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Data Alchemy

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6 contributions to Data Alchemy
Wednesday funny
Wednesday funny
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New comment 7d ago
Wednesday funny
1 like • 7d
That's why understanding client requirements and business is a big part. Business analysts and project managers do this job, but they also often get it wrong 😀
Understanding the Interquartile Range (IQR)
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.
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New comment 7d ago
3 likes • 7d
Nice info. A small thing - shouldn't the median value of lower half of numbers 2, 3, 4, 5, 6 be 4 instead of 4.5 . Please correct me if I am wrong. Thanks.
Just got certified as an AI Engineer
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.
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New comment 6d ago
1 like • 7d
Good accomplishment. Congrats.
Completed Kaggle's Intro to Machine Learning
Completed the course "Intro to Machine Learning" on Kaggle. https://www.kaggle.com/learn/certification/ppiron/intro-to-machine-learning
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New comment 7d ago
2 likes • 7d
Good job. What are you aiming at next?
Completed Python course on Kaggle
Just completed by Python course on Kaggle https://www.kaggle.com/learn/certification/kirtisinghchd/python
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New comment 3d ago
2 likes • 9d
@Erica Goncalves Python is our tool for LLM and related stuff. Grasping it is important and having good knowledge of it can help us provide better and contextual solutions than what 'prompts' offer.
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Kirti Singh
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32points to level up
@kirti-singh-6461
I am Kirti Singh. An IT professional from Chandigarh, India.

Active 2d ago
Joined Nov 5, 2024
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