In our 10 trends for Data and Analytics in 2017 report, we highlighted the rising need for data science skills in the Data & Analytics industry.
“The Data Scientist is probably the most in-demand technologist out there, with people who qualify commanding a significant salary.” According to Data Mation. “Whilst the demand will continue to grow for Data Scientists themselves, there is an expectancy that all within the industry will need to begin to develop their data science skillset. Adapting your skillset is part of staying relevant and employable in this ever-evolving industry.”
The data scientist has been dubbed the sexiest job of the 21st century in an article published in Harvard Business Review. In the article (from 2012), Davenport and Patil say: ‘The shortage of data scientists is becoming a serious constraint in some sectors’. With the growing demand and increasing salaries for data scientists, we wanted to take a look at 5 skills that are important to know for all data scientists, especially moving into 2017.
Which Data Science skills should you develop?
1. Programming languages
One of the first data science skills you need to ensure you’ve got is a solid knowledge of programming languages like R, Python and SAS. R is an open-source programming language and software environment for statistical computing and graphics. Python has been widely used for high-level programming since 1991. It is universally recognised within data science sector and beyond. As always, the more skills you are equipped with, the more roles you will be suitable for, making you attractive to recruiters and therefore increasing your chances of landing a job. You can often also demand a higher salary if you have a wide range of skills.
2. Database query languages
SQL and NoSQL are the most popular database query language in the industry and are regarded as essential by most. It is certainly advisable to be skilled and continue to deepen your knowledge in these areas as they continue to be incredibly important in the data science world.
Whilst SQL is perhaps the most popular, there are plenty of other languages that data scientists can learn. With an SQL-like interface, Facebook’s data team created the language Hive for programming Hadoop projects.
3. Software engineering
This is especially relevant if you’re working for a smaller company or a company that are only at the start of their data development. With less data scientists on board, you’re likely to be responsible for large amounts of data, data logging and setting up the processes themselves. Being skilled in software engineering will enable you to be able to hit the ground running, and that is always attractive to companies when making a new hire.
4. Machine learning
When working for data-driven companies, it’s important to be familiar with machine learning methods. Ensure you’re familiar with machine learning buzzwords like k-nearest neighbors, ensemble methods and random forests. Programming languages like R or Python do enable these to be implemented so it may not necessarily be an essential for all data science jobs, but it is useful to add that extra skill to your armory.
5. Get properly qualified
The statistics show that data scientists are highly qualified, with 88% obtaining a masters degree according to KD. That’s a huge proportion of the workforce and incredibly 46% has PHD’s. Achieving a degree is the bare minimum requirement for a quality data scientists, even a Masters degree is considered pretty much the norm. Unfortuantely there’s not really many alternative routes into the industry, so ensure you have the right qualifications to develop your data scientists career correctly.
Are you a data scientist? At Bodhi we’re currently recruiting for a Data Scientist role in London – so take a look and apply, or upload your CV using the button at the top of this page.