“I was immensely impressed by how well the math is taught on Dataquest, anyone can learn it. Only the most important concepts are used so you don’t waste time.”

Data Analyst Consultant @Fractal

## 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

### 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

### 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

### 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

### Introduction to SQL and Databases 5h

Objectives
• Define the structure of SQL
• Create basic queries to extract data from tables in a database
• Define databases
• Identify different versions of SQL
• Write good SQL code

### Summarizing Data in SQL 3h

Objectives
• Employ SQL to compute statistics
• Provide statistics by group
• Filter results over groups

### Combining Tables in SQL Course 3h

Objectives
• Combine tables using inner joins
• Employ different types of joins
• Employ other SQL clauses with joins
• Join on complex conditions
• Employ set operators like UNION and EXCEPT

### Querying Databases with SQL and R 1h

Objectives
• Connect to a SQLite database using R
• Query a SQLite database using R
• Retrieve a subset of data

### SQL Subqueries 6h

Objectives
• Nest a query inside another query
• Employ different types of subqueries
• Employ common table expressions
• Scale your project with complex queries

### Window Functions in SQL 5h

Objectives
• Set up a frame for window functions
• Compute running aggregations with aggregate window functions
• Explore rank window functions
• Apply distribution window functions
• Use offset window functions

### 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

### 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 3h

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

### Linear Regression Modeling in R 3h

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

### Introduction to Machine Learning in R 2h

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

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

Objectives
• Read the structure of a Shiny app
• Program inputs and outputs in a Shiny interface

## 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 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.

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.

### Plus 12 more projects

Build your project portfolio with the Data Analyst in Python path.

97%
of learners recommend
4.9
Dataquest rating on
G2Crowd and SwitchUp
\$30k
Average salary boost
for learners who complete a path

### Aaron Melton

“Dataquest starts at the most basic level, so a beginner can understand the concepts. I tried learning to code before, using Codecademy and Coursera. I struggled because I had no background in coding, and I was spending a lot of time Googling. Dataquest helped me actually learn.”

### Jessica Ko

#### Machine Learning Engineer at Twitter

“I liked the interactive environment on Dataquest. The material was clear and well organized. I spent more time practicing then watching videos and it made me want to keep learning.”

### Victoria E. Guzik

#### Associate Data Scientist at Callisto Media

“I really love learning on Dataquest. I looked into a couple of other options and I found that they were much too handhold-y and fill in the blank relative to Dataquest’s method. The projects on Dataquest were key to getting my job. I doubled my income!”

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