The Institute of Data caught up with Byron 6 months after completing the Data Science Full-Time Program*. Here’s what he had to say:
Can you explain your career before making the change to data science?
“Mostly a varied IT career with the last 10 years being in part-business and in part-IT related. My educational background was a computer science and operations research major. I had been working in my previous role for 10 years.”
Why did you choose to change your career to data science?
“Two main reasons:
1. It seemed more interesting than purely IT programming and project management roles; and,
2. I could see the supply demand was a much better equation in terms of many companies wanting it, and not nearly as many people having qualifications to satisfy the demand.”
Why did you choose to join the Data Science Full-Time Program?
“To be honest, the Institute of Data found me. So they were obviously looking on LinkedIn and other places for people with the right background and were looking for a change. They approached me, and at first, I thought it probably wasn’t worth pursuing. Then with a little more interaction, I decided it was a good idea.”
How did you find the full-time training program?
“Generally, it was very good. They covered a lot in a short period of time. For a 3-month course, we did very little wasting of time. We basically were taught exercises all the way through. From a practical point of view, the course covered a lot of ground.”
How did you get into the industry after completing your training?
“The Institute of Data found an internship for me at a company called GoCatch, an Australian version of Uber. I worked there for 3 weeks as an intern, and they were happy with what I did. At first, they offered me a contract position. I’m now in a full-time position as a Data Scientist.”
Tell us about what you’re doing in your current role.
“I’ve done some passenger lifetime value analysis and passenger churn analysis. I look at the normal timeframe for people to drop in and out of the system and also spending patterns of users to work out if they are profitable jobs or not.
I also do a lot of mini queries – some planned, some ad hoc – responding to management’s needs. I’m constantly pulling out metrics and reporting on them. I’ve implemented automation technology using python code for many of the reports. The reports are the main go-to tool for management to analyse where the business is going.”
How has your career progressed since entering the industry?
“I guess I knew a little bit of everything to start, emphasis on little. Now in terms of data analysis, I know a lot and can do it quickly. I understand complex tables with many relationships. Over time, I’ve been understanding how data can influence business decisions. Also, how good data and bad data is very important to base decisions on. Management is now very focused on using data to make decisions and not just going with gut feel.”
Now that you have worked for 6-months in the industry, what’s your future career plan?
“My future career plan is to progress the way I’ve already been progressing. I want to focus on machine learning and artificial intelligence as I progress. We certainly have some interesting projects lined up next year where we are going to do some dynamic pricing, and meet the supply and demand curve based on our analysis of the data.”
How do you compare your career situation now to 12 months previously?
“Twelve months ago, I was a bit depressed about everything. I would apply for jobs and wouldn’t get a response for anything, let alone an interview. Whereas, now I can see the demand is high and there are always people looking at my LinkedIn profile and contacting me, so I’m not actively seeking them out. I’m often being approached by recruiters.”