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

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

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

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

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

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)

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

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

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

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 our ggvis data visualization course, you will learn how to create static and interactive graphs to display distributions, relationships, model fits, and more.The first part of the course will focus on how to think conceptually about data visualizations using the theory behind the grammar of graphics.Next, you will dive into the syntax of the R graphics package ggvis. Step-by-step, you'll learn how to create clear R data visualizations with sliders, widgets and text fields, and you’ll become familiar with the ggvis layering scheme to create multi-layered graphs.Once you complete this data visualization class, you will be able to create meaningful static and interactive graphs to display distributions, relationships, and more!

Learn to create interactive graphs to display distributions, relationships, model fits, and more using ggvis.

Learn to create static & interactive graphs to display distributions, relationships, & more with DataCamp's course taught by ggvis creators RStudio today!

Data Visualization in R with ggvis - Online Course

Data Visualization in R with ggvis

Running exciting analyses on interesting datasets is the dream of every data scientist. But first, some importing and cleaning must be done. In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.

In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.

Practice importing and cleaning data in R with this case study focused tutorial.  You will improve your R skills quickly and efficiently with this course!

Importing & Cleaning Data in R: Case Studies

This hands-on-course with real-life credit data will teach you how to model credit risk by using logistic regression and decision trees in R.Modeling credit risk for both personal and company loans is of major importance for banks. The probability that a debtor will default is a key component in getting to a measure for credit risk. While other models will be introduced in this course as well, you will learn about two model types that are often used in the credit scoring context; logistic regression and decision trees. You will learn how to use them in this particular context, and how these models are evaluated by banks.

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.

Learn to create models to evaluate financial credit risk like a professional

Credit Risk Modeling in R

It is estimated that over 70% of potentially usable business information is unstructured, often in the form of text data. Text mining provides a collection of techniques that allows us to derive actionable insights from unstructured data. In this course, we explore the basics of text mining using the bag of words method. The first three chapters introduce a variety of essential topics for analyzing and visualizing text data. The final chapter allows you to apply everything you've learned in a real-world case study to extract insights from employee reviews of two major tech companies.

Learn the bag of words technique for text mining with R.

Discover the basics of text mining using the bag of words method and a variety of other essential topics for analyzing and visualizing text data in R.

Text Mining with Bag-of-Words in R

Importing data into R to start your analyses: it should be the easiest step. Unfortunately, this is almost never the case. Data can come in all sorts of formats, ranging from flat files and statistical software files to databases and web data. Knowing which approach to use is key to getting started with the actual analysis. In this course, you will learn all the basics on how to load data into R so you can get up and running in no time!

Learn how to parse data in any format. Whether it's flat files, statistics software, databases, or web data, you'll handle it all.

DataCamp teaches you to load & import data into R from almost any source with R packages such as fread, readxl & more. Take this comprehensive course today!

Importing Data Into R - Data Importation Course

Importing Data Into R

Statistical Modeling in R is a multi-part course designed to get you up to speed with the most important and powerful methodologies in statistics. In this intermediate course 2, we'll take a look at effect size and interaction, the concepts of total and partial change, sampling variability and mathematical transforms, and the implications of something called collinearity. 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.

In this follow-up course, you will expand your stat modeling skills from the introduction and dive into more advanced concepts.

Learn methodologies in statistics, including total and partial change, sampling variability and mathematical transforms, and implications of collinearity.

Intermediate Statistical Modeling in R

As a Data Scientist, on a daily basis you will need to clean data, wrangle and munge it, visualize it, build predictive models and interpret these models. Before doing any of these, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: (i) from flat files such as .txts and .csvs; (ii) from files native to other software such as Excel spreadsheets, Stata, SAS and MATLAB files; (iii) from relational databases such as SQLite & PostgreSQL; (iv) from the web and (v) a special and essential case of this: pulling data from Application Programming Interfaces, also known as APIs, such as the Twitter streaming API, which allows us to stream real-time tweets.

Learn the critical skills required to effectively import data using Python with ease

Importing Data in Python

Quandl offers millions of free and open financial, economic, and social datasets. In this tutorial you will learn how to effortlessly pull any of Quandl's data into R.

How to work with Quandl in R

Not sure if this is the type of online statistics course you’re looking for? Or perhaps not yet familiar with DataCamp’s interactive learning interface? By taking this free course, you can discover it for yourself! Via a combination of videos and interactive coding challenges, this introductory course will teach you about variables, plotting, and summary statistics like the mean and standard deviation. Enjoy learning by doing!

A friendly introduction to fundamental concepts in statistics in R.

This introductory statistics with R tutorial will teach you about variables, plotting, and summary statistics like the mean and standard deviation.

Free Introduction to Statistics with R

Intro to Statistics with R: Introduction

If you want to have a solid basic foundation in statistics, it is essential to understand the concepts and theories behind t-tests. This module covers both the intuition and the calculations behind dependent t-tests, independent t-tests and z-scores. Topics such as null hypothesis significance testing (NHST), p-values, and effect size are covered in detail.

This introductory statistics with R tutorial will teach you the ins and outs of the Student's T test.

Introduction to Statistics with R: Student's T Test

Intro to Statistics with R: Student's T-test

Analysis of Variance (ANOVA) is probably one of the most popular and commonly used statistical procedures. In this course, Professor Conway will cover the essentials of ANOVA such as one-way between groups ANOVA, post-hoc tests, and repeated measures ANOVA.

This introductory statistics with R tutorial will teach you all about Analysis of Variance: ANOVA.

Introduction to Statistics with R: ANOVA

Intro to Statistics with R: Analysis of Variance (ANOVA)

This course focuses on within-groups comparisons and repeated measures design. With the help of a working memory training experiment, one of Professor Conway’s main areas of research, it will be explained what the pros and cons are of a repeated measures design and how to conduct the calculations in R yourself.

This introductory statistics with R tutorial will teach you all about repeated measures ANOVA.

Introduction to Statistics with R: Repeated Measures ANOVA

Intro to Statistics with R: Repeated measures ANOVA

If you have ever taken a math or statistics class, you’ve probably heard the old adage "Correlation does not imply causation". The first part of this course explores this further, and will offer a broad overview on correlational analysis. In the second part you will leave descriptive statistics behind, and dive into regression, prediction and inferential statistics.

This introductory statistics with R tutorial covers the topics of Correlation and Linear Regression.

Intro to Statistics with R: Correlation and Linear Regression

Multiple regression is a powerful statistical technique, and here you will discover why and how to use it. Part of the course will focus on matrix algebra since it is essential if you want to start estimating regression coefficients in the regression equation. The final chapter will introduce dummy coding as a technique to handle categorical variables.

This introductory statistics with R tutorial covers the topic of Multiple Regression.

Intro to Statistics with R: Multiple Regression

Moderation and mediation sound alike, but in reality they are quite different. This course will get you acquainted with these complex analytical techniques based on multiple regression. Special attention will go to centering predictors as a way to improve interpretability of results.

Intro to Statistics with R: Moderation and Mediation

Always wanted to compete in a <a href='https://www.kaggle.com/?utm_medium=partner&utm_source=datacamp.com&utm_campaign=datacamp+r' target="_blank">Kaggle</a> machine learning competition but not sure you have the right skillset? This interactive tutorial by Kaggle and DataCamp on Machine Learning data sets offers the solution. Step-by-step you will learn through fun coding exercises how to predict survival rate for Kaggle's Titanic competition using R Machine Learning packages and techniques. Upload your results and see your ranking go up! New to R? Give our <a href='https://www.datacamp.com/courses/free-introduction-to-r' target="_blank">Introduction to R</a> course a try.

Kaggle R Tutorial on Machine Learning

In this course you will begin learning the art and science of data exploration. You'll also become familiar with some of the most widely used visualization packages in data science. You'll get the chance to explore a number of fun & interesting datasets such as the <a href='https://www.kaggle.com/census/2013-american-community-survey' target="_blank">2013 American Community Survey</a>, pigeon racing data, data on the length of chopsticks and Marijuana prices in the US. After completing the course, you'll be able to upload your first <a href='https://www.kaggle.com/scripts' target="_blank">Kaggle Script</a>. <hr> Bonus Chapter: Once you have finished your exercises on DataCamp it's time to start building a data science portfolio with your new skills! In this bonus chapter, you'll be given the chance to publish analyses on Kaggle Scripts that you've personalized with information from your own life. <ul> <li><a href='https://www.kaggle.com/benhamner/d/kaggle/us-baby-names/babies-named-benjamin-over-time'>(Basic) Visualize a dataset on US Baby Names to learn the popularity of your name or a friend's name in the USA over the past 100+ years.</a></li> <li><a href='https://www.kaggle.com/benhamner/d/kaggle/college-scorecard/exploring-your-college-duke-university'>(Intermediate) Create a polished analysis in RMarkdown that walks readers through recent changes at the college you attended or would like to attend. You will explore changes in enrollment, tuition, admissions rate, and degree types.</a></li> <li>(Advanced) Each of Ben's scripts end with suggestions for creating an even meatier analysis. Which ones can you use to extend your work?</li> </ul> To start each exercise, follow the link to a script that has been created by Kaggle CTO Ben Hamner. Review the code or output to find instructions for creating a custom analysis. Then click Fork on the script to create an editable copy and get started.

Data Exploration With Kaggle Scripts

A golden rule in investing is to always test the portfolio strategy on historical data, and, once you are trading the strategy, to constantly monitor its performance. In this course, you will learn this by critically analyzing portfolio returns using the package PerformanceAnalytics. The course also shows how to estimate the portfolio weights that optimally balance risk and return. This is a data-driven course that combines portfolio theory with the practice in R, illustrated on real-life examples of equity portfolios and asset allocation problems. If you'd like to continue exploring the data after you've finished this course, the data used in the first three chapters can be obtained using the tseries-package. The code to get them can be found <a href='http://s3.amazonaws.com/assets.datacamp.com/course/portfolio-analysis/data_portfolio_analysis.R' target='_blank'>here</a>. The data used in chapter 4 can be downloaded <a href='http://s3.amazonaws.com/assets.datacamp.com/course/portfolio-analysis/prices.rds' target='_blank'>here</a>.

Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.

Apply your R skills to backtest, analyze, and optimize financial portfolios. The course also shows how to estimate the weights that optimize risk and return.

Introduction to Portfolio Analysis in R

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)

As a data scientist, you will need to clean data, wrangle and munge it, visualize it, build predictive models, and interpret these models. Before you can do so, however, you will need to know how to get data into Python. In this course, you'll learn the many ways to import data into Python: from flat files such as .txt and .csv; from files native to other software such as Excel spreadsheets, Stata, SAS, and MATLAB files; and from relational databases such as SQLite and PostgreSQL.

Learn to import data into Python from various sources, such as Excel, SQL, SAS and right from the web.

You'll learn the many ways to read data into Python: from flat files such as CSVs to Excel spreadsheets and relational databases in SQLite & PostgreSQL.

Introduction to Importing Data in Python

In this course, you will become an expert in fitting ARIMA models to time series data using R.  First, you will explore the nature of time series data using the tools in the R stats package.  Next, you learn how to fit various ARMA models to simulated data (where you will know the correct model) using the R package astsa.  Once you have mastered the basics, you will learn how to fit integrated ARMA models, or ARIMA models to various real data sets. You will learn how to check the validity of an ARIMA model and you will learn how to forecast time series data.  Finally, you will learn how to fit ARIMA models to seasonal data, including forecasting using the astsa package.

Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

In this course, you will become an expert in fitting ARIMA models to time series data using R, from fitting to simulated data and seasonal data to using astsa.

ARIMA Models in R

Are you job interview ready? You may know everything there is to know about your target company, but have you practiced the classic R statistical interview questions? If not, we have you covered. In this course, you'll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more. You’ll sharpen your skills using datasets including Parkinson’s disease data and gas prices. This course is purposely more challenging than a typical DataCamp course to make sure that when it comes to interviewing time you’re ready to confidently tackle any statistics interview question in R.

 In this course, you'll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more. 

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