Jumping into a data career can be exciting, and a little scary. On one hand, job demand for good data scientists is booming and shows no signs of slowing down (experts predict 2.7 million new positions globally by 2020), and The Harvard Business Review called the emerging role as “the sexiest job of the 21st century”. On the other hand, nobody knows what the world of data will look like in six months, let alone five or ten years.
Regardless, there’s an undeniable skills gap when it comes to trained data scientists in Australia. All of this translates to big job potential, but also high volatility. In short, it’s an excellent time to make the switch into data science.
So what is a data scientist?
Data Scientists are critical to the success of both small and large business alike. It’s their job to mine complicated statistical information, and then feed the data back into the business, which informs and guides overall strategy. They’re one part IT professional, one part programmer and one part experimental scientist. More and more companies are investing in Big Data to answer big business problems and get a competitive edge.
The good news for data scientists is that there’s more than enough work to go around. Back in 2011, the McKinsey Global Institute predicted that by 2018, the US would face a data skills gap of around 1.5 million people. But we’ve already blown way past that number. In a recent report, 83% of data scientists believe there’s a shortage of skilled workers in the industry.
Do I need formal qualifications?
Like blockchain and AI, the data boom is a recent one and formal qualifications are far and few between. The question is, will ‘on the job’ training be able to rapidly activate our ecosystem to nurture and grow the talent of today for the jobs of tomorrow? As individuals in the workforce, not enough of us are replenishing our skills in order to keep up with the rapid change we are seeing. And as businesses, we can’t leave the opportunity to upskill and reskill our staff to chance.e. Tertiary institutions are beginning to offer online data-based short courses in fields like Machine Learning and Data Analytics allowing full-time workers to rapidly upskill and jump into a new career, without putting their work or life on hold.
What hard skills should I learn?
The field of data is vast, with PwC estimating an additional 2.7 million jobs globally falling under the ‘Big Data’ umbrella. But generally speaking, you’ll want to become familiar with programming basics and coding languages like Python, R and Java (Clojure, Haskell and Scala are a bonus), as well as common database tools like SQL, RDBMS, Hadoop, HBase and Webscrapers. And don’t ignore the importance of good old Microsoft Excel. Broadly speaking, you can break down data skills into five categories: programming, statistics, machine learning, data wrangling and data visualisation.
All this might sound intimidating, but truthfully you don’t need a coding background to become a data scientist – just need a willingness to learn. You can pick up a lot of these skills through online shortcourses, and others you’ll learn on the job.
What’s the best pathway into a career in Data?
The three most common pathways for data jobs are Data Analyst), Data Scientist, and Data Engineer). If you’re just starting out, Data Analyst is probably the best option. RMIT Online offers a dedicated shortcourse in Data Analytics for those with some Python coding experience. Once you’ve equipped yourself with some basic data skills, look for internships or junior analyst opportunities, and work your way up from there.
Do I need any soft skills?
Despite being a very tech-related field, soft skills are becoming increasingly important for data scientists. The number one skill, according to education giant Udacity, is an inquisitive mind. A good Data Scientist will look at problems critically, to turn a business upside down, shake it, and figure out what’s going on. Often that means asking questions to which nobody (yet) knows the answer.
The other advantage of a data career is that you can leverage skills gained in other areas of business. In 2015, Smart Data Collective did a comprehensive LinkedIn study, and found that senior data scientists are more likely to trade on their business acumen, strategic thinking, leadership and team management, rather than their hard data skills.