Tech Talk with Toni

  A Tesla assembly robot attacked an engineer Today's discussion draws inspiration from a standout moment in the movie Wonka.  In the 'Scrub Scrub' scene, Willy Wonka  unveils a revolutionary clothes-washing machine at Scrubbit’s, where he works part-time. The workers were impressed by the machine and acknowledged its ability to significantly boost their productivity. In a world with many problems, having the freedom to decide how we use our time is empowering. In Wonka, after creating the makeshift washing machine, the characters shifted focus to aiding Willy in selling his chocolate, deeming it more valuable than hand-washing clothes..  Today, despite having washing machines, we fuss about folding clothes and other repetitive chores.  Wouldn't it be great to have these tasks completed in the background while you engage in activities you truly enjoy? With that being said, here are some mundane things that we all do that can be automated:  Too Tired To Turn Off The Ligh

  A stereotypical engineer Despite concerns about job changes due to ChatGPT, something interesting is happening: the emergence of a new job title known as the Prompt Engineer . Still skeptical? Take a moment to browse professional platforms like LinkedIn or Indeed—see it for yourself! Now, you might wonder, 'What is Prompt Engineering?' Well, I'll give you a prompt answer (okay, sorry for the pun). Essentially, prompt engineering involves using special words or hints to instruct computers without saying everything explicitly. Think of it like when your mom says, "Bring me my bag from on top of the table." Even if your house has many tables, you know it's not in her car. She assumes you know what her bag looks like, so she doesn't need to describe it. But if she tells you which room to search in, you'd find her bag faster, right? Having said that, here are some tips that can help you write robust prompts: Understand the Task This is where your domain

  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

   A gateway that allows third-party programs to enter and access the data and functionalities of another program. In the year 2000, Roy Fielding's dissertation popularized REST (Representational State Transfer), the backbone for modern APIs. REST is an architectural style that has become widely adopted for building web APIs due to its simplicity, scalability, and compatibility with the web. Much like a language translator helps two people who speak different languages understand each other, an API, or Application Programming Interface, translates requests from one software system into a language that another system can understand. Now, imagine checking your phone and seeing the weather forecast magically pop up on your home screen! That's a real-life example of an API in action.  It's like your phone asking a weather service nicely for the current weather info, and then showing it to you without you having to open a separate app. With that being said here are some other us

  We can easily see that a piece is missing here.  Last week, my friend and I began brushing up on our Data Analytics skills outside of our work hours, starting off with a Data Cleaning Tutorial on Kaggle. The first section of this tutorial focused on Handling Missing Values, and although it was somewhat familiar to me, I still learned a lot. In a previous post I focused on what to do when working with an incomplete dataset. Today I will be focusing on how to find the number of missing values in a dataset. Pick your path: Go on an adventure to find the missing values manually. Let Python do its thing Now, while option one can work for small datasets like the example below, option two is useful when working with thousands of rows and columns. It has been a year since I completed my Computer Science degree.  Initially, I assumed the process was as straightforward as finding the number of nulls using the .isnull() function and summing them with .sum().  However, to arrive at the correct