Correlations, Association & Hypothesis Testing (with Python)

🚀 Course Title: Correlations, Association & Hypothesis Testing (with Python)

🎓 Headline: Master Statistical Analysis with Python: Dive into Correlations, Associations, and Hypothesis Testing!

Unlock the Secrets of Data Science with Python 🐍

Data scientists are often tasked with the challenge of understanding and assessing the relationships between variables. This course, designed by seasoned data scientist Viani D.B., is your gateway to mastering the fundamentals of correlation, association, and hypothesis testing within the context of Python programming.

Why This Course? 🤔

• Foundation Building: Whether you're just starting out or looking to solidify your understanding, this course provides a comprehensive overview of statistical associations with practical Python applications.
• Real-World Skills: Learn through real-world datasets and apply your knowledge directly in Python, enhancing your analytical toolkit.
• Expert Guidance: Viani D.B., an experienced leader in data science projects, shares insights gleaned from a wealth of industry experience.

Course Structure:

1. Quantifying Associations for Numerical Variables 📊
   • Explore covariances and correlations, understanding their implications and how to measure them effectively.
2. Assessing Categorical Associations 🗣️
   • Delve into the world of categorical data, learning techniques like chi-squared tests to evaluate associations between nominal variables.
3. Mixed Variable Analyses 📈
   • Combine numerical and categorical data to uncover insights through robust statistical methods.

Hands-On Learning:

• Interactive Sessions: Apply concepts learned in real-time using Python, ensuring a deeper understanding of the methods and tools.
• Practical Dataset Application: Gain experience working with datasets, from data collection to result interpretation.
• Quizzes for Reinforcement 🎮
  • End-of-section quizzes help cement key concepts and ensure you're ready to apply your knowledge.

Key Learnings:

• Master statistical metrics like covariances, correlations, t-tests, Chi-squared tests, ANOVA, and F-test with Python.
• Understand when and how to use these tests, including satisfying the underlying assumptions for each.
• Develop a clear and coherent understanding of hypothesis testing within the context of data science.

By completing this course, you will:

• Have a solid grasp of the statistical concepts that are essential for any data scientist's repertoire.
• Be equipped with the ability to choose and apply the appropriate statistical tests.
• Confidently interpret results in a meaningful way, driving actionable insights from your analyses.

🎓 Join Viani D.B. on this journey through the world of correlations, associations, and hypothesis testing with Python. Equip yourself with the skills to extract meaningful patterns from data and become an indispensable asset in any data science team!