What is a data scientist, why the demand for their services is growing and how to enter the profession? The three core skills of analytics, technical and presentational required.
‘Big Data’ Needs ‘Big Analysis’
If you’re good with figures and like ‘number crunching’ in a big way then a career as a data scientist might appeal - especially as there’s predicted to be a worldwide shortage as demand accelerates on an almost epic scale. In the UK alone some 56,000 big data related jobs are expected to need filling each year until at least 2020. In the US the demand is even more acute; global management consultants McKinsey predict there will be a shortfall of between 140,000 -190,000 people with “deep analytical skills”.
What is a Data Scientist?
Fundamentally a data scientist manipulates and interprets raw data and analyses it to extract insights to recommend actions or inform decisions in the context of the organisation they’re working for. This is a massive step up from basic data analysis thanks to the rise of ‘big data’ - the huge avalanche of data being collected in ever-growing amounts by organisations and businesses from users and customers.
The data scientist uses various skills to make sense of the data using machine learning and predictive analytics to turn it into useful intelligence. After all, huge data gathering is of very limited value unless it can be made constructive use of and this is where a data scientist is in such demand.
Who Uses Data Scientists?
Many large businesses use data scientists today, but the main ones so far are finance, retail and e-commerce, who are all hungry to better understand their markets and, as a result, more effectively relate to and target them according to their tastes.
Other industries such as telecoms, energy supply and transport are using big data to make the decisions that will help their business become more effective. As more data is being collected, more use is being made of it and so more data professionals including data scientists are required. Software development companies use data scientists to build bespoke systems that analyse big data and convert it into actionable reports.
The Skills Needed By Data Scientists
Technical - certain programming languages such as Java, Python and Hadoop are required for junior level positions. A raft of new technologies are appearing each year as the profession matures, particularly in the realms of modelling and visualisation tools, so the need to always be learning and being able to grasp new technologies swiftly is important.
Analytical - the ability to take raw data and turn it into useful intelligence is critical. The data scientist has to be able to identify trends and set them into context, find solutions and make usable recommendations for their employer. A solid grounding in statistics is vital.
Presentational Ability - being able to convey the findings succinctly and in a way easily understood by others is important as is the ability to listen and so understand their employer’s objectives.
Industry Knowledge - it makes for a more effective data scientist if they have an understanding and ‘feel’ for the specific industry they’re working in. It helps them understand context better - a key element of big data analysis.
How To Become A Data Scientist
In many ways, learning the right technical elements such as the relevant programming languages and getting a proper insight to an industry of interest to work in are two key steps to take.
There is some formal training available. For example, Brunel University offer an MSc in Data Science and Analytics which can be studied over one year (full time) or two years part time with the option to combine it with a placement with a company or organisation.
Lots To Learn But Worth It
Before becoming a fully-fledged data scientist there is, as shown above, many facets to learn and it takes time - especially to gain a feel for the industry to be worked in. Once this is achieved though, demand is very high and as a result high salaries are commonplace. A career in data science is certainly highly recommended.