Often in data science, you'll encounter fascinating data that is spread across multiple tables. This course will teach you the skills you'll need to join multiple tables together to analyze them in combination. You'll practice your skills using a fun dataset about LEGOs from the Rebrickable website. The dataset contains information about the sets, parts, themes, and colors of LEGOs, but is spread across many tables. You'll work with the data throughout the course as you learn a total of six different joins! You'll learn four mutating joins: inner join, left join, right join, and full join, and two filtering joins: semi join and anti join. In the final chapter, you'll apply your new skills to Stack Overflow data, containing each of the almost 300,000 Stack Oveflow questions that are tagged with R, including information about their answers, the date they were asked, and their score. Get ready to take your dplyr skills to the next level!

Learn to combine data across multiple tables to answer more complex questions with dplyr.

Learn to combine data across multiple tables to answer complex questions with dplyr. You'll learn 6 different joins including inner, full, anti, and more.

Joining Data with dplyr

In this introduction to R course, you'll master the basics of this widely used open source language&mdash;including vectors, factors, lists, and data frames. With the coding skills you'll gain in this course, you'll be ready to undertake your own data analysis in R. There are millions of R users worldwide, cementing it as a leading programming language in statistics and data science. More and more organizations are using R, so begin your coding journey in one of DataCamp's most popular courses today!

Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.

DataCamp's Into to R training course teaches you how to use R programming for data science at your own pace with video tutorials & interactive challenges.

Introduction to R Online

Introduction to R

Intermediate R is the next stop on your journey in mastering the R programming language. In this R training, you will learn about conditional statements, loops, and functions to power your own R scripts. Next, make your R code more efficient and readable using the apply functions. Finally, the utilities chapter gets you up to speed with regular expressions in R, data structure manipulations, and times and dates. This course will allow you to take the next step in advancing your overall knowledge and capabilities while programming in R.

Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.

Discover conditional statements, loops, and functions to power your own R scripts, and learn to make your R code more efficient using the apply functions.

Intermediate R

Python is a general-purpose programming language that is becoming ever more popular for data science. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, this course focuses on Python specifically for data science. In our Introduction to Python course, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. Start DataCamp’s online Python curriculum now.

Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.

Discover how to use Python for data science, learning about the ways to store and manipulate data in the environment to begin conducting your own analyses.

Introduction to Python

Many phenomena in our day-to-day lives, such as the movement of stock prices, are measured in intervals over a period of time. Time series analysis methods are extremely useful for analyzing these special data types. In this course, you will be introduced to some core time series analysis concepts and techniques.

Learn the core techniques necessary to extract meaningful insights from time series data.

Learn the core techniques necessary to extract meaningful insights from time series data, including WN and RW models, correlation analysis, and more. 

Time Series Analysis in R

In this course, you will take a deeper dive into the wide range of data formats out there. More specifically, you'll learn how to import data from relational databases and how to import and work with data coming from the web. Finally, you'll get hands-on experience with importing data from statistical software packages such as SAS, STATA, and SPSS.

Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.

Learn how you can use R to parse data in any format, whether that data is from flat files, statistical software, databases, or data right from the web.

Intermediate Importing Data in R

Where should you buy a house to get the most value for your money? Your first step might be to make a map, but spatial analysis in R can be intimidating because of the complicated objects the data often live in.This course will introduce you to spatial data by starting with objects you already know about, data frames, before introducing you to the special objects from the sp and raster packages used to represent spatial data for analysis in R. You'll learn to read, explore, and manipulate these objects with the big payoff of being able to use the tmap package to make maps.By the end of the course you will have made maps of property sales in a small town, populations of the countries of the world, the distribution of people in the North East of the USA, and median income in the neighborhoods of New York City.

Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.

Visualizing Geospatial Data in R

The role of a data scientist is to turn raw data into actionable insights. Much of the world's raw data—from electronic medical records to customer transaction histories—lives in organized collections of tables called relational databases. To be an effective data scientist, you must know how to wrangle and extract data from these databases using a language called SQL . This course teaches syntax in SQL shared by many types of databases, such as PostgreSQL, MySQL, SQL Server, and Oracle. This course teaches you everything you need to know to begin working with databases today!

Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.

Learn the basics of syntax in SQL, how to extract and wrangle data, and use querying tables in relational databases like MySQL, SQL Server, and PostgreSQL.

Introduction to SQL

In this course, you will strengthen your knowledge of time series topics through interactive exercises and interesting datasets. You’ll explore a variety of datasets about Boston, including data on flights, weather, economic trends, and local sports teams.

Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.

Strengthen your knowledge of the topics covered in Manipulating Time Series in R with xts and zoo using real case study data.

Case Studies: Manipulating Time Series Data in R

Probability is the study of making predictions about random phenomena. In this course, you'll learn about the concepts of random variables, distributions, and conditioning, using the example of coin flips. You'll also gain intuition for how to solve probability problems through random simulation. These principles will help you understand statistical inference and can be applied to draw conclusions from data.

In this course, you'll learn about the concepts of random variables, distributions, and conditioning.

Learn about random variables, distributions and conditioning, while gaining intuition for how to solve probability problems through random simulation.

Foundations of Probability in R

Character strings can turn up in all stages of a data science project. You might have to clean messy string input before analysis, extract data that is embedded in text or automatically turn numeric results into a sentence to include in a report. Perhaps the strings themselves are the data of interest, and you need to detect and match patterns within them. This course will help you master these tasks by teaching you how to pull strings apart, put them back together and use stringr to detect, extract, match and split strings using regular expressions, a powerful way to express patterns.

Learn how to pull character strings apart, put them back together and use the stringr package.

In this course you will learn how to pull character strings apart, put them back together and use the stringr package.

String Manipulation with stringr in R

Forecasting involves making predictions about the future. It is required in many situations: deciding whether to build another power generation plant in the next ten years requires forecasts of future demand; scheduling staff in a call centre next week requires forecasts of call volumes; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments), or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an important aid to effective and efficient planning. This course provides an introduction to time series forecasting using R.

Learn how to make predictions about the future using time series forecasting in R.

Make predictions about the future using time series forecasting in R. You will learn how to plot data, observe patterns, benchmark, forecast, and more.

Forecasting in R

Most of the useful data in the world, from economic data to news content to geographic information, lives somewhere on the internet - and this course will teach you how to access it.

You'll explore how to work with APIs (computer-readable interfaces to websites), access data from Wikipedia and other sources, and build your own simple API client. For those occasions where APIs are not available, you'll find out how to use R to scrape information out of web pages. In the process you'll learn how to get data out of even the most stubborn website, and how to turn it into a format ready for further analysis. The packages you'll use and learn your way around are rvest, httr, xml2 and jsonlite, along with particular API client packages like WikipediR and pageviews.

Learn how to efficiently import data from the web into R.

Discover how to work with APIs, build your own API client, and access data from Wikipedia and other sources by using R to scrape information from web pages.

Working with Web Data in R

From a machine learning perspective, regression is the task of predicting numerical outcomes from various inputs. In this course, you'll learn about different regression models, how to train these models in R, how to evaluate the models you train and use them to make predictions.

In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

Supervised Learning in R: Regression

There has never been a better time to use R for spatial analysis! The sf package has made working with vector data in R a breeze and the raster package provides a set of powerful and intuitive tools to work gridded data like satellite imagery. Instead of the painful process of performing your spatial analysis in GIS systems like ArcGIS or QGIS and then shuffling your results into another system for analysis, you can move your entire spatial analysis workflow into R. In this course you will learn why the sf package is rapidly taking over spatial analysis in R. You will read in spatial data, manipulate vectors using the dplyr package and learn how to work with coordinate reference systems. You'll also learn how to perform geoprocessing of vectors including buffering, spatial joins, computing intersections, simplifying and measuring distance. With rasters, you will aggregate, reclassify, crop, mask, and extract. The last chapter of the course is devoted to showing you how to make maps in R with the ggplot2 and tmap packages and performing a fun mini-analysis that brings together all your new skills.

Analyze spatial data using the sf and raster packages. 

Learn how to work with vector data in R using the SF package. You'll learn how to perform geoprocessing of vectors like buffering, spatial joins, and more

Spatial Analysis with sf and raster in R

The Unix command line has survived and thrived for almost 50 years because it lets people do complex things with just a few keystrokes. Sometimes called "the universal glue of programming," it helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds that may be halfway around the world. This course will introduce its key elements and show you how to use them efficiently.

The Unix command line helps users combine existing programs in new ways, automate repetitive tasks, and run programs on clusters and clouds. 

Discover the Unix command line and how to use it to combine existing programs, automate repetitive tasks, and run programs on clusters and clouds in Shell.

Introduction to Shell

Bayesian data analysis is an approach to statistical modeling and machine learning that is becoming more and more popular. It provides a uniform framework to build problem specific models that can be used for both statistical inference and for prediction. This course will introduce you to Bayesian data analysis: What it is, how it works, and why it is a useful tool to have in your data science toolbox.

Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.

Learn what Bayesian data analysis is and how to use it for business modeling focusing on cash flows, investments, annuities, and saving for retirement in R

Fundamentals of Bayesian Data Analysis

Fundamentals of Bayesian Data Analysis in R

Although you might not have realized, processes take up an indispensable role in our daily lives. Your actions and those of others generate an extensive amount of data. Whether you are ordering a book, a train crosses a red light, or your thermostat heats your bathroom, every second millions of events are taking place which are stored in data centers around the world. These enormous sets of event data can be used to gain insight into processes in a virtually unlimited range of fields. However, the analysis of this data requires its own set of specific formats and techniques. This course will introduce you to process mining with R and demonstrate the different steps needed to analyze business processes.

Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data. 

Business Process Analytics in R

The Bayesian approach to statistics and machine learning is logical, flexible, and intuitive. In this course, you will engineer and analyze a family of foundational, generalizable Bayesian models. These range in scope from fundamental one-parameter models to intermediate multivariate & generalized linear regression models.  The popularity of such Bayesian models has grown along with the availability of computing resources required for their implementation. You will utilize one of these resources - the rjags package in R. Combining the power of R with the JAGS (Just Another Gibbs Sampler) engine, rjags provides a framework for Bayesian modeling, inference, and prediction.

In this course, you'll learn how to implement more advanced Bayesian models using RJAGS.

Bayesian Modeling with RJAGS

This course will teach you to leverage the tools in the "tidyverse" to generate, explore, and evaluate machine learning models. Using a combination of tidyr and purrr packages, you will build a foundation for how to work with complex model objects in a "tidy" way. You will also learn how to leverage the broom package to explore your resulting models. You will then be introduced to the tools in the test-train-validate workflow, which will empower you evaluate the performance of both classification and regression models as well as provide the necessary information to optimize model performance via hyperparameter tuning.

Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.

Machine Learning in the Tidyverse

Linear algebra is one of the most important set of tools in applied mathematics and data science. In this course, you’ll learn how to work with vectors and matrices, solve matrix-vector equations, perform eigenvalue/eigenvector analyses and use principal component analysis to do dimension reduction on real-world datasets. All analyses will be performed in R, one of the world’s most-popular programming languages.

This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.


This data science course is an introduction to linear algebra. Learn how to use matrix-vector equations, perform eigenvalue/eigenvector analyses and pca.



Linear Algebra for Data Science in R

Time series data is fun, but challenging. When ordering matters, your datasets get large, and timestamp precision differences can foil your merges, building reliable data processing pipelines requires a principled approach with the right tools. Enter data.table! Its expressive syntax will make your code powerful without sacrificing readability and its support for in-place operations will make your code super fast. Learn how to master time series data in data.table with this course!

Master time series data using data.table in R. 

Learn how to build reliable data processing pipelines for time series data using data.table. You'll see what the tool is, how to use it, and get to test it.

Time Series with data.table in R

You’ve already used SQL to query data from databases. But did you know that there's a lot more you can do with databases? You can model different phenomena in your data, as well as the relationships between them. This gives your data structure and consistency, which results in better data quality. In this course, you'll experience this firsthand by working with a real-life dataset that was used to investigate questionable university affiliations. Column by column, table by table, you'll get to unlock and admire the full potential of databases. You'll learn how to create tables and specify their relationships, as well as how to enforce data integrity. You'll also discover other unique features of database systems, such as constraints.

Learn how to create one of the most efficient ways of storing data - relational databases! 

Learn how to create tables and specify their relationships in SQL, as well as how to enforce data integrity and other unique features of database systems.

Introduction to Relational Databases in SQL

Being able to write your own functions makes your analyses more readable, with fewer errors, and more reusable from project to project. Function writing will increase your productivity more than any other skill!  In this course you'll learn the basics of function writing, focusing on the arguments going into the function and the return values. You'll be writing useful data science functions, and using real-world data on Wyoming tourism, stock price/earnings ratios, and grain yields.

Take your R skills up a notch by learning to write efficient, reusable functions. 

Learn how to write efficient, reusable functions in R by understanding the basics of function writing, focusing on the arguments and the return values.

Introduction to Writing Functions in R

Shiny is an R package that makes it easy to build highly interactive web apps directly in R. Using Shiny, data scientists can create interactive web apps that allow your team to dive in and explore your data as dashboards or visualizations. If you want to bring your data to life, Shiny is the way to go! Using data about baby names, food ingredients, and UFO sightings, you'll build a variety of different Shiny apps that leverage different inputs and outputs. You’ll also learn the basics of reactive expressions. By the end of this course, you’ll have the Shiny skills you need to build your first app in R.

Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations. 

Building Web Applications with Shiny in R

Analyzing data that comes in tables is fun. But what if the things that we find most interesting are not available as a neatly organized dataset but in plain text? Do not despair: In this course, you'll learn everything you need to know to create powerful regular expressions that will help you find all the information you need for your analyses from just a blob of text. But not only that. Using the concept of string distances you will learn to work even with text that contains typos or scanning errors, as you will be able to match them to their correct counterparts from other data sources (record linkage). As a learning material, we will analyze real documents about box office figures in Swiss cinemas.

Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.

Learn how to manipulate text data, analyze it identify errors, and more by knowing how to create powerful regular expressions and string distances in R.

Intermediate Regular Expressions in R

R Markdown is an easy to use formatting language you can use to reveal insights from data and author your findings as a PDF, HTML file, or Shiny app. In this course, you'll learn how to create and modify each element of a Markdown file, including the code, text, and metadata. You'll analyze data with dplyr, create visualizations with ggplot2, and author your analyses and plots as reports. You’ll gain hands-on experience of building reports as you work with real-world data from the International Finance Corporation (IFC)—learning how to efficiently organize reports using code chunk options, create lists and tables, and include a table of contents. By the end of the course, you'll have the skills you need to add your brand’s fonts and colors using parameters and Cascading Style Sheets (CSS), to make your reports stand out.

R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.

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