Hi everyone! I wanted to share a Skool group I came across that might interest you. The group is led by someone with experience at both Apple and Google, and it’s specifically designed for data scientists. Currently, there are only 55 members. From what I understand, the group will transition to a paid model after reaching 100 members, but those who join early will have free access 😉. This could be a great opportunity for anyone looking to join early! The group is called Advanced Data Science Society, and here’s the owner's profile: LinkedIn Happy learning everyone!!! @Brandon Phillips @Viktorija Trubaciute

After doing a bunch of projects for everything but AI Voice Agents, I finally gave into the hype. Some weeks ago, I showcased the final demo for a custom-coded AI Voice System made for one of the biggest IoT Companies in Spain, and they seemed to love it! Now I know: the ticket is not that high, specially for such a big company. But, as stated, this is the first time I dive away from RAG systems & other solutions, and go into AI Voice Agents instead. And I didn’t want to disappoint! (and no, sadly I couldn't make it a recurring subscription). Managed to get this deal thanks to a professor from a University in Spain who leads research projects in collaboration with this big IoT company, and this should be the first (and cheapest) phase of the project. We're now heading into automated document generations using OpenAI + Google Docs (for URDs, SRDs, etc.) Fully aware this is not the most impressive deal, but we're building things up slowly, there's more to come. ----------------------------------------------------------------------- Project info: 📌 What is it? Custom-built AI voice assistant that initiates interactive, outbound calls at the time desired by the user. It calls the clients from the IoT company who want to develop software projects with them and gathers all the necessary information to get started with the project and create the necessary documents (URDs, SRDs…). ⭐️ What does it solve? What they have to do without the voice agent is: fly out an employee to wherever the customer is located, and speak with them directly to gather all information they require to get started developing the software. An incredibly expensive and time-consuming task for the business. Now they can just send a form to the customer, they fill in their information + desired time for the call (instantly or at a set date and time). Then the LLM will go through a series of questions set by the company, and it will keep asking until it gathers all the necessary data from the customer.

Hi fellow Data Alchemist! I hope you're all doing well. I'm here today to offer a helping hand to anyone who might be struggling with Python, R, or programming logic. I’m really passionate about explaining concepts clearly and want to get even better at it, so I’m offering my support for free, just because I love doing this! If you're stuck on something or have any questions, don’t hesitate to reach out. You can message me privately, and if needed, we can even hop on a call to figure things out together. I’m really looking forward to helping out, so feel free to get in touch! Wishing you all an amazing weekend. 😊 Best, Ana

from bakery import assert_equal from dataclasses import dataclass @dataclass class Rectangle: length: int width: int def area(rect: Rectangle) -> int: return rect.length * rect.width box = Rectangle(5, 3) assert_equal(area(box), 15) I am getting confused which is function here and which is a class. What is the decorator here? Can somebody please explain this in simple words?

Hi fellow Data Alchemists, I’m writing here with the goal of studying together the essentials we need to know if we want to become Data Scientists or work with Machine Learning. I’m considering creating a series of posts covering Statistics, Probability, and maybe even some Math needed for Machine Learning. I’m not an expert, so I’ll be gathering information from the web, I'll write the post and I'll ask ChatGPT to correct me. My idea is to create some accountability for myself while sharing my studies with all of you. Today, I thought I’d start with the basics: Measures of Central Tendency. As the title suggests, "Central Tendency" should already give us a hint about what this is about, right? If you're not sure, it’s simply a fancy term for describing the mean, median, and mode. So, what are Measures of Central Tendency? They’re key statistical tools that help us summarize and understand the central point of a dataset. These measures are especially useful in data science for interpreting data distributions and providing meaningful insights into the general behavior of the data. The three primary measures, mean, median, and mode, each give us a unique perspective on this “center.” - Mean: The mean is calculated by summing up all data points and dividing by the count of those points. It’s useful when data is symmetrically distributed, as it represents the expected value. However, it’s sensitive to outliers, so in skewed distributions, it might not accurately represent the center of the data. - Median: The median, or the middle value when data is ordered, is especially valuable in skewed distributions or when there are outliers. Since it reflects positional rather than magnitude-based centrality, it often provides a more robust measure of central tendency than the mean in non-normal distributions. - Mode: The mode, or the most frequently occurring value, is useful in categorical data or multimodal distributions. It offers insights into the most common category or value in the dataset, which can be particularly important for understanding customer preferences, product popularity, or common patterns in discrete data.