How Paula changed careers with online data science courses

When her teaching work started involving more and more data analysis, Paula realised she was interested in pursuing a career in data science. The challenge? Growing the skills employers were seeking around her full-time job. 

Through UC Online's flexible master's in data science courses, Paula gained the skills she needed to become a Data Analyst/Scientist at High Performance Sport New Zealand — read on to learn how she made this career change happen. 

Finding the right data science course

Like many prospective career changers, Paula faced the challenge of finding a qualification to build the skills employers were seeking without stepping away from work.

"I saw the UC Online Master of Applied Data Science advertised through social media and realised it offered the skills and learning that many Data Science roles require. Additionally, the flexibility meant I could continue with my teaching position and study part time."

After considering other options available, Paula decided to enrol and learn data science online with UC because of the flexibility, programme structure, and breadth of topics. 

"Compared to other courses I was applying for, the structure of a foundational year with the first 3 papers and then moving onto advanced data science courses was very appealing. This made the transition back into postgraduate learning much smoother, as I had been away from tertiary study for a significant period of time."

Through flexible nine-week data science courses, Paula developed skills in SQL, Python, R, data ethics and project management. After finishing the courses that make up the Postgraduate Diploma in Applied Data Science (but before she could start her master’s coursework) she secured a new role as a Data Analyst/Scientist at High Performance Sport New Zealand (HPSNZ).

“Without studying these courses, I would not have been able to pivot from a career in education into one in data analytics at HPSNZ,” says Paula.

Building practical data science skills for real-world use

"The fact that the programme focuses on applied data science meant that we used real world data sets and they were relevant to how people and businesses interact with data every day – this was a strong motivator for me to enrol as I wanted to learn how to apply my study to real world problems."

Alongside developing technical data science skills, Paula got a lot out of the wider topics included in the coursework.

"The technical papers definitely provided me with the ability and confidence to tackle any real-world data set and upskill as required for my role. Then having the opportunity to study data ethics and project management provided the breadth and depth needed to succeed in today's data science world."

The practical and ethical skills Paula developed throughout the programme gave her the confidence to step into her new role at High Performance Sport New Zealand (HPSNZ).

"The knowledge and understanding I gained from the Data Ethics course in particular has been fundamental to my understanding of the Privacy Information Act and how to navigate analysing athlete data appropriately and within the HPSNZ policy guidelines."

Her other highlights included growing her data visualisation skills, and learning more about the ethics of AI.  

“The integration of the ethical use of AI was an important skill to learn and truly relevant to current industry practice in data analytics. I discovered that my personal strength is creating data visualisations and being able to tell the story of the data. This is a key skill in my current role at HPSNZ." 

The reality of studying online while working

Balancing postgraduate study with full-time work wasn't always easy, but Paula says the flexibility of online learning made it possible.

"Studying online is not for the faint-hearted! You need to be very disciplined and plan for around 16- 20 hours a week to commit to the learning experience. But there is a lot of flexibility built into the programme, and if you're unsure, you can always start with 1 paper, or even take a term off."

She also found that the course facilitators understood the pressures faced by learners balancing study with full-time work and other commitments.

"The support from the staff was outstanding, they really understood the pressures the students were under with most of the cohort working full time. There was often opportunity to work ahead on modules to accommodate different personal schedules, and all the facilitators were happy to provide feedback on assignments before deadlines were met."

While people often worry about online study being lonely, Paula found simple ways to stay connected with her classmates.

"It can be quite isolating studying and learning independently, however this is balanced by the freedom to decide when and how to study to meet the various deadlines. I prioritised the weekly online tutorials, put my camera on and participated as much as possible.

"I also connected online with some of my classmates and even met up in person for a coffee! This made such a difference to the online learning experience; to create these personal connections where you could work through the challenges of assessments together and support and check in on one another."

Paula's advice for future data science learners

Learn data science online