Advanced Statistical Methods in Python
Deepen your analytical capabilities with advanced statistical techniques implemented in Python. Master regression, clustering, and hypothesis testing for robust data insights.
Course Syllabus: Advanced Statistical Methods in Python
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Linear Regression:
Understand the foundational concepts of linear regression, its assumptions, and how to implement it using Python for predictive modeling.
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Multiple Linear Regression:
Extend your knowledge to multiple independent variables, learning how to build and interpret models for more complex relationships in data.
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Linear Regression with sklearn:
Gain practical experience using Scikit-learn, Python’s premier machine learning library, to efficiently perform linear regression analysis.
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Linear Regression – Practical Example:
Apply linear regression techniques to a real-world dataset, solidifying your understanding through a hands-on case study.
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Logistic Regression:
Master logistic regression for binary classification problems, learning how to model probabilities and make predictions for categorical outcomes.
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Cluster Analysis (Basics and Prerequisite):
Begin your journey into unsupervised learning by understanding the basics of cluster analysis and essential prerequisites for its application.
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K-Means Clustering:
Dive into K-Means, a popular clustering algorithm, learning its mechanics, implementation in Python, and how to interpret clustering results.
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Other Types of Clustering:
Explore additional clustering techniques beyond K-Means, broadening your toolkit for different data structures and analytical needs.