Jack Sticklen was previously working as a process engineering risk specialist within the government sector, but wasn’t really sure whether it was the right role for his long-term career trajectory. Jack came to realise that there wasn’t any real passion for the work he was doing – however interesting it might’ve been. This led him to explore different alternatives, and once he’d found data science, he felt that was the perfect pathway for him. Even though he had no prior IT experience, he was dedicated to changing careers.
While Jack was studying our part-time data science & AI program with the University of Technology Sydney (UTS), he was able to secure a role at Deloitte as a data analyst before he’d even graduated. Congratulations, Jack! Read more about his journey below:
1. You have a background in chemical and biological engineering! What motivated you to pursue a career in data science and AI?
I took a step back from my current career trajectory and asked myself – do I want to be doing this stuff for the next 40 years? Do I find it meaningful? And the answer was no, of course.
2. What was the most challenging aspect of completing the course? Which aspect of the course did you enjoy the most?
The part of the course that was the biggest curveball was probably the projects – finding your own data and integrating it with the course teachings is a lot harder than dealing with data that’s been chosen with a solution in mind. Ironically, this was also the part of the course I enjoyed the most – it’s rewarding when you come through with your own insights and conclusions.
3. How did you manage to land a new role before graduating from the program? How did the job outcome program support you when you commenced the course?
I used the initial sessions of the job outcome program which started prior to the course finishing, to revamp my resume, cover letter, and application approach, and then just started applying. My focus was on entry-level roles, and especially on roles where my past experience might add some value on top of what I learnt in the course.
4. Tell us about your current data science role, what does your day-to-day involve? What tools/techniques/processes do you use?
My current role is as a data analyst with the risk advisory business unit at Deloitte. The day-to-day involves using core tools such as SQL, Python, Excel, and Power BI to gain insights into client data and practices, both to advise them on their obligations and options within their regulatory space, as well as to help build out systems and capabilities for them.
5. How does your new data science skillset assist you in this new role?
6. You actually started the course with no IT background. What advice would you give to professionals that don’t necessarily have existing data experience but want to upskill or have a career change to data science?
My advice would be: Data science is a domain where you don’t need to be a whiz at programming or understanding integrated systems at a detailed level. If you can get a grip on the basic concepts and syntax of your programming language, and understand what the core tools and libraries can do for you, then you’re already halfway there.
7. How did you find the pre-course work? What topics did it cover? How many hours did you have to spend on it approximately prior to the course commencement?
I found the pre-course work pretty essential to the course, especially if you’ve got no programming background like I did. It mostly covered the basics of the Python language and some core data science libraries, probably taking about 24 hours of total time depending on how slowly you take it.
8. Tell us about your capstone project! How did you come up with your topic?
My capstone project was a bit off the beaten path – I wanted to do something a little novel in my domain of interest, to push the boundaries a bit. I performed sentiment analysis (a type of machine learning where a tweet is assigned a positive or negative emotional sentiment) on a very large Twitter dataset, with a twist: I wanted to test the efficacy of a unique method of generating training data for the model to “learn” on, since it’s not always easy to manually label data for training in the real world. I came up with the idea by looking through some of the literature on the subject, and extrapolating from one underdeveloped study that employed the method, but didn’t really verify its performance vs a baseline – which is what I did in my capstone. Suffice it to say, it didn’t perform very well in the end, but as they say, every failure is a lesson learnt!
9. How did you find the process of completing your final capstone project during the course? What did it teach you?
I think the final capstone really drove home that actually doing the machine learning is 10% of the work – most of the work is in actually finding the data you need, and wrangling it into a form that’s ready for analysis. And often, in the real world, the data you need may not yet exist – you need to go out and find it for yourself.
10. Tell us about the support that you received from the trainers in the program.
Our lead trainer was very enthusiastic – I believe he’d previously transitioned from another career into data science years ago, so he was well-positioned for this kind of course. He was always willing to hear feedback and alter the course delivery to suit our needs and provided plenty of supplementary resources, but at the same time he wasn’t the best at answering questions – he had a tendency to answer the question he heard, rather than the one you asked. This is where our tutor came in – he always made himself available and was very good at answering the questions we had about all the little issues and problems encountered on our journey.
To find out how you can upskill in data science & AI like Jack, book a course consultation here today!