“The hype around AI and data science is justified”

With a solid foundation in data-related fields, Dr Jacqueline Campbell identifies as a data expert. Her journey as a data expert moved her into academia where she has played a key role in leading data modules or courses.

Currently, Campbell serves as the Course Director for BSc and MSc in Data Science at Leeds Beckett University, UK. Her professional experience includes years in the industry, working with major companies in roles such as a software developer, data analyst, and data consultant.

To introduce a similar course in Nepal, she is here in Nepal and working with the team of The British College that runs courses affiliated with Leeds Beckett University. As someone who has led the team in the UK, she brings a wealth of experience and expertise to the table.

After a decade of digital collaboration with the Nepal team, she is now fully engaged in understanding the country, its data landscape, and the students’ interest in data science.

Onlinekhabar caught up with Campbell to discuss the prospects of data science in Nepal, and what the new course holds for aspiring students.

Excerpts:

As someone who has been in the field, how do you observe the growth of data use, and how people perceive data science?

Data has always been there. But before, it was seen only as part of computing. You cannot have any kind of system or information without data. But now that it is cheaper to save data, technology has enabled us to collect different types of data, from different sources, around the clock.

Initially centred on databases and basic analysis for sales reports, data’s role has expanded into a pervasive force. Companies now leverage data not just for retrospective insights but for predictive analytics and pattern recognition, driving more informed decision-making. We can also create dashboards and visualisations to effectively communicate the insights derived from the data, and it has opened up possibilities and uncovered valuable insights that may have been otherwise overlooked.

Beyond the corporate world, individuals engage with data daily – from fitness tracking to optimising their day, health, purchase behaviour, and leisure based on the information [user data]. Data is now everywhere, and it has propelled the world into the integration of artificial intelligence as well. It’s been said to be the 21st-century tool that saves us time, to do things more quickly and accurately.

But businesses and how they do business are changing. And all of that needs data experts to understand how we can do that ethically.

When it comes to data science, does the hype match the depth of its potential and the academic objective?

The hype around AI or data science is true. Look at 10 years ago and look at what you’re doing now, it will be entirely different. But the progression is still slow, and human roles and intellect are still crucial and it will take some time before AI will take over the jobs as feared. 

If you were to play chess against artificial intelligence and you are a chess expert you might not understand why it is doing certain moves but it would win. It took 43 years for AI to achieve that.

I can ask chat GPT to do a report for me but I have 30 years of experience and I know whether what I am looking at is correct or not. So, you can use AI to do something for you, but whether or not it is what you want if it is true or not, where are the outliers, and where do you need to intervene–that is not going anywhere. 

Some companies have created an AI algorithm to help them with recruitment, but they were using the data they already have to help them, and it had data that is mostly male. So their AI tool tells them to recruit males based on the success rates of the data input. 

Data have all sorts of power and what a data expert and an artificial intelligence specialist would know and understand. 

And I think people do not necessarily understand data. So this would be something that we would like to do. We need correct data, without biases, and data literacy focuses on getting the right (fair) data and knowing what is missing.

Why does one need to understand and learn data science academically when there are many online courses and asynchronous learning opportunities?

So the difference between going to university and doing a bunch of courses online is that at university, we are always teaching within case studies, we are always teaching with real problems.

At university, one minute, you will be a coder developer, and then one minute, you will be a manager or a head of a department. So you’re thinking within those different roles to give you the skills and the practice to develop into those roles when you go into the workplace. What we are trying to do is to help people think critically about their work.

Companies would recruit those who can understand how data can be used to solve a problem, investigate a project, and then where to get the data from, format the data and present the data to tell a good story. So they need that digital literacy. [With the course] we would expect the students to be able to fulfil that. And also develop the right visualisation tool and graphics to tell the right story.

AI in the digital world - data science
Photo: Mike MacKenzie for Flickr

How can Nepali students benefit from pursuing data science academically?

Graduates from Nepal also can get jobs as data analysts, data maintainers, AI experts, marketing services analysts and in sectors where one feels the process could be improved or needs improvement.

Of the few examples, one could be water management. In the UK, we have data on all water bodies, underground and on earth, this helps us identify areas to work on, avoid natural disasters, alert systems, etc. And if you look at the world, because of data technology, people have died less of natural causes in comparison. 

Another is in agro-technology, one can use data to study the environment and create one for agriculture to thrive, optimise the resources, automate the process, increase food security and manage waste.

What I see from Nepali people is a real sense of activism and innovation, many of them already thinking about new projects and businesses. So Nepali students can leverage the knowledge from the course and implement it better here as they know the problems here and it is about getting rid of problems [using data]. 

To introduce the course Nepal, what kind of preparations are you doing? Any research on how you need to redesign the course?

For the week I am here, that is exactly what we are working for. So in the UK, we have been running the undergraduate course for three or four years and the post-graduate course for much longer. The course includes learning about databases, some maths, computer science, and analytics. Then it moves into artificial intelligence in terms of machine learning, recommendation, systems, predictions and critical thinking for the projects and the data. 

There are 120 credits on each level i.e. 4, 5 and 6. They will also do projects in data science of their own choice in the second year and the final year. 

For Nepal, we have been collaborating with those with a lot of industry experience and we value their input and opinion. Meanwhile, I am also meeting students and staff, learning what they are thinking about the way we can use data how we can use data and how we are going to build data. We are thinking of doing some kind of data science workshops with students who have the background along with the aspirants and getting some feedback. 

As a part of our research, we are also working on gathering good case studies that students over here can relate to and learn from. We also are looking to check if there’s anything we need to contextualise, and make tweaks. But in general, the delivery is very, very similar.

What do you think are the key points about data collection and usage that we should focus on? Do you have any concerns?

One of the interesting things is that we can only use data science where we have the data. So, for example, in Covid, there were many problems for the world, because there was no history of Covid around. Therefore, a lot of mistakes were made. But, in other areas, such as recognising cancer, or working with diabetes, we have a lot of data. And having such data will help everyone.

Now that we are using all of that data together, the projects have developed in a different way for sort of customer analysis, behaviour analysis, and marketing. 

On the other hand, we are giving your information online and it is being shared with others, to various extents. But we cannot trust that every individual knows this. So, it largely falls on the companies to make sure that people’s data is safe.

So, the use of data needs to be fair and transparent, in particular by the companies collecting them. The companies using the data need to be accountable. The UK government is leading on this as it has some ethical framework in place, to ensure fairness, transparency and accountability. The US is following suit. 

However, how do we globally decide on what is ethical or right and wrong? What is fair or unfair to somebody? What is ethical? It is a really difficult decision for countries actually to decide what they are and are not okay with. 

As we are yet to regulate everything, I recommend everyone to stick with bigger companies that will have to adhere to these rules and not scam or misuse the data. I try and follow this as well. 

The post “The hype around AI and data science is justified” appeared first on OnlineKhabar English News.

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