Unlocking Data Power: Introduction to R Programming
Dive into R, the powerful language for statistical computing and graphics. This course provides a solid foundation for data analysis and visualization.
Course Syllabus: Introduction to R Programming
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Introduction:
Get an overview of R and its ecosystem, understanding its role in data science and statistical analysis.
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Getting started:
Learn to install R and RStudio, navigate the interface, and write your first lines of R code.
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The building blocks of R:
Explore fundamental data structures in R, including vectors, lists, and data frames.
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Vectors and vector operations:
Master vector creation, manipulation, and vectorized operations for efficient data processing.
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Matrices:
Understand matrices, their properties, and operations for working with two-dimensional data structures in R.
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Fundamentals Of Programming With R:
Grasp core programming concepts in R, including control flow (if-else, loops) and functions for logical problem-solving.
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Data frames:
Deep dive into data frames, R’s most popular structure for tabular data, covering creation, subsetting, and manipulation.
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Manipulating data:
Learn essential techniques for cleaning, transforming, and reshaping data in R using various built-in functions and packages.
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Visualizing data:
Create compelling data visualizations using R’s powerful plotting capabilities, including base graphics and ggplot2.
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Exploratory data analysis:
Apply methods for summarizing and visualizing data to uncover patterns, detect anomalies, and test hypotheses.
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Hypothesis Testing:
Understand the principles of hypothesis testing and perform common statistical tests in R to draw data-driven conclusions.
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Linear Regression Analysis:
Learn to build and interpret linear regression models in R to analyze relationships between variables and make predictions.