.

The financial services, retail and eCommerce sectors are leading the charge in hiring data scientists to help them better understand different audience segments and target them with products and offerings that are tailored to their tastes. However, progress is also being made in industries such as telecoms, transport, and oil and gas, as companies come to rely on big data to make decisions that impact their sales, operations and workforce.

The skills you'll need to impress a potential employer

A successful candidate needs a combination of technical programming skills, experience of using analytical tools, expertise in modelling techniques, relevant industry exposure and strong communication skills.

  • Technical skills - The most common programming languages used in big data applications are Java, Python, C# and R and a good understanding of some of these will be required for most junior-level positions. In terms of big databases, there has been significant growth in Hadoop and MongoDB, so learning these will stand you in good stead. Though these are some of the fundamental technologies, there are also a plethora of niche tools being introduced to the market every year. Depending on your experience and interests, you can start by choosing the broad areas like databases, analytical modelling and visualisation tools and then focusing on one or two leading languages and platforms. For senior-level roles, employers will look for a strong technical background and professional knowledge across a range of technical skills.
  • Analytical skills - While technical skills are important, analytical skills are just as critical to your success as a data scientist. You need to be able to take raw data and identify profitable business objectives, putting that analysis into context to find a solution and come up with recommendations for the team. Having exposure to at least one of the industry verticals would also give added advantage here, enabling you to analyse real world problems.
  • Presentation skills - Of course, if the results can't be presented back to the required stakeholders clearly, there's little point in taking the time to do the analysis. A successful data scientist will deliver information effectively but also listen to the stakeholder's requirements and really understand what the business problem is first, in order to solve it.

How to approach your interview

An interviewer will be looking for individuals with a "big data mindset" and may give you sample problems to assess how you think on your feet to come up with solutions. They'll also be looking for how you'd use big data technologies to solve problems. While skills and experience are important, the interviewer will also be assessing your thought processes and how you approach each situation to decipher whether you have the mindset required for the role.

The opportunity for data scientists

Database technologies are where I see a lot of innovation taking place, with traditional databases being replaced with new offerings from Silicon Valley. With such focus on this area, there's never been such demand for data scientists with the right experience and skills. Individuals that demonstrate the qualities discussed here should grab those opportunities with both hands to kickstart their careers.


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