With the explosion of data across just about every industry, one of the fastest growing career fields is data science. According to Deloitte Access Economics, careers in data science are expected to grow at a rate of 2.4% per year, which is substantially more than the average growth rate of 1.5% for all other careers.
Unfortunately, however, there is one thing missing from this exciting career track – women. Although in Australia there are now more female STEM graduates than there have ever been (55% of all STEM graduates are women), there are simply not enough women in data science. In fact, only 15-22% of those in the industry are women.
However, many women have found success in this field, and many more should be encouraged to enter it. If you are a woman who wishes to explore this field, or are a woman in data science, here are five tips to help you make the most of your career.
1. Be flexible and agile with career planning
Data science as a discipline continues to evolve at lightning speed, with technology, applications and forms of analysis constantly changing. As such, for women in this field it’s important to focus on keeping up with current trends and technologies in the industry. There will always be something new to learn, which presents an exciting opportunity.
As new technologies and jobs emerge, professionals who are adaptable and agile in their career planning may find more opportunities to succeed in data science roles compared to those who are less flexible. To make the most of these opportunities, women should endeavour to keep up with industry trends and technologies, network as broadly as possible, and always be open to new and different projects.
2. Pursue postgraduate study
Data science is a technical, exciting and ever-evolving field, and for this reason, pursuing an advanced degree such as a Master of Data Science can help women get ahead in their careers.
Postgraduate study can help professionals gain not only academic skills in the field, but practical ones too. For example, JCU’s 100% online Master of Data Science helps students build a portfolio of skills through capstone projects, while learning an essential suite of programming languages, such as Python, R, Tableau, AWS and SAS.
The statistics paint a stark picture: According to the Department of Industry, Science, Energy and Resources, women make up only 28% of the workforce across all STEM industries in Australia. Just 23% of managers are women. As a result, women in data science may feel isolated. With few if any role models, they may not see a path to management, which can lead to lower salaries compared to their male colleagues. This isolation can contribute to the so-called “leaky pipeline,” in which women enter STEM but these barriers cause them to leave the field altogether.
One solution is networking. Networking helps build connections with colleagues inside a company and across industries. Organisations such as Women in Technology provide community and networking resources, career opportunities and chances to meet with others in a career field. Networking offers an opportunity to connect with potential role models and mentors – and to become a mentor in turn. It can also help in keeping up to date on exciting new developments and a chance to advance in one’s career.
4. Generalise — don’t specialise
The goal of data science is to increase operational efficiencies through iterative learning processes. That’s how algorithms get better at their jobs. As a result, there are many different roles in data science – analyst, researcher, machine learning specialist and more. While it may seem intuitive to pick a specialty, a narrow focus can lead to narrow job choices and less opportunity for growth. Instead, experts say, early career data scientists should generalise, not specialise. Here’s why:
- Process evolution: Algorithms, chatbots, optimisers, and design systems are all learning as they go. They adapt and react as data comes in. Specialisation can prevent this adaptability.
- Workflow: Specialisation means projects can’t proceed until tasks are done in order. This works for factories; it doesn’t work for learning systems.
- Information flow: Specialisation can leave people out of the loop, whether that is the project goal to query structuring to initial datasets. There’s more chance for inefficiencies and less opportunity to pivot to a better course.
As generalists, all data scientists can add value by participating in goal setting, scope, project design, project assessment and project refinement. By building a broad field of expertise early in your career, you can still take advantage of opportunities to specialise later in your career and make your diverse skillset a real asset.
5. Find a mentor or a sponsor
In fields where women are outnumbered, a mentor, or someone who provides professional advice and guidance, can be essential for helping understand how to navigate the workplace environment. A mentor can provide women with valuable advice on how to address everything from technical challenges to corporate politics, and everything in between.
What can be even more helpful is a sponsor. A sponsor is a senior-level staff member who is invested in the success of the person they are working with, and advocates for them so they can get noticed and promoted.
Most organisations have either formal or informal programs for women to find mentors or sponsors. If an employer doesn't have such a program however, it is possible to find mentors through external programs, through networking websites such as LinkedIn, or by contacting industry-specific women in data science groups.
Data science: a career for everyone
In the next decade, there are set to be thousands, if not tens of thousands of new jobs available in the exciting field of data science. Data science is a field for everyone, and women in data science should feel confident and comfortable, studying, working and ultimately succeeding in this area.
Learn more about how a Master of Data Science degree from JCU Online can kickstart your career in this inspiring and intriguing field.
- Deloitte Access Economics “The future of work | Occupational and education trends in data science in Australia”
- Department of Industry, Science, Energy and Resources, “Second National Data Report on Girls and Women in STEM”
- Harvard Business Review, “Why Data Science Teams Need Generalists, not Specialists”
- InformED “Women of Data Science: Who’s Leading the Way?”
- Forbes, "5 Unconventional Career "Tips From Top Women in Data Science"
- Forbes “The Long Term Impact Our Boss Has on Our Career”
- Women’s Agenda, “We Need More Women in AI and Data Science: How Can We Make It Happen?”
- Women in Technology, “State of the Nation for Australian Women in STEM”
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Find out more about JCU’s online Master of Data Science.
Get in touch with our Enrolment team on 1300 535 919