Data Science with R Syllabus

Table of Content

R-Programming basics
• R - Overview
• R - Environment Setup
• R - Basic Syntax
• R - Data Types
• R - Variables
• R - Operators
• R - Decision Making
• R - Loops
• R - Functions
• R - Strings R - Vectors R - Lists
• R - Matrices
• R - Arrays
• R - Factors
• R - Data Frames
• R - Packages
• R - Data Reshaping

R - Data Interfaces
• R - CSV Files
• R - Excel Files
• R - Binary Files
• R - XML Files
•R - JSON Files
• R - Web Data
•R - Database

R - Charts & Graphs
• R - Pie Charts R - Bar Charts R - Boxplots
• R - Histograms R - Line Graphs R - Scatterplots

R - Statistics Analysis
• R - Different types of data
• R -Data summarization
• R -Frequency table
• R -Frequency Distributions
• R -Histogram
• R -Measures of central tendency and dispersion
• R -Skewness and kurtosis
• R -Basic Probability
• R -Conditional Probability
• R -Normal Distribution
• R -Sampling methods
• R -Point and Interval estimation
• R -Central Limit Theorem
• R -Nul and alternative hypothesis
• R -Level of significance
• R -P value
• R -Types of errors
• R -Hypothesis Testing
• R -Simple and Multiple Linear Regression
• R -ANOVA, Interpretation of coefficients
• R -Dummy Variables
• R -Residual Analysis
• R -Outliers
• R -Logistic Regression

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