Koh Shao Ming has had an exciting career shift into a data analyst role just six months after finishing his Data Science & AI course with the Institute of Data under the TIPP (Tech Immersion and Placement Program), accredited with IMDA Singapore (Infocomm Media Development Authority). He was always determined to make his place in the data science industry, so he is very proud of his new role at Micron Technology.
As part of an intensive three-month training session, he was guided by industry experts who helped him develop in-demand skills to cater to the current market needs. After finishing his studies at the Institute of Data, he had a career consultation with one of our experts, which helped him perfect his approach to job hunting and understand what hiring managers look for.
We recently caught up with him to congratulate him on his success and learn more about what this new professional journey has been like for him.
1. What motivated you to upskill and pursue a Data Science & AI career?
My ideal career trajectory involves solving exciting problems. I am naturally a problem solver, so I always seek exciting challenges. Since graduating from Nanyang Technological University as a young mechanical engineer, I have been clear about pursuing a career in a data-related field that allows me to grow personally and professionally.
2. Why did you choose the Institute of Data among other course providers in Singapore?
I didn’t take a long time to decide to enrol in this course, and there were two reasons for that.
The other thing was that I could see the whole syllabus and all the education models on the website, so I knew we would cover all the essential topics.
3. You have mentioned how the course and learning style is ideal for learning Data Science & AI concepts. How did you discover your domain and specialisation?
I discovered that I was interested in pursuing an AI-specific career when I was job hunting. Still, it took a lot of work to break into the industry because the primary demand was for computer science graduates.
Since I had an engineering degree, I decided to pursue machine learning. I ended up starting a data analytics role and developing an interest in writing algorithms while doing technical tests.
4. How did the project executions and practical learning style of Institute of Data courses, including the Capstone project, help you with landing the entry-level position as a data analyst?
Portfolio projects are a big part of my resume. The Institute of Data helped me prepare some excellent portfolio pieces made with the tutors’ support and supervision.
Building my capstone project taught me so much about what it would be like to create something for a real-life client. For instance, I wanted to get into the healthcare industry, so for my project, I built an image classification tool that worked with CT scan images of lung cancer patients. The algorithm would scan submitted images and determine whether the patient had lung cancer.
I did this project because the healthcare industry is getting more advanced, and there is a real need for tools and software to automate the diagnosis process.
5. What was your experience like working with the trainer and your peers for the practical skills training?
I had a fantastic trainer during my course. The best part was that he had zero expectations that any of his students had prior knowledge of the subject.
He explained things in a straightforward, clear manner. Since the topics were new to us, he often gave an analogy or drew out concepts in an illustration to explain his thoughts.
I only had a little social communication with my peers during class to maintain the lesson flow. Most of our communication was through private messages. Still, if we raised a question, the trainer would put us in a break room to discuss and answer the question. During this opportunity, we would have the time to talk.
6. What was your journey with approaching job hunting and getting a career consultation with the Institute of Data?
She also guided me about the best strategies to tackle core interview questions. For example, the first question in an interview is always to tell them about yourself. The answer is crucial to setting your impression on the interviewer, and it’s your chance to tell your story.
I changed my process to give details about my background in mechanical engineering and why I want to pursue that specific role, as a data analyst, for instance.
7. What was your observation of the recruitment process and the technical test?
I submitted over 49 job applications during my recruitment process, out of which I had around 11 interviews. Companies are particular about who they hire since the job market is chaotic, with several people quitting. The prime focus is on more experienced candidates, followed by people like me who are just graduates. So, it could have been a smoother ride.
The start would be with interviews where they would look at the project on my resume and ask me questions about it. Other times, they would focus on understanding my approach to solving a machine learning algorithm like Decision Tree. For example, they could ask me why I used a particular metric to indicate a machine-learning algorithm’s performance. But, again, the focus would be on understanding my approach.
For instance, I had around four interviews with Grab, and they gave me two test assignments, which were quite challenging. I had to do a recursive algorithm as part of a technical interview from Grab. I had yet to learn it, but the hiring manager helped me with hints on how to approach it. I spent a lot of time studying that. In the end, they went with someone more experienced.
The technical test involved a lot of coding and Python, and I learned early on that I needed to work on my approach to tackling it right. Employers conduct these tests to understand your problem-solving activities.
8. Did our Data Science & AI course prepare you adequately for the role at Micron?
Absolutely! The Institute of Data has made me more comfortable with making decisions on the job. I have learned how to take a business problem and make it a data science problem before picking the right solution. This has helped me with my workload at Micron as I understand what the end-user wants and other minute details like choosing the right data visualisation dashboard.
Most of the time, we must help the end-user choose the right tool that best fits their needs since they don’t have the expertise or the data science background to pick the right one by themselves.
9. What has been your favourite part about the Data Science and AI program, and how did it improve your soft skills?
The best part about the program that helped me while job hunting was the references and pre-done code files that our trainer put together. For instance, if I wanted to learn about keyword progression, there would be a particular file they could share with me. Because we had that reference, it was easier to execute in practice.
I learned a lot about project presentation skills from my peers who came from a business and accounting background. Their way of putting together presentations was quite neat and looked pleasant.
10. Why did you apply for the data analyst role at Micron?
Micron was the 44th job application that I sent out. I was starting to get a little desperate towards the end of my job-hunting phase, and I would fill out applications for any vacancies I saw. This increased the number of weekly interviews, making the process quite tiring.
However, with each company turning me down, I learned something new. I became more confident about my skills and what I should apply for. It was necessary and taught me what the recruiters are looking for and what skills can help me land a job. As a result, I edged more toward data analytics. I started to apply for more data analytics roles, which led me to Micron.
11. How did you get hired by Micron Technology?
When I saw a job opening for a data analyst role, I was excited because it was the engineering skill set I had. Most of my colleagues were working on manufacturing and memory-based solutions. Micron was heavily dependent on machinery because it was fully automated. Every machine has a database that tells you how well that particular piece performs. Seeing data science and engineering mixed in a job role was intriguing.
I had three interviews for the overall recruitment process, and I was so impressed with my manager in the first one that I knew I had to secure this job. The second and third interviews were with the senior manager and director.
Most of my job responsibilities tie into my engineering background. I understand how the company comes together and how different stakeholders, like on-site engineers, work. I use Tableau to provide data visualisation for company stakeholders so they can see how well the processes and machinery are performing in real-time.
12. Did your salary package meet your expectations?
It exceeded my expectations, and while there’s no room for negotiation, the company pays very high, even for entry-level jobs.
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 Koh Shao Ming and follow his journey on LinkedIn.