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

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

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

Introduction to R Online

Introduction to R

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

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

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

Intermediate R

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

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

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

Introduction to Python

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

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

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

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

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

Scientists seek to answer questions using rigorous methods and careful observations. These observations—collected from the likes of field notes, surveys, and experiments—form the backbone of a statistical investigation and are called data. Statistics is the study of how best to collect, analyze, and draw conclusions from data. It is helpful to put statistics in the context of a general process of investigation: 1) identify a question or problem; 2) collect relevant data on the topic; 3) analyze the data; and 4) form a conclusion. In this course, you'll focus on the first two steps of the process.

Learn the language of data, study types,  sampling strategies, and experimental design.

Learn how you can identify a question, collect relevant data on the topic, analyze the data and then form a solid conclusion when working with Data in R.

Introduction to 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

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 Quantitative Risk Management (QRM), you will build models to understand the risks of financial portfolios. This is a vital task across the banking, insurance and asset management industries. The first step in the model building process is to collect data on the underlying risk factors that affect portfolio value and analyze their behavior. In this course, you will learn how to work with risk-factor return series, study the empirical properties or so-called "stylized facts" of these data - including their typical non-normality and volatility, and make estimates of value-at-risk for a portfolio.

Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk. 

In this course, you will work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk for a portfolio.

Quantitative Risk Management 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

From stock prices to climate data, time series data are found in a wide variety of domains, and being able to effectively work with such data is an increasingly important skill for data scientists. This course will introduce you to time series analysis in Python. After learning about what a time series is, you'll learn about several time series models ranging from autoregressive and moving average models to cointegration models. Along the way, you'll learn how to estimate, forecast, and simulate these models using statistical libraries in Python. You'll see numerous examples of how these models are used, with a particular emphasis on applications in finance.

In this course you'll learn the basics of analyzing time series data.

Learn the basics of analyzing time series data using several time series models including autoregressive, moving average, and cointegration in Python.

Time Series Analysis in Python

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

When working with data that contains many variables, we are often interested in studying the relationship between these variables using multivariate statistics. In this course, you'll learn ways to analyze these datasets. You will also learn about common multivariate probability distributions, including the multivariate normal, the multivariate-t, and some multivariate skew distributions. You will then be introduced to techniques for representing high dimensional data in fewer dimensions, including principal component analysis (PCA) and multidimensional scaling (MDS).

Learn to analyze, plot, and model multivariate data.

Learn how to summarize multivariate data using mean vector, variance-covariance, and correlation matrices to gain insights from it using the R program.

Multivariate Probability Distributions in R

Become a master at building complex reports!  In this course, you will apply all the SQL concepts and functions you have learned in previous courses to build out your very own dashboard.  By navigating through an Olympics database, you will become an expert data explorer and learn how to understand novel database quickly and effectively.  Since data is never perfect, you will gain valuable strategies to deal with real-world issues commonly found with SQL, including how to remove data duplication and how to turn messy data into clean, organized reports.  Lastly, you’ll conquer complex calculations using window functions and layered calculations, all within the same report.  This is a perfect class for anyone who will be commonly pulling data from databases and is a great complement for those who use R or Python for data science.

Learn how to build your very own dashboard by applying all the SQL concepts and functions you have learned in previous courses.

Reporting in SQL

Businesses track data on everything, from operations to marketing to HR. Leveraging this data enables businesses to better understand themselves and their customers, leading to higher profits and better performance. In this course, you’ll learn about the key metrics that businesses use to measure performance. You'll write SQL queries to calculate these metrics and produce report-ready results. Throughout the course, you'll study data from a fictional food delivery startup, modeled on data from real companies.

Learn to write SQL queries to calculate key metrics that businesses use to measure performance.

Analyzing Business Data in SQL

Tableau is a widely used business intelligence (BI) and analytics software trusted by companies like Amazon, Experian, and Unilever to explore, visualize, and securely share data in the form of Workbooks and Dashboards. With its user-friendly drag-and-drop functionality it can be used by everyone to quickly clean, analyze, and visualize your team’s data.

You’ll learn how to navigate Tableau’s interface and connect and present data using easy-to-understand visualizations. By the end of this training, you’ll have the skills you need to confidently explore Tableau and build impactful data dashboards. Let’s dive in.

Get started with Tableau, a widely used business intelligence (BI) and analytics software to explore, visualize, and securely share data.

Learn to navigate Tableau’s interface, connect and present data, and build easy-to-understand visualizations to build impactful dashboards for business.

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