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Day 12: Mastering the Normal Distribution for Data Science
access_time 2025-01-12T10:43:19.52Z face Nerchuko
Day 12: Mastering the Normal Distribution for Data Science Understanding the foundation of normal distribution is essential for any data scientist, as it forms the backbone of statistical analysis and machine learning algorithms. What is a Normal Distribution? The normal distribution, also called t...
Day 11: Bayes' Theorem: The Mathematics of Updating Beliefs
access_time 2025-01-12T09:44:26.707Z face Nerchuko
Day 11: Bayes' Theorem: The Mathematics of Updating Beliefs A Deep Dive into Probability and Belief Updates What is Bayes' Theorem? Basic Definition Bayes' Theorem calculates the probability of an event based on prior knowledge of conditions related to the event. It's expressed as: P(A|B) = [P(B|A) ...
Day 10: Understanding Probability: From Basics to Conditional
access_time 2025-01-12T09:17:53.952Z face Nerchuko
Day 10: Understanding Probability: From Basics to Conditional A Deep Dive into Probability with Real-Life Examples What is Probability? Basic Definition Probability is the likelihood or chance of an event occurring. It's the ratio of: Probability = Favorable outcomes / Total possible outcomes Probab...
Day 9: Populations vs Sample
access_time 2025-01-12T08:21:09.773Z face Nerchuko
Day 9: Populations vs Sample From Theory to Practice: Making Sense of Survey Data January 12, 2025 Population vs Sample: The Fundamentals What is a Population? Complete set of all subjects/items of interest Example: All students in a university (50,000 students) Often too large or impractical to stu...
Day 8: Correlation Analysis: Theory to Practice
access_time 2025-01-07T16:07:39.625Z face Nerchuko
Day 8: Correlation Analysis: Theory to Practice Understanding Statistical Relationships Through Real-World Applications January 7, 2025 1. Pearson Correlation Coefficient Mathematical Foundation r = Σ((x - μx)(y - μy)) / (σx σy) Key Properties: Range: -1 to +1 -1: Perfect negative correlation 0: No ...