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

Now that you've learned the basics of SQL in our <a href="https://learn.datacamp.com/courses/introduction-to-sql">Introduction to SQL</a> course, it's time to supercharge your queries using joins and relational set theory. In this course, you'll learn all about the power of joining tables while exploring interesting features of countries and their cities throughout the world. You will master inner and outer joins, as well as self joins, semi joins, anti joins and cross joins—fundamental tools in any PostgreSQL wizard's toolbox. Never fear set theory again after learning all about unions, intersections, and except clauses through easy-to-understand diagrams and examples. Lastly, you'll be introduced to the challenging topic of subqueries. You will be able to visually grasp these ideas by using Venn diagrams and other linking illustrations.

Join two or three tables together into one, combine tables using set theory, and work with subqueries in PostgreSQL.

Discover all the fundamental in PostgreSQL, like inner, outer self, semi, anti, and cross joins, as well as unions, intersections, and except clauses.

Joining Data in SQL

The ability to build tools capable of retrieving and parsing information stored across the internet has been and continues to be valuable in many veins of data science. In this course, you will learn to navigate and parse html code, and build tools to crawl websites automatically. Although our scraping will be conducted using the versatile Python library scrapy, many of the techniques you learn in this course can be applied to other popular Python libraries as well, including BeautifulSoup and Selenium. Upon the completion of this course, you will have a strong mental model of html structure, will be able to build tools to parse html code and access desired information, and create a simple scrapy spiders to crawl the web at scale.

Learn to retrieve and parse information from the internet using the Python library scrapy.

Web Scraping with Python

Web Scraping in Python

This course extends your existing Python skills to provide a stronger foundation in data visualization in Python. You’ll get a  broader coverage of the Matplotlib library and an overview of seaborn, a package for statistical graphics. Topics covered include customizing graphics, plotting two-dimensional arrays (like pseudocolor plots, contour plots, and images), statistical graphics (like visualizing distributions and regressions), and working with time series and image data.

Learn complex data visualization techniques using Matplotlib and seaborn.

Introduction to Data Visualization in Python

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

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

Introduction Statistical Modeling in R is a multi-part course designed to get you up to speed with the most important and powerful methodologies in statistical modeling in R.In In this introduction, we'll take a look at what statistical modeling is and what it's used for, R tools for model building, using models for prediction (and using prediction to test models), and how to account for the combined influences of multiple variables.This course has been written from scratch, specifically for DataCamp users. As you'll see, by using computing and concepts from machine learning, we'll be able to leapfrog many of the marginal and esoteric topics encountered in traditional 'regression' courses. <a href="https://www.datacamp.com/courses/statistical-modeling-in-r-part-2">The intermediate course</a> will get you up to speed with the most important and powerful methodologies in statistics.

This course is designed to get you up to speed with the most important and powerful methodologies in statistics.

Learn about the essentials of statistical modeling in R, by exploring data with models, assessing prediction performance and the notion of covariates.

Introduction to Statistical Modeling in R

In this course, you'll learn how to use Spark from Python! Spark is a tool for doing parallel computation with large datasets and it integrates well with Python. PySpark is the Python package that makes the magic happen. You'll use this package to work with data about flights from Portland and Seattle. You'll learn to wrangle this data and build a whole machine learning pipeline to predict whether or not flights will be delayed. Get ready to put some Spark in your Python code and dive into the world of high-performance machine learning!

Learn to implement distributed data management and machine learning in Spark using the PySpark package.

Learn to wrangle data and build a machine learning pipeline to make predictions with  PySpark Python package. Practice your skills with real-world data.

Introduction to PySpark

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

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

There's been a lot of buzz about Big Data over the past few years, and it's finally become mainstream for many  companies. But what is this Big Data? This course covers the fundamentals of Big Data via PySpark. Spark is a "lightning fast cluster computing" framework for Big Data. It provides a general data processing platform engine and lets you run programs up to 100x faster in memory, or 10x faster on disk,  than Hadoop. You’ll use PySpark, a Python package for Spark programming and its powerful, higher-level libraries such as SparkSQL, MLlib (for machine learning), etc. You will explore the works of William Shakespeare, analyze Fifa 2018 data and perform clustering on genomic datasets. At the end of this course, you will have gained an in-depth understanding of PySpark and its application to general Big Data analysis.

Learn the fundamentals of working with big data with PySpark.

Learn to conduct Big Data analysis using the Python package for Spark programming, PySpark, as well as higher level libraries like SparkSQL and MLlib.

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