Just completed by Python course on Kaggle https://www.kaggle.com/learn/certification/kirtisinghchd/python

Hey everyone! As we’re heading towards the final month of 2024, I’ve been reflecting on what a whirlwind the past few months have been. Q3 and Q4 have been incredibly busy — between running Datalumina, managing client projects, seeing amazing growth in Data Freelancer, shipping the GenAI launchpad, moving into a new office, and onboarding the first customers for our new SaaS product, it’s been a ride. 🚀 On top of that, keeping up with the YouTube channel has been both a challenge and a joy. Honestly, it’s one of my favorite parts of what I do — helping you all learn, grow, and tackle data challenges with confidence. Seeing your feedback and progress keeps me motivated to keep creating. Now, I’m starting to map out my content plans for 2025, and I’d love to hear from YOU. What are you currently learning? What topics would you like me to cover next year? 🎥 Would you like to see more LLM-based tutorials? Deeper dives into machine learning workflows? Maybe even some freelancing tips for data professionals? Or something else entirely? Let me know in the comments — what are you most excited to learn in 2025? This is your chance to shape the direction of the content we create together. Thanks for being part of this community! 🙏🏻 — Dave

Wednesday funny

Hello, everyone! Sorry for the delay in posting. Let's continue our statistics exploration. In our last post, we covered standard deviation and how it measures variability around the mean. Today, we’re diving into another useful measure of spread: the Interquartile Range (IQR). This metric zeroes in on the middle portion of the data, offering a clearer picture of central distribution while sidestepping the impact of outliers. What Is the Interquartile Range (IQR)? The Interquartile Range (IQR) captures the spread of the middle 50% of data. Unlike variance or standard deviation, which involve all data points, the IQR focuses on the range between the first and third quartiles (Q1 and Q3). By ignoring the extreme values on either end, the IQR is especially helpful in understanding data with outliers or skewed distributions. In other words, the IQR helps us see where most of the central data points lie, offering a more robust view of spread that’s less influenced by unusually high or low values. How to Calculate the IQR The IQR is the difference between the third quartile (Q3) and the first quartile (Q1): IQR = Q3 − Q1 Breaking Down the Quartiles: - Q1 (First Quartile): The median of the lower half of the dataset, marking the point below which 25% of data points lie. - Q3 (Third Quartile): The median of the upper half, showing the point below which 75% of data points lie. Example: Student Scores (on a Scale of 1 to 10) Let’s look at an example of IQR using student test scores ranging from 1 to 10: Scores = 2, 3, 4, 5, 6, 6, 7, 8, 9, 10 Find Q1 (first quartile): The lower half of the dataset (2, 3, 4, 5, 6) has a median value of 4. Find Q3 (third quartile): The upper half (6, 7, 8, 9, 10) has a median of 8. Calculate the IQR: IQR = 8 − 4 = 4 This IQR tells us that the central 50% of scores lie between 4 and 8 on the 1-10 scale, providing insight into where the middle values are concentrated. While the range (4 to 8) defines the boundaries of the middle 50%, the IQR (4) quantifies how spread out those values are, offering a clearer picture of the data's variability.

I just wanted to share the win here. I've been quiet here lately, but that's because I've been neck deep studying the Microsoft Azure learning path for the AI-102 exam. I took the exam today and passed it. Link to the exam and study materials if anyone else is interested: https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-engineer/?practice-assessment-type=certification P.S. I've used Azure before for 2-3 years and considered myself proficient in it, but AI is new and it felt like learning Azure all over again. Very useful skill to have, imho.