Koo Ping Shung
In today’s writtencast, I am joined by Koo Ping Shung, a LinkedIn Top Voice, the President of the AI Professionals Association, and co-founder of Data Science Rex Ltd.?
Thank you, Koo, for taking the time. Tell us about your work at AI Professionals Association.?
I am the President and Co-founder of the AI Professionals Association, AIP. Our mission is to provide tools to assist passionate individuals and organizations in using Artificial Intelligence effectively, efficiently, and, more importantly, ethically.?
To kick things off, we have the “Chartered AI Engineer,” CAIE, in short.? A Professional Qualification program where we hope to bridge the talent gap in data science. What gap is that? Talents have challenges understanding whether their skill sets are enough to start working on Data Science or Artificial Intelligence. In contrast, businesses have challenges understanding the skill set needed, plus whether the candidate sitting in front of them has all the technical skills required to implement an AI commercial-level project.
Talents who sit for and attain the Chartered AI Engineer qualification signal the potential employer that they have adequate knowledge and skills.
For more information on CAIE, they can check out this webpage: https://www.aip.org.sg/caie/?
How did you get into the data science field??
Back in my university days, all I knew was I wanted analysis work because I like work that requires critical thinking. In the early 2000s, there was no such thing as “Data Science,” so I took Economics and Mathematics because I felt they complemented each other. It also required looking at data and analysis.
However, I graduated during the SARS period. Hence, things were tough for at least 18 months till my friend introduced me to a research center on pedagogy. That kickstarted my “data science” career because I got to use statistical models for analysis in the research center and saw a lot of potential in the business field.?
And on my own, I started reading up on it, and I knew it was a field I loved and wanted to venture in.
Tell us about your work applying data science to smart cities.?
I like the concept of a smart city, as defined by using data to build a sustainable city, physically and mentally sustainable. That got me thinking about what kind of data to collect and how the data can be used. Besides understanding how data works, my Economics background also came into the picture in reading and researching Smart Cities. We live in a capitalist society; therefore, dollars and cents must be considered to be “sustainable.”
I also want to point out a blindspot in how most people view Smart Cities, which is human well-being. Most articles discussing Smart Cities are about managing climate change and being green, but we should pay attention to what is at the center of all, humanity. And being in data science and artificial intelligence, I felt there was a lot of potential.
However, I have more theoretical knowledge and hope to have a platform to realize some of my theories and turn them into applications.
What is the importance of communities in building a smart nation??
To run a Smart Nation, talent is part of the equation. We are moving into a lifelong learning paradigm. Classroom learning will be one of the many learning methods. Another method to learn, which I strongly believe in, is collaborative learning, i.e., helping and learning from each other. This is where communities come into the picture.?
Communities provide economies of scale, networking opportunities and knowledge, and sharing opportunities that supports the life-long learning paradigm. It can create a virtuous cycle of learning and growing the talent pool needed to build, run and maintain a smart nation.?
Tell us about your work with data science and its intersection with cybersecurity.?
Cybersecurity is something I’ll like to read more about if I have more time. I believe in this day and age, where a lot of our assets are digital, online security is essential. AI is a tool that can allow efforts to scale up easily, I am sure black hats will use it to their advantage. Given the nature of security, where white hats have more space to defend and black hats have the AI tool to rapidly poke for security loopholes, they also have to adopt AI quickly.
That is why I would love to see the intersection of data science and AI with cybersecurity to see where are the potential use cases.
You have done an impressive job training and have high ratings. How did you get into this work, and what do you enjoy most??
I was given an opportunity to train people for a software company, which kickstarted other opportunities, given the huge demand for data analytics and data science skills.?
I got to train in presentation and communication skills as I did the training. I also saw the positive impact that I had made on the participants as some of them do spend the time to write a compliment/feedback to me. As the participants ask questions, I understand their different perspectives, allowing me to think more thoroughly about my explanation. This thinking process led to my understanding of the concepts better. So it's a virtuous cycle. As I help the participants to understand and apply the concept, I get to see the concept from many perspectives, which gives me a clearer picture of the concepts discussed.?
How have you found an MBA degree useful in your data science and machine learning work?
Definitely! Data science and machine learning is never about just data and maths alone. We also have to look at the business aspect, such as revenue model, business processes, target market, sales and marketing, accounting procedures, etc. The MBA degree helps me with that, and it clears any doubt that my client or target audience may have about my ability to communicate business value from the insights gained from mathematical models.?
You have worked with numerous businesses to help them take advantage of their data. What are some common challenges you have seen from your decade-plus of experience??
Most companies only start looking at the “sexy” part of the whole value chain, i.e., focusing too much on the algorithms but overlooking data quality. Many people can quote the mantra, “Rubbish In, Rubbish Out,” but how many businesses pay attention to how data is collected to ensure that data is collected at the highest quality possible? So data quality is one of the common challenges that can be easily overcome with planning.
Another challenge is that data and its related tech are still very new, and thus there is a lot of misunderstanding on what it can or cannot do. Education then needs to be done on, and it takes time.
You help companies with their data strategies. What does that involve??
I help companies to build up data capabilities. It involves scoping out relevant use cases, the proposal on data collection and management, training and mentoring of talents, and then advising on implementing use cases, especially deploying the insights and models in business processes.
What strategies did you use to grow the DataScience.sg community to over 10K members??
Always focused on gathering resources and giving the community what they want, ensuring that the community is not exploited for anyone’s agenda.?
You have grown your brand on LinkedIn and through other engagements such as TV appearances and facilitating a Bootcamp organized by the World Bank. How important is building a personal brand in tech??
Branding is a mind shortcut at the end of the day. A good brand helps one build up and gain credibility quickly in other people's minds. This is especially important in one’s career because a good brand attracts opportunities.
Tell us about your work at Data Science Rex.?
At DSR, I am a trainer, consultant, and mentor. I help companies build data capabilities through training, consulting, and mentoring.
Consulting comes in first to help define useful use cases in the companies. This is followed by training to get client employees trained to take up the use cases. Last but not least, we have mentoring to integrate the lessons from training into the use cases so that the client can continue to maintain the use cases/projects.?
What common myths about data science & artificial intelligence have you encountered in your work??
There are a lot of myths and misunderstanding about what data science and artificial intelligence is. Many share on this topic too. If you want can follow my podcast episodes on them.?
Part 1
https://podcasts.google.com/feed/aHR0cHM6Ly9hbmNob3IuZm0vcy8xZGZhYmM2Yy9wb2RjYXN0L3Jzcw/episode/OTllNTdkYWMtNTZmYi00NzM4LTgwMzAtNzY4ZDk4OWE3OGZl
Part 2
More importantly, many out there claim to be experts, but if you look at their background and experience carefully, they might not be what they claim to be, so for all readers, always be critical of what is shared, even on my own posts and content. Process and think through them carefully. :)
You have written numerous publications. How important is the writing skill in data science??
I see writing as a form of communication. Compared to speaking, it has its nuances to pay attention to. In data science, communication is essential, and thus writing is an important skill to have in data science.
What would you advise someone trying to penetrate the data science space but not knowing where to start??
Follow people on Linkedin that tend to share on the topic of getting started and building a career in Data Science and Artificial Intelligence, including me. But, be critical of what is shared and consider whether it is true, logical, and workable.?
I have written a lot on the topic of breaking into Data Science, so feel free to check out my blog for more information: https://koopingshung.com/
Here are a few posts to start:?
I hope these are a good start, and feel free to come onto LinkedIn to share your feedback with me. :)?
Where can people find you online??
People can find me at the following:
Blog: Koopingshung.com
YouTube Channel: https://youtube.com/channel/UCRRmaDLJsLQfAQa0lgoC9dw
Product | Data | AI
1 年Lovely
Growing the Best AI/ML Audiences on LinkedIn | ML Engineer with a Plot Twist
1 年great convo!