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

Introduction to R (edX 1.1)

Introduction to R (edX 1.2)

Lab exercises on Create and Name Vectors.

Introduction to R (edX 2.1)

Introduction to R (edX 2.2)

Introduction to R (edX 2.3)

Lab exercises on Create and Name Matrices.

Introduction to R (edX 3.1)

Introduction to R (edX 3.2)

Introduction to R (edX 3.3)

Introduction to R (edX 4.1)

Introduction to R (edX 5.1)

Lab exercises on Subset and Extend Lists.

Introduction to R (edX 5.2)

Introduction to R (edX 6.1)

Lab exercises on Subset, extend and sort your data frame.

Introduction to R (edX 6.2)

Introduction to R (edX 7.1)

Introduction to R (edX 7.3)

Intro to Python for Data Science (edX 1.1)

Intro to Python for Data Science (edX 1.2)

Intro to Python for Data Science (edX 2.1)

Intro to Python for Data Science (edX 2.2)

Intro to Python for Data Science (edX 2.3)

Intro to Python for Data Science (edX 3.1)

Intro to Python for Data Science (edX 3.2)

Intro to Python for Data Science (edX 3.3)

Intro to Python for Data Science (edX 4.1)

Intro to Python for Data Science (edX 4.2)

Intro to Python for Data Science (edX 4.3)

Lab exercises on Basic Plots with matplotlib.

Intro to Python for Data Science (edX 5.1)

Intro to Python for Data Science (edX 5.2)

Intro to Python for Data Science (edX 5.3)

Lab exercises on Boolean Logic & Control Flow.

Intro to Python for Data Science (edX 6.1)

Intro to Python for Data Science (edX 6.2)

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 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

A majority of data is stored in databases and knowing the necessary tools needed to analyze and clean data directly in databases is indispensable. This course focuses on T-SQL, the version of SQL used in Microsoft SQL Server, needed for data analysis. You will learn several concepts in this course such as dealing with missing data, working with dates, and calculating summary statistics using advanced queries. After completing this course, you will have the skills needed to analyze data and provide insights quickly and easily.

In this course, you will use T-SQL, the flavor of SQL used in Microsoft's SQL Server for data analysis.

Learn concepts for working in  T-SQL, such as dealing with missing data, working with dates, and calculating summary statistics using advanced queries.

Intermediate SQL Server

As with any fundamentals course, Introduction to Natural Language Processing in R is designed to equip you with the necessary tools to begin your adventures in analyzing text. Natural language processing (NLP) is a constantly growing field in data science, with some very exciting advancements over the last decade. This course will cover the basics of these topics and prepare you for expanding your analysis capabilities. We dive into regular expressions, topic modeling, named entity recognition, and others, all while providing thorough examples that can be used to kick start your future analysis.

Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.

Discover basics skills and tools for natural language processing (NLP) in R, like regular expressions, topic modeling, named entity recognition and more.

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