When I was growing up the term “data science” didn’t even exist, let alone dedicated “data scientist” roles. My friends and colleagues might argue that is because I am yet to grow up (!), but do not let this ruin my lead in to the fact that data science as a field and data scientist as a job title is a very recent smash hit, even though many were doing most of what we call data science decades ago.

Making an assumption that you have read widely on what data science is, beyond the ability to type data scientist into LinkedIn, you know that there are massive opportunities in the field and that these skills are in high demand. I have seen myself firsthand how hard it is to find these skills in the market. Becoming a data scientist can increase your paycheck and set you up for a challenging and rewarding career.

On top of this, a recent Money magazine and PayScale.com study showed that SAS skills are the biggest pay differentiator in the market. SAS and data science together could set you well on your path, so if you are sitting here wondering, “How do I work through the multitude of learning options available to me?” I am here to try to help. Below I have covered three approaches to set you on your path to becoming a data scientist, ideally a SAS skilled one as well!

1. Undertake a degree program in data science or advanced analytics

Over the last three years we have seen degree programs in data science and analytics develop into some of the most sought after programs for universities and business schools to establish. Below is a snapshot of programs currently running in Australia and New Zealand in data science or advanced analytics.

There are options for everyone in there; varying focuses and specialties, face to face / blended / fully online options, intensive programs, or the more standard full time and part time options. The below five programs also include a Joint Certificate from SAS.

• Master of Data Science at the University of South Australia
• Master of Business Analytics at Melbourne Business School
• Master of Business Analytics at La Trobe University
• Master of Analytics (Business or Health) at Massey University
• Master of Analytics at Auckland University of Technology

There is also the Master of Data Science and Innovation at the University of Technology Sydney as well as the Master of Business Analytics at Deakin University. There may be more, however I have listed the ones I have worked closely with over the last few years.

2. Enrol in a data science academy or boot camp

Data science academies or boot camps allow you to upskill, typically in a shorter space of time, with a more time intensive commitment during that period. The SAS Academy for Data Science is one example. It is an intensive training and certification program focused on creating end-to-end excellence in data science. Importantly, successful completion of the academy leads to a globally recognised credential from SAS.

The content within this academy is split into two levels, Level 1 being focused on the skills to be a big data professional, Level 2 on advanced analytics skills. Individuals can take either level or both, but completing the exams within both levels leads to the data science credential. Technology utilised within the levels goes beyond just SAS, to include Hadoop and open source technologies. The first Australian intake into this Academy starts on August 29 in Sydney with the 6 weeks of level 1 content being spaced over 3 months.

Classroom and live web instruction is combined with hands-on case studies, projects, self-paced resources, exam preparation and certifications to create a blended learning model, all guided by a coach to help mentor students along the process and prepare for certification.

3. Take a data science related MOOC

Whether you love or loathe them, Massive Open Online Courses (MOOC’s) have brought an amazing amount of learning resources to student’s fingertips, from some outstanding organisations. There are an abundance of data science related MOOC’s to consider, this article attempts to bring together a list of the top 20. Many are focused on areas within data science, rather than data science as a whole, so you may need to look at a combination of a number of them to create a more comprehensive data science learning pathway.

Completion rates for MOOC’s have long been the bane of their existence, most reports have completion rates at somewhere between 5% and 10%. The numbers are swayed slightly by students who enroll and never plan to complete, they are just using the MOOC as a job aid. However, it still illustrates that to successfully complete a MOOC you need to be a strongly driven individual, more so than the average individual who was already motivated enough to register.

The time and financial investment in the above three options vary, however the return on your investment is there in the years to come if you are serious about enhancing your current skills and pursuing data science as a career. Whatever you choose from the above, remember that learning never stops, (neither does growing up, I guess, as well), so continue to seek opportunities to learn about data science through both training and experiences. As Leonardo da Vinci said “Learning never exhausts the mind”.