Tech Talk with Toni

You can clearly see which one stands out   Today's post is inspired by a question one of my colleagues had around outliers: Are there different types? And to that question, the simple answer is yes! As data professionals, we often encounter data points that stand out like a sore thumb during analysis, leaving us wondering what to do with them. In most cases, deletion seems like the obvious choice.  However, I believe we need to pause and take a closer look at these points before removing them. You see, during Exploratory Data Analysis (EDA for short), asking the right questions about the dataset is crucial to uncovering its full story.  Deleting an outlier just because it stands out can create a narrow, myopic view of the data, limiting the depth and value of the insights you can gain. Rather than rushing to remove outliers, consider what they might reveal. Could they signal an emerging trend, an unusual but important event, or even a data collection issue?  With t...

Tens of millions of people suffer from dog bites each year globally, compared to just 50–80 shark bites. As a data professional, I am expected to interpret data and provide stakeholders with insights in the form of a story. Many refer to this skill as "critical thinking." While that is valuable, one must also question how they arrived at their conclusions in the first place. According to Wikipedia, a bias is a disproportionate weight in favor of or against an idea or thing, usually in a way that is inaccurate, closed-minded, prejudicial, or unfair. I gained a deeper understanding of biases during an Organizational Behavior course I took in university, and I briefly mentioned it in Blog Post #8, which discusses data-driven decision-making. Beyond numbers in a database, biases can lead to poor decisions in the real world. For example, it is statistically more likely to be bitten by a dog than by a shark; however, availability bias caused authorities in Tobago to place a bounty ...

On May 22, 1960, in Valdivia, Chile, the largest recorded earthquake with a magnitude of 9.5 occurred. This week, I decided to refresh my understanding of the math behind the Poisson Distribution (pronounced as "pwah-son"). I first came across it during Lecture 3 of Industrial Statistics, where we were exploring Probability Distributions amidst the pandemic. Now, you might wonder, "What is a probability distribution?" In simple terms, it's a way to represent how likely an event is to happen. So, what makes the Poisson Distribution special?  It's named after the French mathematician Siméon Denis Poisson, who was fascinated by mortality rates and birth statistics.  He developed this probability distribution to model events that are both rare and random but occur at a constant average rate —think of earthquakes. Example Earthquakes occur on average 3 times per month in Little Garden Island. What is the probability that on a given month there will be no earthqu...

  y = mx + c: looks familiar, right? In my Data Analytics journey, I've chosen to revisit and explore algorithms commonly used in this field. Today, our focus is on linear regression: understanding its significance and limitations. Let's begin by unraveling the concept of 'Linear Regression' through a series of "dumb" questions: What is a line? A line consists of an infinite number of points that extend indefinitely in two opposite directions. What does it mean to be linear? In mathematics, linearity often refers to a relationship between variables that can be graphically represented as a straight line.  What is regression? Regression refers to a statistical method used to analyze the relationships between variables. Considering these explanations, we can define Linear Regression as the analysis of the relationship between two variables displayed along a straight line. Suppose we want to understand how traffic volume changes on a particular highway as Christma...