Did you know that everything Internet-related generates data? A text message, a YouTube tutorial, checking LinkedIn, buying a saucepan off Amazon all generates data, and businesses use that data to gain valuable insights into everything from their customers to their productivity. But what is data without people who know what to do with it? Cue the Data Scientists.
Typically at the peak of the academic hierarchy, the majority of data science professionals come equipped with a Master’s Degree and a select few have a PhD. While not a general rule, it must be said that a strong educational background comes in handy in order to develop the depth of knowledge necessary to be a data scientist, who earn on average £45,000/year.
With a foundation in Computer Science, mathematics, statistics, engineering, or physical or social sciences; aspiring data scientists can glean the skills they need to process and analyse big data.
Data, of course, is at the heart of working in data science. Pulling it, merging it, analysing it, building visualisations from it, finding problems within it and on and on. There are a great many data tasks involved in the day to day of a data scientist’s job, however what a data scientist is needed for primarily is problem solving.
Numbers and stats may be your bread and butter but what underlies it all is the needs of the business you’re working for. Business savvy is just as important a skill as technical expertise. Demonstrate your understanding of the industry and how the problems you solve can impact the business. You need to be working on identifying how the business could leverage its data, and ultimately using that knowledge to create ‘big picture’ changes for the better.
Candidates applying to data scientist jobs should have experience with a wide variety of analytical tools. R, Python, Perl and C/C++ are among the most popular tools and coding languages for data science roles thanks to their versatility. When it comes to working with big data, Hadoop and Apache Spark are both useful tools to become acquainted with. You will require additional training to be able to use them.
Not all data comes in a compact database format, particularly in the age of social media, so you must also have the ability to work with and manipulate the unstructured data that come from different platforms including videos, blog posts, customer reviews and so on.
Ongoing self-education, as in most areas of cyber security related work, is certainly a must-have if you see yourself in a data science role. The field is constantly changing with new information exploding daily as data science professionals work out new ways of solving problems. Thus it’s imperative that you follow and subscribe to relevant journals and blogs, newsletters and discussion forums to keep yourself continually updated. Online networking and in person at relevant conferences will also further both your knowledge and your profile as a data scientist.
Data science is fundamentally rooted in experimentation, as its propagators work with new types of algorithms or models and so on in order to find the best ways to solve the problems and answer the questions in the data. This is also where fluency with SQL comes in as it is designed to help you access, communicate and work on data.
Tools like Tableau, ggplot, d3.js and Matplottlib will prove invaluable in your data visualisation work as they enable you to convert complex results into a format anyone could understand. This is an important point and a segue into a critical soft skill required in data scientist jobs and that is communication. You must be able to translate your technical findings into a narrative that every person in the organisation can relate to.
Whether it’s discussing strategy with the company executives, launching campaigns with the marketing department or product meetings with designers, not to mention relaying the necessary information to your clients; you must be able to work effectively with every person in your organisation and communicate with each and every one of them in a language they understand.
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