## Path overview

In this path, you’ll learn the fundamentals of R and build upon them with more advanced skills. You’ll learn how to use RStudio, applications and tools, tidyverse, DataFrames, tibbles, operators, expressions, and much more — as well as data visualization, graphs, plots, and charts.

Best of all, you’ll learn by doing — you’ll write code and get feedback directly in the browser. You’ll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.

## Key skills

- Programming with R to perform complex statistical analysis of large datasets
- Performing SQL queries and web-scraping to explore and extract data from databases and websites
- Performing efficient data analysis from start to finish
- Building insightful data visualizations to tell stories

## Path outline

###
**Part 1: ** Introduction to R [4 courses]

### Introduction to Data Analysis in R 3h

Objectives- Define R programming syntax
- Define variable use and naming rules
- Perform calculations using arithmetic operators

### Data Structures in R 6h

Objectives- Create a data structure
- Index a data structure
- Perform operations over a data structure

### Control Flow, Iteration, and Functions in R 4h

Objectives- Employ control flow with if-else statements
- Replicate your code using iteration
- Write functions

### Specialized Data Processing in R 4h

Objectives- Manipulate strings from the stringr package
- Manipulate strings from the lubridate package
- Employ the map function from the purrr package

###
**Part 2: ** Data Visualization in R [1 course]

### Introduction to Data Visualization in R 4h

Objectives- Visualize changes over time using line graphs
- Analyze data distributions using histograms
- Compare groups using bar charts and box plots
- Identify the relationships between variables using scatter plots

###
**Part 3: ** Data Cleaning in R [2 courses]

### Introduction to Data Cleaning in R 7h

Objectives- Manipulate DataFrames
- Define relational data
- Resolve missing data
- Reshape data using the tidyr package

### Advanced Data Cleaning in R 6h

Objectives- Employ regular expressions to clean and manipulate text data
- Employ the map and anonymous functions
- Resolve missing data

###
**Part 4: ** Working with Data Sources [4 courses]

### SQL Fundamentals 5h

Objectives- Analyze data using SQL
- Organize data using SQL
- Write SQL queries to estimate summary statistics

### Intermediate SQL in R 4h

Objectives- Query data across multiple tables
- Answer business questions using SQL
- Define table relations

### Introduction to APIs in R 3h

Objectives- Query external data sources using an API
- Query using an API with authentication

### Introduction to Web Scraping in R 3h

Objectives- Scrape data from the web
- Identify tools for complex web pages

###
**Part 5: ** Probability and Statistics [5 courses]

### Introduction to Statistics in R 5h

Objectives- Sample data using simple random sampling, stratified sampling, and cluster sampling
- Measure variables in statistics
- Build, visualize, and compare frequency distribution tables

### Intermediate Statistics in R 2h

Objectives- Summarize a distribution using the mean, the weighted mean, the median, or the mode
- Measure the variability of a distribution using the variance and the standard deviation
- Compare values using z-scores

### Introduction to Probability in R 1h

Objectives- Estimate theoretical and empirical probabilities
- Define the fundamental rules of probability
- Identify combinations and permutations

### Conditional Probability in R 2h

Objectives- Assign probabilities based on conditions
- Assign probabilities based on event independence
- Assign probabilities based on prior knowledge
- Create spam filters using multinomial Naive Bayes

### Hypothesis Testing in R 1h

Objectives- Implement probability density functions
- Create testable hypotheses
- Decide which hypotheses to support based on your data

###
**Part 6: ** Predictive Modeling and Machine Learning in R [2 courses]

### Linear Regression Modeling in R 2h

Objectives- Define predictive modeling
- Build linear regression models
- Interpret linear regression models
- Assess model fit and accuracy

### Introduction to Machine Learning in R 1h

Objectives- Identify a proper machine learning workflow
- Implement the k-nearest neighbors algorithm
- Employ the caret library

###
**Part 7: ** Shiny Applications in R [1 course]

### Introduction to Interactive Web Applications in Shiny 2h

Objectives- Read the structure of a Shiny app
- Program inputs and outputs in a Shiny interface
- Extend your Shiny apps

## The Dataquest guarantee

Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.

We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.

## Master skills faster with Dataquest

### Go from zero to job-ready

Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.

### Build your project portfolio

Build confidence with our in-depth projects, and show off your data skills.

### Challenge yourself with exercises

Work with real data from day one with interactive lessons and hands-on exercises.

### Showcase your path certification

Impress employers by completing a capstone project and certifying it with an expert review.

## Projects in this path

### Project: Install RStudio

Learn how to install and use RStudio, a free and open-source development environment for R.

### Guided Project: Investigating COVID-19 Virus Trends

Learn to combine the skills you learned in this course to perform practical data analysis.

### Guided Project: Creating An Efficient Data Analysis Workflow

Apply control flow, loops and functions to create a reusable data workflow.

### Guided Project: Creating An Efficient Data Analysis Workflow, Part 2

Employ even more programming techniques to create a reusable data workflow.

### Guided Project: Analyzing Forest Fire Data

Use data visualization techniques to explore data on forest fires.