Correlation & Covariance for Data Science ll code to CEO #ai

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Welcome to Code to CEO!

This channel is dedicated to empowering aspiring data scientists, analysts, and AI enthusiasts with the statistical foundations essential for data science. In this series, you'll learn practical statistics, from basics to advanced topics, tailored to help you analyze, interpret, and make data-driven decisions with confidence. Perfect for beginners in data science or anyone looking to strengthen their understanding of statistics for machine learning and AI.

Topic List:

• Introduction to Statistics for Data Science

• Types of Data and Levels of Measurement

• Descriptive Statistics: Measures of Central Tendency (Mean, Median, Mode)

• Descriptive Statistics: Measures of Dispersion (Range, Variance, Standard Deviation)

• Understanding Data Distributions

• Probability Basics for Data Science

• Conditional Probability and Bayes' Theorem

• Discrete and Continuous Probability Distributions

• The Normal Distribution and Z-Scores

• Sampling and Sampling Techniques

• Central Limit Theorem and its Importance

• Confidence Intervals and Margin of Error

• Hypothesis Testing Fundamentals

• T-tests, Z-tests, and ANOVA

• Chi-Square Test and Categorical Data Analysis

• Correlation and Causation

• Linear Regression Analysis

• Multivariate Analysis and Multiple Regression

• Introduction to Time Series Analysis

• Statistical Significance and P-Values

• Data Visualization Techniques for Statistics

• Handling Outliers and Missing Data

• Advanced Probability Concepts (Binomial, Poisson Distributions)

• Resampling Methods: Bootstrap and Permutation Tests

• Introduction to Bayesian Statistics for Data Science

This series will give viewers a solid statistical foundation to apply directly in data science projects and machine learning.