Statistical Analysis in R - R Programming For Data Science | Lecture Notes - YNetStudyHub
R Programming For Data Science

Statistical Analysis in R

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Cover statistical analysis techniques in R, including descriptive statistics, hypothesis testing, regression analysis, and ANOVA. Explore how to perform statistical tests and interpret the results using R.

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Statistical Analysis in R Introduction to R R is a programming language and environment specifically designed for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques, making it a popular choice for data analysis. Data Import and Manipulation R allows users to import data from various sources such as CSV files, databases, and web APIs. Data...…

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Unit Syllabus 28 Topics
1
Introduction to R Programming
Data Types and Data Structures in R
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Data Manipulation with dplyr
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Data Visualization with ggplot2
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Working with External Data Sources
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Statistical Analysis with R
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Machine Learning Basics in R
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Text Mining and Natural Language Processing in R
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Time Series Analysis in R
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R Markdown for Reproducible Research
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Introduction to R Programming
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Data Types and Data Structures in R
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Control Structures and Functions in R
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Data Import and Export in R
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Data Wrangling and Cleaning
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Data Visualization with ggplot2
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Statistical Analysis with R
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Machine Learning with R
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Reporting and Presenting Results
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Best Practices and Advanced Topics
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Introduction to R Programming
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Data Manipulation in R
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Data Visualization with ggplot2
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Statistical Analysis in R
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Working with APIs and Web Scraping in R
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Machine Learning Basics in R
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Text Mining and Natural Language Processing in R
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Shiny App Development
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