Tech Talk with Toni

A bitcoin spider (it does not exist) Fintech is an area of the financial industry that is constantly evolving and full of excitement and confusion. While you may enjoy the convenience of tapping your card on a point-of-sale terminal, your granny may struggle to adapt to the bank's new app. We can all agree that technology is infiltrating every sector and has led to the emergence of cryptocurrency and autonomous vehicles.  However, with innovation comes the need for regulation, and companies in the United States are now subject to more than 50,000 regulations according to page 6 of  SVB's Breakdown on The State of Fintech . Failure to comply with these regulations can lead to severe penalties, damage to the company's reputation, and even legal action. Coinbase is a prime example of this, as they were fined $50 million for failing to build and maintain a compliance program that could scale with their rapid growth.  Today, risk management is still a highly manual task that re

 From Numbers to Knowledge  A bird's eye view of the brain with a CPU embedded in it The demand for data analysts and scientists is hotter than the Caribbean sun!  And it's not just because we want to make graphs and charts that look fancy.  No, no, my friend. We need data literacy to survive in the Information Age! Sure, fight or flight responses helped us dodge deadly predators like marsupial lions and saber-tooth tigers back in the day, but now we must use our brains instead of our instincts.  We have to know what the numbers are telling us, and we need to ask the right questions to find the answers we seek. Personally, I've always been a "Why?" kind of person, and it's helped me a lot in my tech career.  Trust me, annoying your parents and coworkers with that one-word question can be a great strategy for finding answers. But it's not just about asking questions, we also need to know our field's jargon and what's expected in each area.  For inst

 A Guide to Effective Data Storytelling Two Trini Girls Gossiping In the Office Eh girl, lemme tell you dis story bout Maya, dis analyst in de office.   She had to work on some set of data and it was giving everybody rell headache.   Maya say she goh solve it, but as she was going through de numbers,  she start to realize dat someting wasn't right.  Den one night, she hear ah voice telling her dat she eh goh ever figure it out.    Maya get frighten and turn around but ain't see nobody.    Like one ah dem real old-time horror movie, eh!    From dat day, Maya start to say dat de data  was possessed and she went around de office telling everybody. Pat was like "Child, your story is more twisted than the road to Maracas" When a friend tells us a story, we often find ourselves fully engaged and able to recall the details easily. This is because our friends tend to use literary devices such as metaphors and imagery to pique our interest and evoke emotions.  Similarly, when

 Here's Your Guide to Successful Data Collection Tom is going duck hunting Congratulations on embarking on the exciting journey of becoming a Data Analyst!  You’ve got the degree, you’ve got the skills, and now it’s time to enter the wild world of data. But, wait!  Before you head out with your data traps and bait, there are a few things you need to know.  Unlike the nice and tidy datasets you worked with in school from Kaggle, data in the real world is a wild beast.  It’s unpredictable, unruly, and sometimes downright dangerous. Think of yourself as a hunter.  You need to know exactly what you’re looking for, where to find it, and how to capture it.  And, just like hunting, the data collection process requires patience, strategy, and a willingness to learn from your mistakes. So, here are five steps to help you capture the right data: Step 1: Define the Problem Just like a hunter needs to know which animal to hunt, you need to define your problem before you start collecting data.

2021-2022 : Smaller Classes, Harder Work My final year was one of the most challenging years for me at UWI, surpassing even my first year.  This was mainly due to the fact that I chose more difficult elective courses such as Intro to Data Analytics and Intelligent Systems, despite having no background knowledge in those domains.  Although the first and second years laid the foundation for programming, it did not prepare me for the complexities of the Horizontal Apriori Algorithm (used in Data Mining). Each programming course in the final year was densely packed with foreign concepts that were not always easy to grasp in class, which meant that I had to spend a lot more time studying and preparing for exams.  Even the lecturers would often say, "Today's topic should be an entire course on its own." Group projects carried more weight, with Human Computer Interaction being an entirely group-based course. Additionally, I contracted COVID-19 during the semester, which made att