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 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

This course will dig deeper into some of the core functionality of Google Sheets. There's a whole bunch of predefined functions we'll cover, like `SUM()` and `AVERAGE()`, and `VLOOKUP()`. We'll apply these techniques to do some analysis on your grades in school, look at performance statistics within a company, track monthly sales, and look at some real geographical information about the countries of the world.

Learn how to analyze data with spreadsheets using functions such as SUM(), AVERAGE(), and VLOOKUP().

Data Analysis in Spreadsheets

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)

Spreadsheet software is one of the most popular and powerful tools in data analysis. Millions of people use tools like Google Sheets or Microsoft Excel on a daily basis. Even the most experienced data scientists often started their careers with spreadsheets and still use it to test assumptions or to look at data for the first time. In this course, you will learn the basics of spreadsheets by working with rows, columns, addresses, and ranges. You will create your own formulas and learn how to use references.

Learn the basics of spreadsheets by working with rows, columns, addresses, and ranges. 

Discover the basics of spreadsheets by working with rows, columns, addresses, and ranges, then create your own formulas and learn how to use references.

Introduction to Spreadsheets

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 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 finance-oriented introduction to R, you will learn essential data structures such as lists and data frames and have the chance to apply that knowledge to real-world financial examples. By the end of the course, you will be comfortable with the basics of manipulating your data to perform financial analysis in R.

Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.

Learn the essential data structures, including lists and data frames and have the chance to apply that knowledge directly to financial examples using R.

Intro to R for Finance

Introduction to R for Finance

If you enjoyed the Introduction to R for Finance course, then you will love Intermediate R for Finance. Here, you will first learn the basics about how dates work in R, an important skill for the rest of the course. Your next step will be to explore the world of if statements, loops, and functions. These are powerful ideas that are essential to any financial data scientist's toolkit. Finally, we will spend some time working with the family of apply functions as a vectorized alternative to loops. And of course, all examples will be finance related! Enjoy!

Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.

Learn about how dates work in R, and explore the world of if statements, loops, and functions. You'll practice this knowledge using financial examples.

Intermediate R for Finance

Once you've started learning tools for building interactive web applications with shiny, this course will translate this knowledge into building dashboards. Dashboards, a common data science deliverable, are pages that collate information, often tracking metrics from a live-updating data source. You'll gain more expertise using shiny while learning to build and design these dynamic dashboards. In the process, you'll pick up tips to optimize performance as well as best practices to create a visually appealing product.

In this course you'll learn to build dashboards using the shinydashboard package.

Building Dashboards with shinydashboard

Add sentiment analysis to your text mining toolkit! Sentiment analysis is used by text miners in marketing, politics, customer service and elsewhere. In this course you will learn to identify positive and negative language, specific emotional intent, and make compelling visualizations. You will end the course by applying your sentiment analysis skills to Airbnb reviews to learn what makes for a good rental.

Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.

Sentiment Analysis in R

After learning the basics of using Shiny to build web applications, this course takes you to the next level by putting your newly acquired skills into practice. You'll get experience developing fun and realistic Shiny apps for different common use cases, such as using Shiny to explore a dataset, generate a customized plot, and even create a word cloud. With all this practice and new knowledge, you will be well-equipped to develop Shiny apps for your own use.

Practice your Shiny skills while building some fun Shiny apps for real-life scenarios!

Case Studies: Building Web Applications with Shiny in R

HR analytics, people analytics, workforce analytics -- whatever you call it, businesses are increasingly counting on their human resources departments to answer questions, provide insights, and make recommendations using data about their employees. In this course, you'll learn how to manipulate, visualize, and perform statistical tests on HR data through a series of HR analytics case studies.

Manipulate, visualize, and perform statistical tests on HR data. 

Learn how you can gain insights, answer questions, and make predictions from your HR data by using R to manipulate, visualize, and perform statistical tests.

Human Resources Analytics: Exploring Employee Data in 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

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

In this course you'll learn how to work with and visualize network data. You'll use the <code>igraph</code> package to create networks from edgelists and adjacency matrices. You'll also learn how to plot networks and their attributes. Then, you'll learn how to identify important vertices using measures like betweenness and degree. Next, this course covers network structures, including triangles and cliques. Next, you'll learn how to identify special relationships between vertices, using metrics like assortativity. Finally, you'll see how to create interactive network plots using threejs.

In this course you'll learn to analyze and visualize network data with the igraph package.

Network Analysis 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

Lists can be difficult to both understand and manipulate, but they can pack a ton of information and are very powerful. In this course, you will learn to easily extract, summarize, and manipulate lists and how to export the data to your desired object, be it another list, a vector, or even something else! Throughout the course, you will work with the purrr package and a variety of datasets from the repurrrsive package, including data from Star Wars and Wes Anderson films and data collected about GitHub users and GitHub repos. Following this course, your list skills will be purrrfect!

Learn to easily summarize and manipulate lists using the purrr package.

Learn how to work with the purrr package and a variety of datasets from the repurrrsive package to extract, summarize, manipulate, and troubleshoot lists.

Foundations of Functional Programming with purrr

Have you ever been wondering what the purrr description (“A functional programming toolkit for R”) refers to? Then, you’ve come to the right place! This course will walk you through the functional programming part of purrr - in other words, you will learn how to take full advantage of the flexibility offered by the .f in map(.x, .f) to iterate other lists, vectors and data.frame with a robust, clean, and easy to maintain code. During this course, you will learn how to write your own mappers (or lambda functions), and how to use predicates and adverbs. Finally, this new knowledge will be applied to a use case, so that you’ll be able to see how you can use this newly acquired knowledge on a concrete example of a simple nested list, how to extract, keep or discard elements, how to compose functions to manipulate and parse results from this list, how to integrate purrr workflow inside other functions, how to avoid copy and pasting with purrr functional tools.

Continue learning with purrr to create robust, clean, and easy to maintain iterative code.

Intermediate Functional Programming with purrr

This course will introduce a powerful classifier, the support vector machine (SVM) using an intuitive, visual approach. Support Vector Machines in R will help students develop an understanding of the SVM model as a classifier and gain practical experience using R’s libsvm implementation from the e1071 package. Along the way, students will gain an intuitive understanding of important concepts, such as hard and soft margins, the kernel trick, different types of kernels, and how to tune SVM parameters. Get ready to classify data with this impressive model.

This course will introduce the support vector machine (SVM) using an intuitive, visual approach.

Support Vector Machines in R

In this course, you will learn the foundations of A/B testing, including hypothesis testing, experimental design, and confounding variables. You will also be exposed to a couple more advanced topics, sequential analysis and multivariate testing. The first dataset will be a generated example of a cat adoption website. You will investigate if changing the homepage image affects conversion rates (the percentage of people who click a specific button). For the remainder of the course you will use another generated dataset of a hypothetical data visualization website.

Learn A/B testing: including hypothesis testing, experimental design, and confounding variables.

A/B Testing in R

This course will expand your Google Sheets vocabulary. You'll dive deeper into data types, practice manipulating numeric and logical data, explore missing data and error types, and calculate some summary statistics. As you go, you'll explore datasets on 100m sprint world records, asteroid close encounters, benefit claims, and butterflies.

Expand your spreadsheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.

Learn about Google Sheets by practicing manipulating numeric and logical data, exploring missing data and error types, and calculating summary statistics.

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