Statistical Foundations for Data Science
Learn the core statistical concepts essential for data analysis, including probability, distributions, hypothesis testing, and regression models.
967 courses
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Cholesky Factorization and Practical Applications in R
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Data Wrangling in R with Essential Tidyverse Functions
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Practice Guide to Basic Regression and Data Modeling
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Inference for Linear Regression: Checking Assumptions and Conditions
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Foundational R Syntax for Data Science Beginners
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Linear Regression Essentials and Practice
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Organizing R Projects with Libraries and Working Directories
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Data Import and Verification in R for Beginners
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Data Analysis for API Product Metrics: Cohorts, Clustering, and Regression
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Text Mining in R: Managing Metadata with the tm Package
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Data Science Handbook: Key Concepts and Core Techniques
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Handling Quantitative and Qualitative Data with Dummy Variables
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Linear Regression with One Numerical Explanatory Variable
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R Programming Fundamentals and Core Syntax Practice
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Linear and Logistic Regression in Data Analysis for Beginners
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Geographic Retail Sales Analysis with R
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Confidence Intervals and Statistical Estimation with R
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Analyzing Data Relationships with Correlation Matrices
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Feature Selection and F-Tests in Logistic Regression
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Understanding Variance and Covariance in Linear Regression
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Cross-Validation in R with tidymodels
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Linear Regression with Categorical Variables in R
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Practice and Test Your Foundational Data Skills in R
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Analyzing Regression Models: Observed, Fitted, and Residual Values
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