How Jun Yen Tan Upskilled and Successfully Transitioned His Career Into Data Science After Sudden Redundancy
Jun Yen Tan truly felt the impact of covid-19 on his career and lifestyle when his career as a quantity surveyor was cut short. He started his e-commerce business and quickly discovered the strong need for Data Science is transforming the way small & big organisations operate. He felt a strong need to upskill and, after talking with an Institute of Data career advisor, he enrolled in our Australian Data Science & AI certification program in partnership with RMIT.
Within 3 months of full-time intensive training, he earned his dual certification and was equipped with the most relevant skills in today’s world. He worked closely with our career coaching and training team and has successfully landed a full-time position with RHB Bank as an Analytics Graduate Associate. We’re so proud of his progress so far!
Read about his inspiring journey here:
Can you describe your career or industry experience before making the change to data science & AI?
“I was a Quantity Surveyor (QS), working for a local interior design fit-out company, and also had a Bachelor’s Degree in Quantity Surveying. After working for about a year in this industry, the covid pandemic halted all my work activities. I had so much free time I created a small e-commerce business and also picked up something new. It was then that I learned about how data science is helping businesses solve day-to-day problems and thought it was a good solution for me at the time of running my e-commerce store.”
What were your career goals before upskilling to data? How has your outlook changed?
“When I joined the data science program, my career goals were a bit different. I wanted to utilise data to solve my day-to-day business problems. However, I ended up being so engrossed in developing machine learning and deep learning models that I wanted data science to become my full-time career instead.”
Why did you choose to join the full-time program in particular? What appealed to you?
“I knew data science was useful to me. It was a decision made at the time of running my e-commerce store. Hence, I wanted it to work out for my e-commerce store so much that I did not want to waste any time. I wanted to fully focus on the program and learn as much as I could. In the end, I was grateful for selecting the full-time program as I had not learned any programming languages prior to this course, and the full-time cohort allowed me to fully focus on learning Python.”
You’re now working with RHB Banking Group as an Analytics Graduate Associate – Congratulations! How did you get a job in the industry after completing your training and what guidance would you give someone applying for jobs after upskilling?
“The main thing that helped was utilising all the contacts that you have who are in the industry. Just put it out there the job you are looking for after upskilling. Many companies out there (big or small) are in the midst of restructuring their organisation or upskilling themselves in terms of technology. Whenever this happens, the company is always on the lookout for new talent. That was how I got into RHB as it is a local bank seeking to upskill in terms of a big data environment, and was lucky enough to be selected for a graduate associate program.
Furthermore, as more people are learning about the data industry, I think it is important to stand out during your application. This can be achieved through completing personal side projects; such as building a computer vision model, creating a new database using SQL, or even deploying a machine learning model on Streamlit. Another tip is to search for what sort of tools companies in your area are using and implementing those tools in your projects – such as Tableau, Docker, and more.”
Are you still using your previous skillset as well as your new data skills?
“Not really, as my previous skillset is for a whole different industry. Skills like multi-tasking, time management, and project management experience will be added on as time goes on no matter the industry you are in.”
What surprised you the most about your new job?
“The never-ending cycle of learning. While it is not a must, it definitely can be a huge factor in determining an expert in this area. Many tools out there are available in the data industry, especially in the big data platform. Hence, I am always learning on the job as my team and I are currently focused on building our bank’s big data platform.”
Can you describe when you knew that data science was the right career path for you?
“I was doing a side project on computer vision once, and the end result was so satisfying and brought me so much joy that I knew I wanted to continue building machine learning models and deep learning models.”
Tell us about your capstone project! How did you come up with your topic?
“My capstone project was about classifying ASL Hand Signs into English alphabets. It utilises a Convoluted Neural Network to classify which pattern of the pixel values arrays (ASL Hand Signs Images) belongs to which output (English alphabets).
Computer vision is one of the most interesting domains to me. The computer vision side project I mentioned before was a project I did before my capstone. I wanted to test out whether it was feasible to be my capstone.”
What did the process of preparing for your capstone project teach you?
“The main thing to focus on when preparing for or completing your capstone project would be to understand that it is an exercise for not just your trainers and yourself to understand what you have learned over the period of the accelerated program, but also to show the hiring managers from companies you apply to what you have learned and what you are capable of. Therefore, the main thing anyone should focus on is the ability to perform exploratory data analysis, data cleansing and transformation, feature engineering, building the model, hyperparameter tuning, and evaluation of your model.”
What kind of professionals would benefit from an accelerated training program like this?
“Anyone looking for a career change, or looking to implement data in their industry would benefit.”
What guidance would you give someone from a non-IT background that wants to upskill in data science?
“The very first thing anyone with a non-IT background can do is to learn Python, as it is the easiest programming language anyone can start out with. Learn the basics, create a few mini-projects, and by then they will be able to figure out if they truly want programming to be a part of their careers. Then get a cert, practice more and they will be on their way to receiving their first gig in the data industry.”
What do you enjoy the most about data science, data analytics, machine learning & AI?
“Python programming to me has been a really fun journey. In fact, I love it so much that I have been regretting not going into computer science or software engineering back in my university days. But like all things, it doesn’t mean that building a machine learning model or a deep learning model is an easy task. At times it can be frustrating not knowing why your model is underperforming, but after a series of troubleshooting and hyper-parameter tuning and getting your model to perform as you want it to, you’ll have a sense of relief, pride, joy, and satisfaction that will just send you over the moon, and fill your motivation to completing building more models.”
Connect with Jun Yen Tan on LinkedIn here.
If you are interested in up-skilling and transitioning your career to the data science field, schedule a consultation with a Data Industry Career Consultant today.