How Sakina Sakdun Upskilled in 12 Weeks and Transitioned Into a Data Science Role in Singapore

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Through the Institute of Data’s Data Science and AI Singapore Program – Sakina Sakdun future-proofed her career and successfully transitioned into the data science profession by gaining in-demand data skills – quickly progressing her role with Quandoo!

During her experience as a Project Manager with Quandoo, Sakina noticed a growing shift towards data-driven processes and decided to be proactive and adapt to changing business needs. She pursued our Data Science and AI Full-time Program in Singapore to expand her skillset and in just 12 weeks, Sakina was mentored and trained by industry experts and was able to apply what she was learning to facilitate data-driven decision making on the job. This is her journey so far:


What was your career experience before making the change to data science & AI?

“Before deciding to take the leap into data science, I was a Project Manager for about 3 years. My responsibility back then was to effectively liaise with people from all levels to ensure successful and timely execution of projects – be it upper management, or across different departments. As my company has become more data-driven, any business strategies or decisions have had to be backed by data (as opposed to personal opinions/feelings). Quality tracking of performance and success metrics have also become increasingly important. Therefore, this led me to work more closely with the Business Intelligence team where I witnessed the power of machine learning, and that sparked my interest in data science.”

Why did you choose to join the Full-Time Program in particular?

“I knew that I wanted a future career in data science and to have a competitive advantage in the job market, I wanted to obtain new hard skills (i.e. to be able to code in Python and build machine learning models). When I came across IOD’s Data Science & AI course syllabus, what appealed to me was the breadth of topics that would be covered in a relatively short period of time (12 weeks). I also needed a live classroom environment where I was able to ask questions when in doubt instead of one-way communication, like in a typical online course. IOD’s live online learning environment allowed me to engage with my instructor in real-time, which enhanced my training experience.

Due to the steep learning curve, I also felt that it might be too much to juggle work and study at the same time. So I negotiated with my managers about this course and they kindly gave me some time off from work to focus solely on learning new skills. For this, I’m very grateful to have supportive managers who appreciate the value of data science for the business.” 

How has your outlook changed after upskilling to data science?

“I’ve always found passion in solving problems and making things more efficient. Now with the knowledge of data science, it feels like I’m equipped with a powerful tool that allows me to solve problems in a much more effective way.”

You’re now transitioning your role from Project Manager to Business Intelligence Analyst – Congratulations! How did you get a job in the industry after completing your training?

“I feel lucky because the timing of it all could not have been more opportune. During the time of negotiations with my managers regarding this course, it just so happened that there was an open position of BI Analyst in our company.

From there, I made the decision to switch from the Supply department to the BI department within the same company because I felt that was where I could thrive and be exposed to learning from other colleagues in the same field.”

How do you compare your career situation now to 6-12 months ago?

“Upskilling to data science has opened up more doors for me in terms of career advancement. This program has not only provided me with a good solid foundation to kickstart my data science career, but it also instilled the curiosity in me to learn many new topics on my own (i.e. new algorithms / techniques, software, etc). In the past, my go-to tool to analyse any data was Google sheets, but now I feel more sophisticated.

What is one thing you know now that you wish you knew before changing careers to data?

“I underestimated the foundational knowledge of mathematics / statistics needed to really understand the algorithms behind each machine learning model – luckily the pre-work provided can build up your foundational knowledge before the classes start. Even though I’ve graduated with BSc in Mathematics and Economics, it had been a while since I touched on mathematical theorems and concepts. So throughout the course, I came across things like bias, variance, eigenvalues, chain-rule, t-test – which I’d forgotten about and needed to revise. However, as long as you are determined to build your skills – you can learn and revise the level of mathematics / statistics knowledge needed.”

Tell us about your Capstone Project! How did you come up with your topic?

“Since my company has been working on the area of customer retention, I wanted to explore how machine learning captures high value customers who are at risk of churning. I also wanted to challenge myself by wrangling more than a million rows of events data into a clean customer-level view and from there, do an unsupervised clustering modelling to find the “high-value, high-risk” customers.”

How did you find the process of completing your final Capstone Project during the course?

“I did struggle a bit initially. Unlike supervised learning methods (e.g. predicting a number), unsupervised learning is less straightforward and a lot of trial and error is needed to get even one model that works. I took it as a learning experience, hence for every model that did not work out, I wrote down the learning points and made further improvements until I got it right.

All in all, the biggest learning point for me was the importance of recognising the nature of the data (in my case it was extremely skewed) and how that affected my final results. It boiled down to my core understanding of statistical concepts like bias and normal distribution, and to implement the appropriate data transformation techniques even before modelling.”

Now that you’re trained with in-demand skills and working in the industry, what’s your future career plan?

“The challenge now is to know how to correctly apply the things that I’ve learnt in the course to real-life business scenarios, which are often more complex and dynamic. But overall, I feel very excited to take on the challenges!”

What kind of professional would be suited to and benefit from an accelerated training program like this?

“Call me biased, but I really do feel like a background knowledge in mathematics and having professional experience in project management would definitely be helpful. To be a good data scientist, you have to have both hard and soft skills at hand.

Having a background in mathematics will shorten your learning curve when it comes to understanding the complexities (or “black-box”) behind each algorithm, to recognise the nature of your data, and to apply the most appropriate machine learning techniques. But again, completing the pre-work provided will enable you to build these basic skills before the course begins. At the same time, your communication and organisational skills are most useful on the job when it comes to presenting your ideas and convincing stakeholders that your approach is the best while also managing their expectations.”

Read: Learning how to learn data science

Read: Project management and data science

What did you enjoy the most about learning data science, data analytics, machine learning & AI?

“I really enjoyed learning with all my passionate and hardworking classmates throughout the entire 12 weeks. The trainers have also been amazing in guiding me and deepening my interest in data science. I just love having a strong support system that shares the same passion and walks the same path as me.”

What guidance would you give someone from a non-IT background that wants to upskill to data science?

“Firstly, you must know whether or not this is what you want to do. While data science and artificial intelligence may seem like a “cool” thing to do, ask yourself if you are mentally prepared to put in the effort, time and discipline. You will be learning about the practical applications of python, mathematics / statistics for data science, analytics and machine learning within a short time frame, and also have daily lab work and projects – it’s a lot of hard work but it will pay off if you are focused on upskilling and making a career change.

For me, I knew from the beginning that this is what I want to do, therefore I enjoyed every minute of it and I’m now able to see that the rewards are beyond my expectations!”

Connect with Sakina on LinkedIn here.

If you are interested in up-skilling and transitioning your career to the Data Science field in Singapore, schedule a consultation with a Data Industry Career Consultant today.

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