Udemy – Statistics for Data Science: A Comprehensive Journey

Statistics is the backbone of Data Science, Machine Learning, and AI, yet it remains one of the most misunderstood topics. This course is designed to take you on a clear, structured, and application-driven journey, helping you build strong statistical intuition and hands-on problem-solving skills—without unnecessary math overload.

In this course, you’ll move step by step from fundamental statistical concepts to hypothesis testing and ANOVA, using real-time examples and practical datasets, including water quality data to connect theory with real-world decision-making. Whether you are a student, data science aspirant, working professional, or researcher, this course will give you the statistical confidence needed to analyze data, interpret results, and build reliable AI/ML models.

What You’ll Learn:

• Understand population, samples, and sampling techniques
• Perform descriptive statistical analysis and interpret results
• Apply probability concepts with solved examples
• Work with marginal, joint, and conditional probabilities
• Master Bayes’ Theorem with step-by-step problem solving
• Understand random variables and probability distributions
• Apply Binomial, Uniform, and Normal distributions
• Generate and interpret Normal distributions using sample data
• Understand variance, statistical significance, and hypothesis testing
• Perform t-tests and ANOVA with real examples
• Execute t-tests using Excel
• Develop statistical thinking for Data Science and AI applications

Why This Course is Different:

• Real-world examples (including environmental & water quality data)
• Step-by-step solved problems
• Concept-first approach (no blind formula memorization)
• Hands-on Excel-based statistical testing
• Perfect bridge between statistics and machine learning

Who This Course Is For:

• Aspiring Data Scientists & Machine Learning Engineers
• Students in AI, ML, Data Science, and Engineering
• Professionals transitioning into analytics roles
• Researchers needing strong statistical foundations
• Anyone struggling to understand probability and hypothesis testing

DOWNLOAD LINK: Statistics for Data Science: A Comprehensive Journey

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