R Programming

Duration

Varies depending on the learning pathway (self-paced, online courses: 3-6 months, bootcamps: 12-24 weeks, degree programs: 2-4 years)

Eligibility

Basic understanding of statistics and data analysis; programming experience (optional)

Fees

Varies depending on the learning pathway (self-paced: free or low-cost resources, online courses: $100-$5000, bootcamps: $5000-$20,000, degree programs: $10,000-$50,000+)

Top Institute

Coursera, edX, Udacity, DataCamp, universities with data science or statistics programs

Median Salary

$96,320 (USD) according to Indeed (as of February 2024)

Top Recruiters

Google, Facebook, Amazon, Netflix, Uber, data science consultancies, research institutions

Job Position

Data Scientist, Statistician, Machine Learning Engineer, Business Analyst, Quantitative Analyst, Research Analyst

Concept

Description

Variables and Data Types

Learn about different data types in R (e.g., numeric, character, logical) and how to assign values to variables. Understanding how to manipulate these types is crucial for data analysis.

Vectors and Lists

Vectors are one-dimensional arrays that can hold numeric, character, or logical data. Lists are collections of elements that can be of different types. Mastering these data structures is essential for data manipulation.

Matrices and Data Frames

Matrices are two-dimensional arrays with elements of the same data type. Data frames are more flexible than matrices and can hold different types of data in each column, similar to a spreadsheet.

Factors

Factors are R's data type for categorical data. Understanding how to use factors is key for data analysis involving categorical variables.

Functions and Control Flow

Learn how to write functions and use control flow statements (if, else, for loops, while loops) to execute code conditionally or repetitively. This is fundamental for automating tasks and writing efficient code.

Data Import and Export

Knowing how to read data from files (CSV, Excel, etc.) and databases, and how to write data back out is crucial for working with external data sources.

Data Manipulation

Skills in data manipulation include filtering, sorting, summarizing, and transforming data sets. Packages like dplyr and data.table enhance these capabilities.

Data Visualization

Visualizing data is key to understanding the underlying patterns and communicating insights. Learn to use ggplot2 and other visualization packages for creating compelling data visualizations.

Statistical Analysis

R is built for statistics, so a solid grasp of statistical tests, models (linear, nonlinear, clustering, etc.), and concepts is essential.

Package Management

Understanding how to install and manage R packages like tidyverse, shiny, caret, etc., expands the tools at your disposal for data analysis and application development.

Version Control with Git and GitHub

Version control is essential for managing changes to your code, collaborating with others, and contributing to open-source projects. Learn how to use Git and GitHub to track your R projects.

Reproducible Research and Markdown

Reproducible research practices ensure that your data analyses can be independently verified. R Markdown allows you to create dynamic analysis documents that combine code, output, and narrative text.

Topic

Description

Introduction to R and RStudio

• Introduction to R and its applications.
• Downloading and installing R and RStudio. 
• Navigating the R environment. 
• Basic syntax: variables, data types, operators, expressions. Working with the R console and scripts.
• Introduction to R packages and package management.

Data Structures in R

• Vectors: creation, manipulation, subsetting. 
• Matrices and data frames: creation, indexing, subsetting. 
• Lists: creation, manipulation, accessing elements.
• Working with different data types: numeric, character, logical, and factors.

Data Input and Output

• Reading data from various sources (CSV, Excel, text files).
• Writing data in different formats.
• Importing and exporting data using R packages.

Data Manipulation and Cleaning

• Data cleaning techniques: handling missing values, outliers, and inconsistencies. 
• Data transformation functions: filtering, sorting, aggregating.
• Data wrangling with the dplyr package.

Control Flow Statements and Functions

• Conditional statements: if, else, else if.
• Looping statements: for, while. 
• Defining and using functions: arguments, return values.

Basic Statistics in R

• Descriptive statistics: measures of central tendency, dispersion, skewness, kurtosis.
• Probability distributions: common distributions and their properties in R. 
• Hypothesis testing: t-tests, chi-square tests, ANOVA.

Data Visualization

• Creating basic plots: scatter plots, bar charts, histograms.
• Customization of plots: color, line styles, annotations.
• Advanced plotting with ggplot2 package.

Introduction to Advanced Topics

• Machine learning fundamentals: classification, regression, model building. 
• Introduction to specific machine learning packages (e.g., caret, tidymodels). 
• Data analysis projects: applying the learned skills to practical problems and real-world datasets.

Introduction to R Programming for Data Science

Data Analysis with R Programming

Data Science: R Basics

Introduction to Probability and Data with R

Data Analysis with R - Capstone Project

Data Science Master Course Program

Job Role

Skills Required

Average Salary

Data Scientist

• Proficiency in R programming
• Statistical analysis
• Machine learning
• Data visualization

$85,000 - $120,000*

Data Analyst

• R programming for data manipulation and analysis
• SQL
• Data visualization using ggplot2

$60,000 - $90,000*

Quantitative Analyst

• R programming for financial modelling
• Statistical analysis
• Risk management

$70,000 - $100,000*

Bioinformatician

• R programming for genomic data analysis
• Bioconductor packages 
• Statistical modeling

$75,000 - $105,000*

Statistician

• R programming for statistical tests and models
• Data interpretation
• Experimental design

$75,000 - $95,000*

Machine Learning Engineer

• R programming for developing machine learning algorithms
• Deep learning
• Natural Language Processing (NLP)

$100,000 - $140,000*

Business Intelligence Analyst

• R programming for business analytics
• SQL
• Dashboard creation with Shiny

$70,000 - $95,000*

Research Scientist

• R programming for data analysis and modeling
• Publication-quality visualization
• Scientific writing skills

$80,000 - $110,000*