PLUS-Skilling Not Re-Skilling Needs To Be the New Norm
AI Singapore runs a very successful apprenticeship programme – the AI Apprenticeship Programme (AIAP). Started in 2018, the AIAP is now into its 6th batch of apprentices. More than 90% of our graduates secure an AI job before they complete the 9-month programme.
More information about the AIAP programme can be found below.
In short, we take in self-directed, highly motivated learners who have taught themselves the tools of the trade like Python, AI/ML libraries either through formal courses in the universities, online materials, MOOCs, or just from books and personal experimentation. We then take them on a 9-month journey to deepen their skills to build and deploy an AI model into production for a real-life “paying” customer.
Who are the Apprentices?
After 2 years of studying the trends and profiles of the apprentices, what is apparent is that the field of data science, AI/ML – is not just the domain of computer science graduates. In fact, computer science graduates make up only 21% of our cohort. Yes, you can say those with a computer science degree are less likely to join the AIAP since they have the background knowledge already, and you would be partly right.
However, the work of the data scientist and AI Engineer is not just about the development of novel machine learning or AI algorithms (we leave that to the researchers!). Data Scientists and AI Engineers are passionate about creating data products that provide the organization they work for with useful insights and actionable plans and/or products and services. The people who excel here are passionate about understanding the data and creating products, and the use of any AI/ML tool – is just that – a tool or technique – that is to be used as required.
The chart above shows the profile of our apprentices from batch 1 to 6. What is clear from the data is that people from any field, with the right motivation and training, can get into the AI field and do very well (all of our AIAP graduates have landed data science/AI/ML roles with major private and public organizations).
PLUS-skill and not Re-skill
What is interesting in speaking with the current apprentices and maintaining a relationship with past AIAP graduates and tracking their progress, is that it was never about re-skilling (because they found their skills irrelevant or their industry disappeared) but about getting new skills which would allow them to perform better in their chosen profession. Many AIAP graduates continued in a similar profession after the AIAP, but now in an enhanced role with their new-found and validated data science and AI skills.
In PLUS-skilling, an accountant learns how to use data science and AI tools and techniques to analyse the accounts faster or detect fraud. Some accountants may learn to code and build those AI models themselves, but they will be a minority and will be highly sought after.
A sales executive, who now understands AI and is able to build AI models, would be well-positioned to sell AI products and services intelligently and correctly. (I have lost count of the number of salespeople whom I have met trying to sell me AI products without understanding how their products actually work).
A software engineer, who is trained in machine learning, can now build more intelligent systems in a robust and correct manner. It is not just about learning how to call another API. Building machine learning systems involves the correct use of data and interpretation of the output of various ML algorithms. Garbage into an API, garbage out of the API.
A mechanical engineer, is now expected to design smart systems which use data to drive the design, and not necessarily from first principles or physics equations anymore. The engineer who is able to effectively use AI tools or build machine learning models will be the new norm.
A government policy planner, will now use her new data science skills to develop better policies with data instead of just gut feel, the popular vote, or based on often biased published reports.
The AIAP is for professionals who have that passion for data, the ability to code and want to become full-fledged AI engineers to develop AI software, products and solutions. Creating a nation of AI Engineers is just not possible and is not wise.
So you may ask
if I cannot code, how can I participate in the new world of AI and data?
Fallacy of programming for everyone
Do not learn programming!
Not everyone needs to learn programming to be data-savvy and do data science and AI roles. You do not need to know how to code even “Hello World” to be part of the data revolution.
5-day bootcamps which offer HTML and CSS coding and make you the promise of landing you a job as a web designer is just so wrong. In 5 days you cannot even hope to build a website which can be generated with tools like WordPress, Wix or Squarespace in an hour. You will need months, if not years, of training and hands-on work to become a “real” web-designer.
3-week bootcamps which claim to train you to be a data scientist is just fake news! That is a story for another day.
Working professionals will encounter more and more AI tools and systems in the next few years. Learning how to use them will be part-and-parcel of the job (just like how you learn to use Excel or switch from Lotus 1-2-3 to Excel).
Tools like Azure Automated Machine Learning, Orange Data Mining and Rattle (package in R) allow you to click through an analysis and perform machine/data mining without coding.
What you will need to learn here is not programming, but data analytics skills, understanding how to use the correct algorithm for the specific use case, and how to pre-process the data, analyse the output and then make a recommendation to management based on your analysis. Or you can view terabytes of data and present them intelligently with beautiful visualisations without a single line of code with highly intuitive GUI-based tools like Tableau, Qlik or PowerBI, etc.
It is about learning beyond Y = MX + C (and who said you do not use data science!) and newer, more advanced algorithms combined with the data you have and domain expertise to deliver value to your organization. Just as most of us who need to do forecasting may have used Excel’s FORECAST() function at one time, you did not need to learn how to programme the FORECAST function to use it. You only had to learn there was such a tool and HOW TO USE IT correctly.
The push of getting everyone to learn programming is not only ineffective but also a waste of time and resources. Not everyone is cut out to code, just like not anyone can be a lawyer, painter or doctor. Programming is an art, and you need to have that artist in you before you can do well.
If you are a PMET, and think you want to learn Python programming in 3-months and transition into a data science role, where do you stand compared to fresh graduates who may have spent 3-4 years in a University programming every week, coding and working on data-related problems? Would an employer hire you instead of a fresh graduate if you wanted that entry-level programming role?
But if you PLUS-skill yourself and present yourself as someone who has the domain knowledge and now with proper data analysis skills who can use common tools mentioned above to better drive an organisation’s strategy, you will likely be the manager of that fresh graduate who would be doing all the lower-level programming to get the right data to you for your analysis.
Everyone is (or will be) an expert in their chosen domain of profession after a few years and the focus should be on PLUS-skilling so that you can take on new tasks in the new age of data, data science and machine learning.
Happy learning (and not necessarily programming!).