Tomás Aragón’s Post

Master the art of research with our complete manual, "All About Quantitative Research." Discover the tricks to composing top-notch research queries, automating like a champ, and acing data accuracy. Prepare to transform your quantitative research - snag your guide here: https://bit.ly/3yUwVQJ #MarketResearch #QuantitativeResearch #DataInsights #ResearchTips

Whoever says charts cannot be used to help time investments, is just wrong. Charts represent human behavior, in graphic form. And human behavior has patterns that repeat, over & over, through the years... Also, the "technical analysis vs. fundamental analysis" argument is misguided. You need to use BOTH.

Great article: frequent mistakes people make in interpreting p-values, confidence intervals and statistical power and how to correctly interpret. Article 👇 https://t.co/uq1sdJVeeb

Pre-training on specialized knowledge graphs vs fine tuning (great for efficiency) reduces hallucinations. Rate of experimentation is great to see hear. The yet to be built knowledge graphs will unlock large step function improvements. https://lnkd.in/gwpmK-Au

📢Missed our recent webinar on "Optimizing #StatisticalPower: Essential Strategies for #SampleSize Determination"? Don't worry! Please watch the full recording and gain valuable insights into key strategies for robust study designs and reliable results. 🎥

Day-23 🚀 Today's Problem was all about finding the number of digit '1' in a range. I solved trailing zeroes by counting factors. Similarly, here I count how many times '1' appears between 1 and n using digit-place analysis. 🧠 Approach: 💡 🔢 Break n into parts: Higher digits: Left of the current digit. Current digit: The one you're counting. Lower digits: Right of the current digit. Counting logic: If the current digit is 0, count based on higher digits. If the current digit is 1, count higher + lower + 1. If the current digit > 1, count all possible combinations. 🌀 Iterate through place values (ones, tens, hundreds). Complexity: ⏱ Time: O(log n) 🧮 Space: O(1)

In this video, I demonstrated how missing values can be handled in a scientific and acceptable way

Unlocking the Secrets of Distance Metric Learning for Outlier Detection 🚀✨ Have you ever wondered how we can identify unusual data points lurking in vast datasets? Outlier detection is crucial for making sense of data and ensuring reliable insights. One powerful approach to this problem is Distance Metric Learning

Data can be powerful... if you unferstand what it is represent by you! Human brsin process visual information than write- up.

This simple graph can unlock valuable insights when analyzed correctly.