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

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

Begin your journey into Data Science! Even if you've never written a line of code in your life, you'll be able to follow this course and witness the power of Python to perform Data Science. You'll use data to solve the mystery of Bayes, the kidnapped Golden Retriever, and along the way you'll become familiar with basic Python syntax and popular Data Science modules like Matplotlib (for charts and graphs) and pandas (for tabular data).

Dive into data science using Python and learn how to effectively analyze and visualize your data. No coding experience or skills needed.

Dive into data science using Python and learn how to effectively analyze and visualize your data using Pandas and Matplotlib. No coding experience needed.

Introduction to Data Science in Python

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

Learning Python is crucial for any aspiring data science practitioner. Learn to visualize real data with Matplotlib's functions and get acquainted with data structures such as the dictionary and the pandas DataFrame. After covering key concepts such as boolean logic, control flow, and loops in Python, you'll be ready to blend together everything you've learned to solve a case study using hacker statistics.

Level up your data science skills by creating visualizations using Matplotlib and manipulating DataFrames with pandas.

In this intermediate Python course, learn to visualize real data with Matplotlib's functions and get to know new data structures such as the pandas DataFrame.

Intermediate Python

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

It's time to push forward and develop your Python chops even further. There are tons of fantastic functions in Python and its library ecosystem. However, as a data scientist, you'll constantly need to write your own functions to solve problems that are dictated by your data. You will learn the art of function writing in this first Python Data Science Toolbox course. You'll come out of this course being able to write your very own custom functions, complete with multiple parameters and multiple return values, along with default arguments and variable-length arguments. You'll gain insight into scoping in Python and be able to write lambda functions and handle errors in your function writing practice. And you'll wrap up each chapter by using your new skills to write functions that analyze Twitter DataFrames.

Learn the art of writing your own functions in Python, as well as key concepts like scoping and error handling.

Python Data Science Toolbox (Part 1)

In this second Python Data Science Toolbox course, you'll continue to build your Python data science skills. First, you'll learn about iterators, objects you have already encountered in the context of for loops. You'll then learn about list comprehensions, which are extremely handy tools for all data scientists working in Python. You'll end the course by working through a case study in which you'll apply all the techniques you learned in both parts of this course.

Continue to build your modern Data Science skills by learning about iterators and list comprehensions.

Python Data Science Toolbox (Part 2)

This is an introduction to the programming language R, focused on a powerful set of tools known as the Tidyverse. You'll learn the intertwined processes of data manipulation and visualization using the tools dplyr and ggplot2. You'll learn to manipulate data by filtering, sorting, and summarizing a real dataset of historical country data in order to answer exploratory questions. You'll then learn to turn this processed data into informative line plots, bar plots, histograms, and more with the ggplot2 package. You’ll get a taste of the value of exploratory data analysis and the power of Tidyverse tools. This is a suitable introduction for those who have no previous experience in R and are interested in performing data analysis.

Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.

Discover the fundamentals of the Tidyverse, and learn all about renaming and reordering variables, while becoming familiar with binomial distribution.

Introduction to the Tidyverse

In this interactive tutorial, you will learn how to perform sophisticated dplyr techniques to carry out your data manipulation with R. First you will master the five verbs of R data manipulation with dplyr: select, mutate, filter, arrange and summarise. Next, you will learn how you can chain your dplyr operations using the pipe operator of the magrittr package. In the final section, the focus is on practicing how to subset your data using the group_by function, and how you can access data stored outside of R in a database. All said and done, you will be familiar with data manipulation tools and techniques that will allow you to efficiently manipulate data.

Master techniques for data manipulation using the select, mutate, filter, arrange, and summarise functions in dplyr.

Learn how to manipulate data in R with the easy and  intuitive Dplyr syntax--from summarizing to subsetting data.--from our leading course taught by RStudio. 

Manipulate Data in R

Data Manipulation in R with dplyr

Functions are a fundamental building block of the R language. You've probably used dozens (or even hundreds) of functions written by others, but in order to take your R game to the next level, you'll need to learn to write your own functions. This course will teach you the fundamentals of writing functions in R so that, among other things, you can make your code more readable, avoid coding errors, and automate repetitive tasks.

Learn the fundamentals of writing functions in R so you can make your code more readable and automate repetitive tasks.

This course will teach you the fundamentals of writing functions in R so that you can make your code more readable, avoid coding errors, and automate tasks.

R Functions

Writing Functions in R

Version control is one of the power tools of programming. It allows you to keep track of what you did when, undo any changes you decide you don't want, and collaborate at scale with other people. This course will introduce you to Git, a modern version control tool that is very popular with data scientists and software developers, and show you how to use it to get more done in less time and with less pain.

This course is an introduction to version control with Git for data scientists. 

Discover Git, a popular and modern version control tool for data scientists, and how you can use it work more efficiently, quickly, and with fewer errors.

Introduction to Git

Importing data into R should be the easiest step in your analysis. Unfortunately, that is almost never the case. Data can come in many formats, ranging from .csv and text files, to statistical software files, to databases and HTML data. Knowing which approach to use is key to getting started with the actual analysis.
In this course, you’ll start by learning how to read .csv and text files in R. You will then cover the readr and data.table packages to easily and efficiently import flat file data. After that, you will learn how to read .xls files in R using readxl and gdata.

In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.

Learn to read .xls, .csv, and text files in R using readxl and gdata, before learning how to use readr and data.table packages to import flat file data.

Introduction to Importing Data in R

The ability to produce meaningful and beautiful data visualizations is an essential part of a data scientist skill set. This course, the first R data visualization course in the series, introduces you to the principles of good visualizations and the grammar of graphics plotting concepts implemented in the ggplot2 package. ggplot2 has become the go-to tool for flexible and professional plots in R. We’ll examine the first three essential layers for making a plot: data, aesthetics, and geometries. By the end of the course you will be able to make complex exploratory plots.

Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.

Recommended by ggplot2 author Hadley Wickham, this online course teaches you how to create meaningful data visualizations in R with ggplot2 at your own pace

Data Visualization with ggplot2 (Part 1)

Learn how to write a data report quickly and effectively with the R Markdown package, and share your results with your friends, colleagues or the rest of the world.  Learn how you can author your own R Markdown reports, and how to automate the reporting process so that you have your own reproducible reports. By the end of the interactive data analysis reporting tutorial, you will be able to generate reports straight from your R code, documenting your work — and its results — as an HTML, pdf, slideshow or Microsoft Word document.

Learn to create interactive analyses and automated reports with R Markdown.

Learn how to generate & write reports from your R code with the R Markdown package through this tutorial taught by RStudio. Learn at your own pace today!

Reporting with R Markdown - Online Tutorial

Reporting with R Markdown

It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time actually analyzing it. For this reason, it is critical to become familiar with the data cleaning process and all of the tools available to you along the way. This course provides a very basic introduction to cleaning data in R using the tidyr, dplyr, and stringr packages. After taking the course you'll be able to go from raw data to awesome insights as quickly and painlessly as possible!

Learn to explore your data so you can properly clean and prepare it for analysis.

DataCamp's course teaches you to clean data in R so you can turn raw data into valuable insights quicker. See why over 800,000 data scientists use DataCamp!

Cleaning Data In R

Cleaning Data in R

This online machine learning course is perfect for those who have a solid basis in R and statistics, but are complete beginners with machine learning. After a broad overview of the discipline's most common techniques and applications, you'll gain more insight into the assessment and training of different machine learning models. The rest of the course is dedicated to a first reconnaissance with three of the most basic machine learning tasks: classification, regression and clustering.

Learn to train and assess models performing common machine learning tasks such as classification and clustering.

DataCamp's intro to machine learning with R online tutorial teaches you about different machine learning models & tasks. Learn at your own pace today!

Introduction to Machine Learning

This course builds on what you learned in Data Manipulation in R with dplyr by showing you how to combine data sets with dplyr's two table verbs. In the real world, data comes split across many data sets, but dplyr's core functions are designed to work with single tables of data. In this course, you'll learn the best ways to combine data sets into single tables. You'll learn how to augment columns from one data set with columns from another with mutating joins, how to filter one data set against another with filtering joins, and how to sift through data sets with set operations. Along the way, you'll discover the best practices for building data sets and troubleshooting joins with dplyr. Afterwards, you’ll be well on your way to data manipulation mastery!

This course will show you how to combine data sets with dplyr's two table verbs.

Using dplyr, you'll learn how to augment columns with mutating joins, filter a data set against another with filtering joins, and how to sift through data sets.

Join Data in R

Joining Data in R with dplyr

Shiny is an R package that makes it easy to build interactive web apps straight from R. Shiny combines the computational power of R with the interactivity of the modern web. This course will take you from R programmer to Shiny developer. If you want to take a fresh, interactive approach to telling your data story, let users interact with your data and your analysis, and do it all with R, dive in!

Build interactive web apps straight from R with shiny!

This course will show you how to use the shiny package in R to build interactive web applications with no HTML or JavaScript.

Building Web Applications in R with Shiny

This beginner-level introduction to machine learning covers four of the most common classification algorithms. You will come away with a basic understanding of how each algorithm approaches a learning task, as well as learn the R functions needed to apply these tools to your own work.

In this course you will learn the basics of machine learning for classification.

Supervised Learning in R: Classification

When your dataset is represented as a table or a database, it's difficult to observe much about it beyond its size and the types of variables it contains. In this course, you'll learn how to use graphical and numerical techniques to begin uncovering the structure of your data. Which variables suggest interesting relationships? Which observations are unusual? By the end of the course, you'll be able to answer these questions and more, while generating graphics that are both insightful and beautiful.

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

Learn how to use graphical and numerical techniques for exploratory data analysis while generating insightful and beautiful graphics in R.

Exploratory Data Analysis in R

This follow-up course on Intermediate R does not cover new programming concepts. Instead, you will strengthen your knowledge of the topics in Intermediate R with a bunch of new and fun exercises.

Strengthen your knowledge of the topics you learned in Intermediate R with a ton of new and fun exercises.

Practice the skills you learned in DataCamp's Intermediate R course See why over 700,000 people use DataCamp today!

Intermediate R: Practice

The R data.table package is rapidly making its name as the number one choice for handling large datasets in R. This online data.table tutorial will bring you from data.table novice to expert in no time. Once you are introduced to the general form of a data.table query, you will learn the techniques to subset your data.table, how to update by reference and how you can use data.table’s set()-family in your workflow. The course finishes with more complex concepts such as indexing, keys and fast ordered joins. Upon completion of the course, you will be able to use data.table in R for a more efficient manipulation and analysis process. Enjoy!

Master core concepts in data manipulation such as subsetting, updating, indexing and joining your data using data.table.

Master core concepts in data manipulation such as subsetting, updating, indexing and joining your data using data.table in this course. 

Merge, Join, Subset Data in R

Data Analysis in R, the data.table Way

Ultimately, data analysis is about understanding relationships among variables. Exploring data with multiple variables requires new, more complex tools, but enables a richer set of comparisons. In this course, you will learn how to describe relationships between two numerical quantities. You will characterize these relationships graphically, in the form of summary statistics, and through simple linear regression models.

Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.

Learn how to describe relationships between two numerical quantities and characterize these relationships graphically through simple linear regression models.

Correlation and Regression in R

In the first part of this two-part RStudio tutorial, you will learn how to use RStudio, an IDE for R.Chapter 1 will cover:<ol><li>how to install R Studio for Mac, Windows, or Linux </li><li>how to run R code</li><li>debugging in R</li></ol>Chapter 2 will get into the features of the IDE geared toward making you a more effective R programmer, including code diagnostics and running scripts.Finally, Part 1 finishes with a brief chapter on using RStudio projects for organizing and sharing your code with others.In <a href="https://www.datacamp.com/courses/working-with-the-rstudio-ide-part-2">Part 2</a>, you will learn how RStudio makes it easy to build your own R packages, integrate with GitHub, and use tools like R Markdown.

Learn the basics of the important features of the RStudio IDE.

Working with the RStudio IDE (Part 1)

The beauty of R is that it is built for performing data analysis. The downside is that sometimes R can be slow, thereby obstructing our analysis. For this reason, it is essential to become familiar with the main techniques for speeding up your analysis, so you can reduce computational time and get insights as quickly as possible.

Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.

Writing Efficient R Code

This course provides a comprehensive introduction on how to plot data with R’s default graphics system, base graphics.After an introduction to base graphics, we look at a number of R plotting examples, from simple graphs such as scatterplots to plotting correlation matrices. The course finishes with exercises in plot customization. This includes using R plot colors effectively and creating and saving complex plots in R.Base Graphics Background R supports four different graphics systems: base graphics, grid graphics, lattice graphics, and ggplot2. Base graphics is the default graphics system in R, the easiest of the four systems to learn to use, and provides a wide variety of useful tools, especially for exploratory graphics where we wish to learn what is in an unfamiliar dataset.

This course provides a comprehensive introduction to working with base graphics in R.

Data Visualization in R

This ggplot2 tutorial builds on your knowledge from the first course to produce meaningful explanatory plots. We'll explore the last four optional layers. Statistics will be calculated on the fly and we’ll see how Coordinates and Facets aid in communication. Publication quality plots will be produced directly in R using the Themes layer. We’ll also discuss details on data visualization best practices with ggplot2 to help make sure you have a sound understanding of what works and why. By the end of the course, you’ll have all the tools needed to make a custom plotting function to explore a large data set, combining statistics and excellent visuals.

Take your data visualization skills to the next level with coordinates, facets, themes, and best practices in ggplot2.

Ggplot2 author Hadley Wickham recommends this tutorial that teaches you how to create data visualizations with ggplot2. See why over 300,000 use DataCamp!

Data Visualization with ggplot2 (Part 2)

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

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

Dates and times are abundant in data and essential for answering questions that  start with when, how long, or how often.  However, they can be tricky, as they come in a variety of formats and can behave in unintuitive ways. This course teaches you the essentials of parsing, manipulating, and computing with  dates and times in R. By the end, you'll have mastered the lubridate package, a member of the tidyverse, specifically designed to handle dates and times.  You'll also have applied your new skills to explore how often R versions are released,  when the weather is good in Auckland (the birthplace of R), and how long monarchs ruled in Britain.

Learn the essentials of parsing, manipulating and computing with dates and times in R.

Dates and Times in R

Working with Dates and Times in R

The data.table package provides a high-performance version of base R's data.frame with syntax and feature enhancements for ease of use, convenience and programming speed. This course shows you how to create, subset, and manipulate data.tables. You'll also learn about the database-inspired features of data.tables, including built-in groupwise operations. The course concludes with fast methods of importing and exporting tabular text data such as CSV files. Upon completion of the course, you will be able to use data.table in R for a more efficient manipulation and analysis process. Throughout the course you'll explore the San Francisco Bay Area bike share trip dataset from 2014.

Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table. 

Data Manipulation with data.table in R

Once you've started learning tools for data manipulation and visualization like dplyr and ggplot2, this course gives you a chance to use them in action on a real dataset. You'll explore the historical voting of the United Nations General Assembly, including analyzing differences in voting between countries, across time, and among international issues. In the process you'll gain more practice with the dplyr and ggplot2 packages, learn about the broom package for tidying model output, and experience the kind of start-to-finish exploratory analysis common in data science.

Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.

Use your data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.

Case Study: Exploratory Data Analysis in R

Time series are all around us, from server logs to high frequency financial data. Managing and manipulating ordered observations is central to all time series analysis. The xts and zoo packages provide a set of powerful tools to make this task fast and mistake free. In this course, you will learn everything from the basics of xts to advanced tips and tricks for working with time series data in R.

The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.

You will learn everything from the basics of working with the xts and zoo packages to advanced tips and tricks for working with time series data in R. 

Manipulating Time Series Data with xts and zoo in R

This course begins by reviewing slopes and intercepts in linear regressions before moving on to random-effects. You'll learn what a random effect is and how to use one to model your data. Next, the course covers linear mixed-effect regressions. These powerful models will allow you to explore data with a more complicated structure than a standard linear regression. The course then teaches generalized linear mixed-effect regressions. Generalized linear mixed-effects models allow you to model more kinds of data, including binary responses and count data. Lastly, the course goes over repeated-measures analysis as a special case of mixed-effect modeling. This kind of data appears when subjects are followed over time and measurements are collected at intervals. Throughout the course you'll work with real data to answer interesting questions using mixed-effects models.

In this course you will learn to fit hierarchical models with random effects.

Discover what a random effect is and how to use one, as well as how to use generalized linear mixed-effect regressions and repeated-measures analysis.

Hierarchical and Mixed Effect Models in R

Hierarchical and Mixed Effects Models in R

Many times in machine learning, the goal is to find patterns in data without trying to make predictions. This is called unsupervised learning. One common use case of unsupervised learning is grouping consumers based on demographics and purchasing history to deploy targeted marketing campaigns. Another example is wanting to describe the unmeasured factors that most influence crime differences between cities. This course provides a basic introduction to clustering and dimensionality reduction in R from a machine learning perspective, so that you can get from data to insights as quickly as possible.


This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

This course provides a basic introduction to clustering and dimensionality reduction in R from a machine learning perspective to get insights quickly.

Unsupervised Learning 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

Linear regression serves as a workhorse of statistics, but cannot handle some types of complex data. A generalized linear model (GLM) expands upon linear regression to include non-normal distributions including binomial and count data. Throughout this course, you will expand your data science toolkit to include GLMs in R. As part of learning about GLMs, you will learn how to fit model binomial data with logistic regression and count data with Poisson regression. You will also learn how to understand these results and plot them with ggplot2.

The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

Learn to fit model binomial data with logistic regression to count data with Poisson regression, and how to understand and plot the results with ggplot2.

Generalized Linear Models in R

So what is all this AI fuss about? Machine Learning, Deep Learning, Predictive Analytics -- what is the reality behind the hype? How do machines actually learn and what are their limits? How can we use Machine Learning to recognize written digits, predict customer churn and find structure in Elon Musk's tweets? All this -- and much more -- is the topic of this course, which will introduce you to the world of AI in a gentle, but firm and very practical manner.

Learn the fundamentals of AI. No programming experience required!

Learn to work with labeled data, train regression models, divide data into clusters, detect anomalies, select the right model, and create neural networks.

AI Fundamentals

Machine learning is the study and application of algorithms that learn from and make predictions on data. From search results to self-driving cars, it has manifested itself in all areas of our lives and is one of the most exciting and fast growing fields of research in the world of data science. This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for optimal performance, how to preprocess data for better results, and much more. The popular <code>caret</code> R package, which provides a consistent interface to all of R's most powerful machine learning facilities, is used throughout the course.

This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for performance, and how to preprocess data

Machine Learning with caret in R

In this course you'll take your skills with simple linear regression to the next level. By learning multiple and logistic regression techniques you will gain the skills to model and predict both numeric and categorical outcomes using multiple input variables. You'll also learn how to fit, visualize, and interpret these models. Then you'll apply your skills to learn about Italian restaurants in New York City!

In this course you'll learn to add multiple variables to linear models and to use logistic regression for classification.

Multiple and Logistic Regression 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

Missing data is part of any real world data analysis. It can crop up in unexpected places, making analyses challenging to understand. In this course, you will learn how to use tidyverse tools and the naniar R package to visualize missing values. You'll tidy missing values so they can be used in analysis and explore missing values to find bias in the data. Lastly, you'll reveal other underlying patterns of missingness. You will also learn how to "fill in the blanks" of missing values with imputation models, and how to visualize, assess, and make decisions based on these imputed datasets.

Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.

Learn how to use tidyverse tools and the naniar R package to 
visualize missing values, tidy values, and eveal other underlying patterns of missingness.

Dealing With Missing Data in R

If you already know SAS, SPSS or Stata, you don’t need to spend time learning how to analyze data; you need a course that focuses on translating your knowledge into R. This comprehensive course introduces R jargon using the language you're familiar with.

Learn to translate your knowledge of SAS, SPSS, or Stata into R using the same statistics techniques you're familiar with.

Master R quickly with the help of your prior knowledge of SAS, SPSS, and STATA

R for SAS, SPSS and STATA Users

In the real world, data sets typically come split across many tables while most data analysis functions in R are designed to work with single tables of data. In this course, you'll learn how to effectively combine data sets into single tables using data.table. You'll learn how to add columns from one table to another table,
how to filter a table based on observations in another table, and how to identify records across multiple tables matching complex criteria. Along the way, you'll learn how to troubleshoot failed join operations and best practices for working with complex data sets. After completing this course you'll be well on your way to be a data.table master!


This course will show you how to combine and merge datasets with data.table.

Learn how to combine and merge datasets, add columns from one table to another, filter based on observations in another table, and more with data.table

Joining data with data.table in R

Joining Data with data.table in R

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

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

Learn how RStudio makes it easy to build your own R packages, how you can integrate with your favorite version control software like Git and GitHub for maximum productivity, and finally how to make use of tools like R Markdown, LaTeX, and Shiny for reporting your results to the relevant stakeholders.This is the second part of a two-part course on how to use RStudio. <a href="https://www.datacamp.com/courses/working-with-the-rstudio-ide-part-1">Part 1</a> covers the basics of working with RStudio.

Further your knowledge of RStudio and learn how to integrate Git, LaTeX, and Shiny

Working with the RStudio IDE (Part 2)

In this course, you'll learn to work with data using tools from the tidyverse in R. By data, we mean your own data, other people's data, messy data, big data, small data - any data with rows and columns that comes your way! By work, we mean doing most of the things that sound hard to do with R, and that need to happen before you can analyze or visualize your data. But work doesn't mean that it is not fun - you will see why so many people love working in the tidyverse as you learn how to explore, tame, tidy, and transform your data. Throughout this course, you'll work with data from a popular television baking competition called "The Great British Bake Off."

Learn to work with data using tools from the tidyverse, and master the important skills of taming and tidying your data.

Working with Data in the Tidyverse

As a data scientist, you will often find yourself working with non-numerical data, such as job titles, survey responses, or demographic information. R has a special way of representing them, called factors, and this course will help you master working with them using the tidyverse package forcats. We’ll also work with other tidyverse packages, including ggplot2, dplyr, stringr, and tidyr and use real world datasets, such as the fivethirtyeight flight dataset and Kaggle’s State of Data Science and ML Survey. Following this course, you’ll be able to identify and manipulate factor variables, quickly and efficiently visualize your data, and effectively communicate your results. Get ready to categorize!

Get ready to categorize! In this course,  you will work with non-numerical data, such as job titles or survey responses, using the Tidyverse landscape.

Categorical Data in the Tidyverse

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

In this course you'll learn how to work with tree-based models in R. This course covers everything from using a single tree for regression or classification to more advanced ensemble methods. You'll learn to implement bagged trees, Random Forests, and boosted trees using the Gradient Boosting Machine, or GBM. These powerful techinques will allow you to create high performance regression and classification models for your data.

In this course, you'll learn how to use tree-based models and ensembles for regression and classification.

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