Willis News

Staff Profile: Anthony Aighobahi

June 24, 2024
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Do I need any computer basics before starting the AI programs?

Anthony: The course is actually designed to consider basic computer technology knowledge. So essentially, one will need to understand what computer hardware are, as well as software, when I say software, I mean operating system, and a little bit of basic programming with mathematics, which is essentially statistics. That is the fundamental knowledge required for the program. But the good thing, like I said, is all of this will be covered in the very first module in the course.

 

Great; can you tell me the differences between Data Analyst, Business Intelligence Analyst and Data Analytics & Artificial Intelligence?

Anthony: Good question! The Data Analytics & Artificial Intelligence, I call it the end game. We have smaller courses before that end game course. So, we have the Data Analyst, like you said, we have the Business Intelligence Analyst before the Data Analytics and Artificial Intelligence. The tasks that one will typically conduct as a Data Analyst is to clean data, process data, try to interpret it, and that’s why, one, would need knowledge of Microsoft Excel, SQL, and a little bit of Python for that part. The Business Intelligence Analysts go deeper. They understand the business and they try to apply it to data. And that’s why skills like visualization comes in. How do you interpret these results to stakeholders? We would need to know visualization tools like Power BI, Tableau, to be able to communicate the analyzed results to stakeholders. And the final one, which is the one that everyone loves, is Data Analytics & Artificial Intelligence. We need essentially to know machine learning, which is one of the tools that powers AI. Deep learning is another one of them and others like that. So yeah, those are the differences between the three courses.

 

Can you provide examples of tasks that I’ll be working on in these fields?

Anthony: Data Analyst is essentially for people in finance, in business, in marketing, in the healthcare, even in cybersecurity field as well. So, one will be dealing with large datasets trying to clean these datasets, process the datasets, find patterns and trends from these datasets, essentially trying to interpret these datasets. For the Business Intelligence Analyst, one of their key responsibilities will be to tell stories from these datasets, how do you visualize this? How do you create dashboards from these datasets? That will be the key task for the Business Intelligence Analyst. Then for the Data Analytics and Artificial Intelligence, the big task would be how do I automate this task? And that’s where the AI tools come into play, like the machine learning come into play, where one has to use these advanced technologies to automate, to make it faster. For example, dealing with big datasets. One, will need to train these datasets using some of the tools I’ve mentioned earlier, especially machine learning. So how do I apply machine learning models to data problems? How do I apply it to my dataset?

 

What tools will I be learning in the program?

Anthony: In the Data Analyst course, one will be learning Microsoft Excel, which is a very basic but very important tool for data analysts. SQL is one of the tools that we’ll be learning, and for the Data Analyst course, very important, we’ll introduce Python, which is the tool we’ll be learning. For the Business Intelligence Analyst, we’ll be using tools like Power BI, we’ll be using tools like Tableau, and we’ll introduce machine learning concepts in that program as well. And we’re going to be using Python again for the Business Intelligence Analysts. We’ll be using frameworks in Python for machine learning. And for the last part of it, which is the Data Analytics and Artificial Intelligence, then we’ll start looking at even some advanced tools. Using Python again, we’ll be looking at tools like TensorFlow, Keras, and so many other frameworks in Python that’ll be using for the AI part of the course.

 

How can businesses effectively adopt AI technologies to enhance growth and efficiency?

Anthony: So that’s one question that gets asked a lot. Many businesses are worried about the rapid growth in AI, so they’re finding it challenging. One of the challenges they found is privacy, which is what everyone is talking about in AI. There are different legislations right now that the government has proposed for handling these privacy concerns in AI. But back to what your question about how businesses can adopt AI is to, first of all, look at which of these tools meet their business goal. Because one of the concerns is there are so many AI tools and there are some of them that are really, really advanced. So, if my business requires just basic Excel, then yeah, go for it. Why will I go for machine learning when it’s very huge and expensive? So, businesses, the advice will be, look at the business goal, look at the business needs, and find the AI tool that is appropriate for their business.

 

How can data visualization tools assist in making data-driven business decisions?

Anthony: Usually if you look at large data sets by just opening them, either it is in a CSV file, which is essentially Excel or it’s in a JSON file or a TXT file, they look really huge. And I can bet you if you present that to a stakeholder, they can’t make sense of the data. So, the best thing is to use some of these visualization tools that creates graphs, that creates patterns that is very easy to interpret. For example, you want to know what the trend of sales has been for a period of time by just visualizing it or plotting it in a graph. One can easily, within seconds, interpret that data set by using visualization tools compared to when you’re dealing with raw data sets that you can really make sense of it.

 

What are the key differences between supervised and unsupervised learning in AI?

Anthony: It’s a good question. So supervised learning and unsupervised learning, is how we train data sets. The supervised learning data sets, these are data sets we are trying to train but they come labeled. That means they have labels in them and usually more straightforward to train data. Unlike unsupervised learning, the data sets are unlabeled. So, one has to create machine learning models that trains these kind of data sets without label, and that’s essentially what’s unsupervised learning.

 

What are the essential skills required to begin a career in data analysis and AI?

Anthony: Like I said, we need to be able to utilize Microsoft Excel, know statistics and be able to understand how to make predictions because most of these tools helps with making prediction. We want to know what the next year will look like in budgeting, for example. So, one has to know statistics to succeed in the field. And apart from that, one has to know basic programming, which is essentially, Python , a very popular for data analysts. We need to know some visualization tools as well, know how to use Power BI, know how to use Tableau and some other visualization tool. And finally, in this day of AI, one needs to know how to apply some of these machine learning models to our data sets.

 

Can you provide examples of real world applications of AI across various industries?

Anthony: One application that comes to mind is in cybersecurity. In cybersecurity, we have what they call logs. So, for example, you have machines that generate log. Log means when an attempt was made when someone tries to log into your computer, when you try to perform an action. So, it logs all of this. In cybersecurity, what one typically looks for is anomalies. What are the strange patterns? Why does a person always logging in at 2:00 PM? So that can signal that someone is trying to do an attack. Sometimes these logs are really huge and you need to be able to interpret what this log means. So, one of the application of data science or data analytics is to use the training to be able to analyze these logs and find patterns that one can report and essentially make sense of it.

 

What are the best practices for ensuring responsible and ethical use of AI in applications considering data security and privacy concerns?

Anthony: Yeah, so one of the biggest concerns with AI now, I will give an example of ChatGPT, which is what most people think of AI. Right now, ChatGPT uses different data from different sources and sometimes, especially when you’re trying to use this information in the public domain, one is worried about where is this source of this data? Am I even allowed to use this data? Those privacy concerns come in when one is using AI. But the appropriate thing to do is before using any of this, is to check guidelines and check regulations. Canada right now, through the AIDA, that’s the Artificial Intelligence Data Act, have regulations and guidelines that one can use or adopt just to make sure you’re using these AI tools appropriately.

 

Do you have any advice for someone thinking of taking one of the AI programs at Willis College?

Anthony: One very unique thing about Willis College is that, we give you skills that will get you hired. We teach you skills that will get you hired. We’re not just teaching theory, we’re not just teaching from the textbook, but we’re teaching practical application of these concepts. And we follow the same thing for our Data Analytics and AI course. We’ve introduced real-world projects into our courses, and this will equip students for the job market. And we are proud of that because in our other IT courses, most of our students, once they graduate, many of them get hired immediately. And that’s because we are not just teaching them the theoretical concepts, but we’re also teaching them the real-world application of these concepts.

 

And lastly, would Cybersecurity graduates benefit from taking any of our AI courses?

Anthony: The good thing about IT, which in Willis College, one of the things we are looking at is how can we make you successful all rounds. Because in Willis College right now, we have our cybersecurity program, which is doing very, very well. So that way, we thought about we are tackling a problem from one dimension. In our Cybersecurity program, we are focused on securing your information. IT is all about information. We are securing your information. In Data Analyst (and Artificial Intelligence) now, we’re going to be using that same information to help businesses. So, the good thing is once you finish from the cybersecurity program, you’re very well-grounded in securing information. But now how do I take that secured information to make business decisions? So that will give you a better advantage when you finish because you’re not only securing the data now, you can also use that same secured data to make informed business decisions that can grow a business.

 

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