From Analysing Data to Creating Seamless Systems: Reed’s Journey into Data Science

From analysing data to creating seamless systems: Reed’s journey into data science

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Reed Iredale has forged a unique path in life and career.

Reed has always been interested in numbers and originally trained as an accountant. Accumulating a rich understanding of marketing and design in a range of opportunities that followed, Reed was drawn to further cement his analytical abilities by transitioning into data science.

As a result, Reed offers a unique set of skills. These skills, along with his pioneering attitude, have really paid off, landing him a conversion rate optimisation (CRO) manager role.

We caught up with Reed to hear how he’s finding his CRO role and how the Institute of Data’s Data Science & AI program played a pivotal part in his success.

1. Tell me about yourself, Reed: Where are you from, and what are your interests?

I grew up in Byron Bay, one of the big tourist towns of Australia. It’s a small place, with limited opportunities job-wise. I was partway through an accounting MBA when I quit and moved to Brisbane to pursue my career.

At that stage, I could do a little bit of CSS styling code, and I was building small-scale websites.

In Brisbane, I soon moved into working in marketing agencies and, from there, into product design. I learned how to run my own business and do really high-level product design.

2. Could you provide a summary of your career prior to starting the course?

For the three years leading up to when I decided on the data science & AI program, I was working in high-end consulting. My main driver was growing business outcomes from a design point of view. I was using a lot of qualitative data and a lot of quantitative data. I am very analytical, and I was basing my decisions on the data. What I lacked, though, was a deeper level of understanding in maths.

The bigger modules in the data science & AI program really took my skills to an extra level. I found the program allowed me to back up my claims and approach with data. Doing the program gave me the tools to articulate this with confidence.

When I was looking at other programs and courses, I realised that I wanted something that had real depth, something that offered data science and artificial intelligence. That’s what shaped my decision.

Data science starts and ends in artificial intelligence.

I searched around and saw that the Royal Melbourne Institute of Technology (RMIT) and the University of Technology Sydney (UTS) were behind the data science & AI program offered by the Institute of Data. I felt that insurance that I was going to get a good business outcome by going with a program backed by a good university.

I’m happy to invest in something that’s going to give me a good outcome. That’s where I invested my money, and I was happy to do so.

3. How did you find the Institute of Data program?

I read a really good quote in David C. Baker’s book, The Business of Expertise. He said you can’t be a very strong specialist in the fields of science and engineering if you don’t have a deep focus.

My focus on design and quantitative data was where I wanted my data science program to get me to.

I already had marketing-based expertise that I wanted to expand on. I pushed for a strong emphasis on how data science impacts marketing, ultimately leading me to the data science & AI program.

I loved the structure. The structure to me felt like modern education, with real-world experiences and very good theoretical use cases.

When people are introduced to the idea of a course in data science & AI, it’s assumed it will be heavily maths-based. Maths is only one part of it, so it isn’t overbearing. The data is playful, the technology isn’t overly burdensome, and it was taught really well.

It’s a good mix to get you job-ready. That’s what I found with the program overall. There wasn’t any part I’d lop off.

4. Did you utilise any of the Institute of Data’s additional resources? Were they of value to you?

Because I had a very business-minded focus going into the program, and I had particular marketing data that I wanted to specialise in, I had a certain set of outcomes I wanted from the program.

My one-on-ones with Amin were very professional. His deep, real-world knowledge of AI was just brilliant. He brought real-world examples, which produced real-world outcomes. I’m super thankful for his wisdom and his expertise.

I had so many great, in-depth discussions about high-scale and high-end data science projects he’d been involved with. That’s invaluable!

I’m also grateful to Ricky, who kept me grounded in terms of what was actually going to happen when you get into a job. Ricky was just great at explaining how real employees buy data expertise.

Ricky’s hands-on technology skills and his know-how in implementing projects were amazing.
Vincent Guam, too, he was just lovely. Super helpful and always very professional and kept everyone in check in terms of being inclusive and getting everybody excited and into their projects.

5. Can you share the techniques or methods you employed to complete the course successfully?

The thing that got me through the course was that idea of specialisation. I would recommend to anyone considering the program to be open to niching. Narrowing down makes data science and learning data science incredibly powerful and so much easier.

Specialising really helped me focus. Finding a niche and applying real-world problems made learning data science incredibly easy for me.

I think of data science now as the side-car to a motorbike – it’s just this brilliant tool to have. It’s a skill set on the side that makes things so much easier. My focus was so clear.


6. Tell us about your capstone project.

I’m big into pricing. So I’m really interested in how that works from a marketing and behavioural economics point of view. I was initially going to do my project on price elasticity of demand, but there was a lot of complexity with the code I was starting off with, so I went down another path of insurance products with marketing engagement prediction modelling.

Essentially, it was to predict if we could get re-engagement of our current customer base. The project was around determining which clients would be the most suitable to re-engage with and receive a budget allocation based on their potential as ideal customers.

By looking at the decision-making steps customers make, I was able to determine who’s actually going to engage and who will continually engage with you in your marketing. When I was deciding on what to do, I looked into lots of marketing data science ideas and found one data set that worked really well.

7. Have you applied the knowledge you gained from the Institute of Data’s program to enhance your professional development?

The Institute of Data encourages you just to keep going. Data science is a really good industry to get into. It’s a really popular industry. They reinforce that you won’t be the only one going for a job.

The Institute of Data really shaped me. They didn’t sugarcoat the process. They basically said: be ready, be as good as you can be. Put your best foot forward, and keep going.

When I first got my current job, I found the earlier modules of the course really shaped me for this role. Having the Institute of Data on my resume made me a far better candidate. They were interested in what I could bring beyond analytics.

I had to be a bit of a unicorn: designer, data scientist, and to know behavioural economics.

I’ve utilised so much of my coursework in my day-to-day. I use Python and SQL as sidekicks of my own reporting. Those two tools make up the majority of the course. I have a strong track record in utilising these tools to achieve cost-effective victories.

8. Do you have any advice for individuals who are looking to apply for jobs after completing their studies?

If you’re coming into data science, stick to the three big areas – finance, health or technology – they’re the most lucrative. Next, look at one to three companies you want to work for.

If you focus on 50 companies, you end up just shallow-dumping a resume like everyone else. If you focus on three: find their hiring managers, talk to them, find the people below them, find the people above them and just build relationships, you have a much better chance.

I think the program suits someone who is numbers-inclined and interested in business from data science point of view.

In terms of personality, someone who is happy to sit with a number problem for a long time. You have to be able to sit and work with a problem.

I really want to emphasise finding a niche. I think it’s incredible to go into this course when you have ideas or projects that you want to test the data early on.


If you are looking for a career transition into tech and pursuing your dream job, book a career consultation with one of our experts at the Institute of Data and start your journey with an actionable plan.

You can connect with Reed and follow his professional journey on LinkedIn.

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