Changing the World Through Coding – Webinar on Why AI & Big Data are Skills for Future Jobs

Firoze M Zahidur Rahman, CEO and Chief Solutions Architect at Loosely Coupled Technologies,  speaks with Prof. Vinesh Thiruchelvam Chief Innovation Officer at APU and also deputy Vice Chancellor of APU, on Live Webinar Session hosted by Asia Pacific University, moderated by Ryan Tan. Watch the full webinar session here.

Data science, AI, & the future: 

Why are AI and Big Data skills for future jobs? Like it or not, AI & big data is the future replacing many jobs and it’s important for people to embrace it. AI is not new, but everyday it’s advancing. So keeping up can be a challenging task. But jobs are abundant in these areas. What’s in the future of this industry will be discussed in the webinar.

Prof Vinesh says, “AI is a feeder, an enabler to the wants and needs of the new generation, new millennium, coming to APU. But challenging, changing by the day. 

Do you agree, Mr. Firoze, that AI is about the wants and needs of the new generation?

“Back in 2007 or 2008, we watched the movie Iron Man for the first time and JARVIS was this AI assistant helping him through everything. And now in the real world, you have Alexa, you have Siri doing a similar job. In terms of AI. we’re in that pathway already. What you see in movies and tv as science fiction is now becoming reality,” says Mr. Firoze. 

Overarching of AI

“AI is an overarching representation, overarching the current trends in terms of enablers in the ICT world. If it’s the overarching topic, there are many factions underneath it like the internet of things, cyber security, data science, financial technology (fintech), mobile technology, and robotics. What do you think about the overarching representation of AI?”, asks Prof. Vinesh.

“AI works with different mindsets, compared to the traditional idea and technology. So that’s why we call it overarching and we say that it’s going to change the entire world of technology. AI tries to simulate the way a human child grows. You come into the world with no knowledge and then you start experiencing different events and you start gathering your knowledge and you start making decisions. Some of them can be wrong, some right, but eventually you learn and develop your own perception. So, AI starts with no knowledge or a subset of knowledge given to it, we call it training data, and basically from there the system starts learning and it starts taking the real world feedback and it teaches itself. It’s a discipline, and this discipline can be applied to almost anything like if you’re in the financial sector and you see that there are frauds happening and you see your past frauds and you see your past problems and the system learns from that. So basically you figure out how that particular world works. Even with the COVID situation. I would say that it was phenomenal to come up with vaccines from so many different companies in such short notice. Almost all of them have deployed the different variants and are detecting the variants so fast. But with only humans working on this, it wouldn’t have been possible. We’re engaging AI. we’re deploying the machines, feeding it with data which is the assistance from humans and making it learn, the machine learning. So overall this is the discipline and you can apply it anywhere, be it manufacturing, retail, marketing, or anything at all,”

replied Firoze.

Prof Vinesh responds with, “Basically AI is an economic movement shaker, when you sort of couple AI with a lot of AI applications, they sort of push different shifts and trends and enable for business enhancement in terms of interaction, engagement and act as a customer reach out advantage for businesses. If you look at the slides today, there will be 1.8 Billion users by the end of 2021 of AI, in terms of just even a few apps, using voice recognition, using chatbots to enable people to get personalized assistance and using digital assistants to actually reach out to customers and engage with them. And especially during the pandemic when you’re moving away from face to face and a lot of engagements are remote, AI allows for that non-destructive engagements. And we see that that would be further enhanced with different applications, along with the string of applications right now. You see the amount of video capture, the number of devices to enhance processes, scripts and texts. And now we know that AI is going to create new jobs with economies of scale, and all the statistics are there by Forbes. 58 Million new jobs by the end of 2022. Those jobs are eminent. What do you think about that, Firoze?”

“This is a completely new field and there will be jobs in abundance, but the challenge will be the other way around, that is to find the right people for these jobs. So in terms of that, people need to be ready so they can take up those jobs,” says Firoze. 

“For any academic institution, jobs are the indirect outcome, after the direct outcome that is the qualification. And that’s very important for anyone’s educational journey,” says Prof. Vinesh.

The Major Sectors of AI Application:

“Now, taking a look at the AI applications in the industry, there are three major sectors. First of all, Manufacturing. Manufacturing and AI merge together with the upcoming 4th Industrial Revolution, where many activities are being automated. In terms of enhancing productivity and efficiency on the ground, not just for increased quality, which is an important factor for customer satisfaction, but you have predictive maintenance, and you know about your labour workforce, and it’s all about improving your mean time before failure, it’s about automating your processes, and here again the jobs are becoming smarter. There are even factories now that are remote. How do you connect these factories in terms of data, production, and the supply chain?

Now let’s speak about the Telecoms, a lot of them now have remote communication systems integrating AI with voice recognition, and chatbots. You don’t have to go to their kiosks for anything, you can communicate through these channels and then if needed get connected to a customer representative. And then at the back end too, the predictive maintenance, there are towers, service stations, optimization of the entire network, the destructiveness is at a very very low level. And lastly, from the cyber security perspective, the intrusion and detection of malware and also fraud. 

And lastly, I want to talk about Healthcare. Healthcare is being revolutionized by the application of AI. You have robotics which is assisting in surgeries, which is now taking a slow creeping move in the medical world. You have virtual nursing systems in the form of chatbots and people who can talk to you and find solutions to your healthcare problems. You have new drug discoveries, where a lot of the scientific work being done at the back end are actually using AI to quicken the process of analysis and medical scientific work. Then you have genomics too. And AI helps in reducing the errors, because AI is computational and there is no room for human errors. That’s the big three of AI applications. Firoze, please talk about the further applications and give some insights on Finance, Retail and  Government/Defense,” discusses Prof. Vinesh.

“We work in these three specific sectors. Now, the world is very connected. From the government or public sector point of view, we have seen revolutions happening all over the world just by connecting people over social media. People are revolutionizing parts of the world by being connected through social media. This revolutionizing has been happening for 5 to 10 years. Now, from the government’s side, AI can help understand the public sentiment, what the people think about different government initiatives, or the pros and cons of new decisions. People are very expressive. That’s one of the biggest areas where the power of AI and natural language processing are being used. It’s being used to grasp what the people like, dislike, want or care about. This is one of the biggest sectors where social listening is happening. 

This is the same for brands. People are so active in social media and online platforms that brands now listen to what people are saying. AI comes into play here because you cannot make people talk to each other, and you cannot make people go through all the online discussions, comments, reactions, engagement to figure out what people are actually saying. Other than that, cybersecurity is of course a big area of focus, and it’s not only govt. or defense sector cybersecurity, it’s personal cybersecurity too because now we’re so connected with many devices, even at home, starting from smart TVs to the smart watches we use, cybersecurity in the personal realm is definitely growing fast. 

For retail, if you look at the pandemic scenario, there is no other alternative for stores but to make sales remotely. In physical stores, customers had the opportunity to interact with the salespeople, to see the products, touch it and get a feel for it first before making a purchase. But when we’re remotely functioning, we need to give the same real feel. We’re using augmented reality, similar to instagram filter objects. People are using apps where buyers can try out lipstick shades through an augmented reality generated filter on the camera, or other makeup products. Even for fashion, there are businesses coming up with apps where a buyer can put the certain dresses on a picture of themselves and see what it looks like. The volume of data that gets generated in this shopping journey is also utilized to target customers better and enable small and large businesses to reach out to their customers better. 

In terms of finance, when all the transactions are being made digitally, a person’s credit worthiness is easier to measure. Transaction profiles are accessible and it becomes much easier for institutions to define someone’s credit score or financial status. And with the data readily available, it’s easier to spot frauds and we can identify suspicious transactions so that they can be intervened. In these three particular sectors, the data volume is so huge that AI, data science and every field of this entire new tech, the industry 4.0 that we talked about, is very useful and is revolutionizing the entire sector. Pretty much everyone now has a financial wallet, so the transactions that we generate are enablers of the further uses of data science in these fields,” comments Mr. Firoze. 

Career Opportunities in AI

Prof. Vinesh says, “The industries discussed just now and the applications of AI that Firoze discussed are just future avenues and platforms for future career opportunities. They’re quite attractive job titles if you ask me: AI engineer, machine learning engineer, robotics scientist, research scientist for deep dive research, computer vision engineer, artificial intelligence specialist for overarching specialization. 

The other fields that also allow for AI job titles are Software Engineers with a focus on AI and Analytics, there are digital systems processors, image processing, data mining analysts, business intelligence developers and also video game programmers will also use AI. These are available in the current market already but will have new avenues to be explored through AI. 

Now, Data Science is a part of AI, and AI is the overarching umbrella while data science is a part of it. AI has neural networks and machine learning, and voice recognition. And then there is the other area of deep learning, deeper dives into the further science of the neural networks. And all of this happens because of big data. How does a data scientist apply big data?”

Firoze replies, “Let me first explain what it means. When you see machine learning, big data, AI and data science it’s difficult to understand where they all interconnect. We all need to have some clarity here to proceed. When we say big data, as an example, we have transactional data. We’ve been doing business for long enough. In a bank, we have transactional data management systems, on Facebook we have comments, posts and engagement, the statistics is that every five minutes that is spent on a mobile phone, one minute is on Facebook. So people are generating millions and billions of terabytes of data every moment, just to give you an idea of the scale of it. So that is big data. But that data is unstructured. We need to transform that into information. Data science is the science that is applied to data to make some sense out of it. And once you make some sense out of it, you also need to act and see the results and put it back into your operations. And that is where AI  and machine learning comes in, all these things are the overall discipline. AI is when the machine learns on its own, you feed it some structured and unstructured data and it learns on its own, tries it on its own and learns the feedback. That’s the final level of technology. Then comes machine learning, trying to structure the data and you get statistical regression, and other statistics guided tools to structure the big data sets. And then there’s the neural networks, it can be a simple neural network, or it can be a multilayered deep learning neural network. These are the overall tools that are there. The source is big data, and the tools are the disciplines for it.”

The Career Prerequisites

“Now, to take the audience further, I want to quickly speak about what the job titles require. First, data scientists. A lot of people get confused with data scientists, data engineers, data miners. A data scientist needs to understand the data, analyze it, figure out the outcome and the actionable insights from the data. Data scientists make the visualizations and build a story board so that the data makes sense to non-technical people. Furthermore, they also build models, using algorithms and the different types of tools available in the market. You don’t have to create your own tool. These algorithms and visualizations will help services within different industries. Computer science, statistics, data science, software engineering, data analytics programs etc. are the types of programs that will help people get into these careers. You’ll learn some programming languages such as Python, R, depending on the specific needs. You’ll do a lot of machine learning and NLP because Natural Learning Programming is needed for AI applications. And you’ll learn a little bit of statistics because at the end of the day you want to make sense of the data. These will help you specialize yourself in the world of data science. If you like numbers, if you want to crunch data and always have the curious mindset of wanting to find out what the data will lead to, and if you can be a good listener, while understanding domain functions, then you have the qualities of a very good data scientist. 

Next comes the data engineer. What they do is they capture and store data and make warehouses of data and they ensure that the filtration is done. They know where to get the data from. Data engineers have similar qualifications as the data scientists, but here we can also look at business specializations, especially related to analytics, data informatics, cloud computing specializations, etc. Python and R languages are a part of this discipline as well. Python and R are quite friendly as coding languages. I learned Python in 4 days at my age, and these two are quite common in the data analytics field. Database, cloud design etc. are important along with deciphering the structured and unstructured data. 

Now, Firoze, can you chip in here on data analysts? Explain what the role is as a data analyst,” says Prof. Vinesh. 

“Basically, the primary work of a data analyst is to visualize the data. They slice and dice the insights from data. They apply different tools and techniques, visualize the data and try to extract insights. The way that we utilize data analysts, once the engineers have structured the data or even if the data is unstructured and in its raw form, you need to look at the same data in different ways, different dimensions and parameters. Maybe, as an example, you can analyze your sales by geographic regions, you can analyze your consumer behaviour and customer satisfactions over time and all these things. So, data analysts are the people who will be going through the data and giving different representations of the data and insights,” responds Firoze. 

Integrated functional relationship between data analysts, engineers and scientists

“Over the last 18 months, none of us have been able to travel. The big data portfolio can help in dynamic pricing, predicting flight delays, so the travel industry or the airline industry uses data to optimize their sales. And that is why you have different pricing for different seat locations for different days for different destinations. Now a lot of travel operations are also impacted due to different factors, you have flights arriving late, early, and a lot of people believe it’s due to airport functionality and weather control. But it’s actually not. If you look deeper inside, you’ll find other reasons from the data.

Now, let’s talk about insurance and credit. Firoze, give us some insights,” says the professor.

Firoze remarks, “The credit industry is one that is being revolutionized by AI and data science. You see, traditionally banks only finance a certain group of people. We’ve all heard discussions about financial inclusion, we need to start including the majority or the masses in financial systems. That’s how all these different wallets are coming up to enable people to use digital financial systems. Once they’ve been digitized, their transactions are digitally accessible. Once data about them is analyzed, these people who aren’t financially backed by traditional financial banks can be targeted profitably. If you look at the current industry, credit, insurance, and even microinsurance, we have seen one day insurance or one ride insurance, people can even insure for one single plane ride. These microinsurances are coming up because of the overall industrial revolution and use of data.”

Prof. Vinesh says, “Business analysts can be from a computer science background or a business person with analytical skills. SQL, different visuals, different business intelligence services can be used and the business analysts can come up with different concepts that will define strategies and will be the end of the line when communicating with owners of the companies, data providers or even customers. They don’t need to do the deep technical analysis, which is done by the engineers and the miners, but they do have to make sense of the outcome of the analysis so that they can sort of put it out in terms of presentations, outcomes, and the result for all the stakeholders. They play the role in bridging the gap between the core data scientist and the stakeholders. Then there are business intelligence analysts, who are quite similar to the business analysts but they use some tools that help in extracting information and transform it into information that makes sense so that the end result improves the business. The BI Analyst will know the KPIs, the vision and mission and goals of the company. They can use the identified trends to examine it against other competitors too to figure out a strategy to enhance business and give an edge.

Firoze, can you explain a bit about dashboards and where the data comes from and where it eventually leads?”

“Let us consider a scenario in a Telco environment. Hypothetically, if a tower goes down, then people in that area will not be able to use telecommunication services for that particular operator. So, if any cell tower goes down, it has a high impact on the overall network and even on the public service view as well. So, how can data provide any aid here? Even for situations such as a cyclone, storm, power outage, maintenance problem, or infrastructure problem, any reason for the network being down, we have the historical data for sites. And we know from weather forecasts and different external matters, how the future might look like for a particular day. By combining historical data and weather forecasts, we can predict that maybe these are the probable sites which may face a certain type of problem on that day. And when I know that there will be a problem, such as a power outage, what can I do here? If the outage duration is long, I can put backup generators and make sure it’s functional.That is going to be a precaution against the possible problem.

So you see, combining historical data and weather forecasts, we can predict which sites will have some power problem and we can prepare against it, so when the problem arrives, I can still have some backup. This is how existing and predictive data together can come up with indications on what can go wrong. And when I know what can go wrong, I can deploy my operation to prevent that. This is an example but this is generally how the operational workflow will proceed for virtually any business or organization or operations,” discusses Firoze. 

Questions from the Audience

What is the difference between Pseudo Code, and Python, JavaScript, C? 

Firoze responds, “Python, JavaScript and C are programming languages but Pseudo Code is basically just outlining your thought process. How you want the logic to be organized. It doesn’t run but it helps you organize your thought process. The programming languages on the other hand get executed.”

The moderator Ryan Tan chips in, “Between the mentioned languages, Python, JavaScript and C, which one do you think is the most popular for data analytics?” 

Firoze answers, “There are actually rankings of programming languages, at the moment Python and JavaScript are the most popular. In terms of data science, I believe Python is going to be the most popular due to its friendliness and ease of use and ease of exploration.”

Ryan further says, “We always get questions, students are worried about whether it’s very difficult to do programming. When we talk about big data and data science, it sounds difficult.”

Prof Vinesh says, “I’ll ask a simple question here, as a data scientist or data analyst yourself, tell us about your job satisfaction.”

“I am very passionate about programming code. To me, I’m changing the world through writing code. I personally feel, as a programmer or data analyst, whatever you do when you apply the changes to a business, you can see the changes so fast by your own hand and you see the impact. You see the satisfaction on people’s faces. And that is what keeps me going, and pushes me forward in my career. The impact that technology has in people’s lives is amazing. And maybe someday, my written program can develop enough to be close to JARVIS from Iron Man, and that potential future is what drives me.”


Prof Vinesh says, “Students learn things course by course, but the job itself or the career path in the future is still very confusing for them. It’s an unknown world. They get a taste of it doing their internship, but they are not really doing deep projects. Knowing how attractive this field of work is what we wanted to hear from you.”

Firoze adds, “On that note, we learn engineering in school but we deliver business in the real world. We all must understand that what we learn and what we deliver are completely different and this transformation only happens in the workplace. Unless I’m focused about delivering the final benefit to the business, my technological knowledge is of no use. Whatever I know, I have to apply, transform and then deliver. I have to have the mindset when I start my career. This is something that fresh graduates struggle with often. Overnight, you don’t do so much. There are business understandings, business impacts, chains of delivery processes, and the businesses themselves are in a life cycle of their own, most likely not just starting from scratch. The things you’ve learnt in university are fantastic, but you have to get the mindset that whatever you learn is giving you the opportunity to apply it to bring the results. And this mindset is required for the real world.”

Prof Vinesh asks, “Mr. Firoze, a quick fire statement from you about AI and Data Science now to end our session and about the future.”

Firoze comments, “AI and data science are just in their initial stages now. When computers came in, we had computer operators and computer engineers and all these job titles. We’re actually just at the beginning of that, and that’s why we have AI and data scientist job titles now. Just as no one actually hires people for the purpose of knowing excel anymore, similarly at one point in time AI and data science will be embedded into every field of business and every field of work. This is just the beginning of the entire field, and it will be picking up. We’re just at the bottom of the wave right now, and if we can get into this wave now then I think the career opportunities will be fabulous.”

Prof Vinesh ends the session with some statistical information, “In the last 5 years, the jobs in this field with highest growth have been:

Machine Learning Engineers: 344% growth

A Robotics Engineer: 128%

Computer Vision Engineer: 116% 

Data Scientist: 78%

Prof. Vinesh

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