Introducing DataFramed, a Data Science Podcast

We are super pumped to be launching a weekly data science podcast called DataFramed, in which Hugo Bowne-Anderson (me), a data scientist and educator at DataCamp, speaks with industry experts about what data science is, what it’s capable of, what it looks like in practice and the direction it is heading over the next decade and into the future. You can check out the podcast here and make sure to subscribe, rate and review! For a sneak peak, check out the trailer above! Instead of answering “what is data science?” merely through the lens of related technologies, tools and skill-sets, a methodology commonly invoked to discover what data science is, we have decided to answer this question by delving into what modern data science looks like in practice via in-depth conversations with practitioners. These are the types of conversations we…
Original Post: Introducing DataFramed, a Data Science Podcast

New Course: Working with Dates & Times in R

Hello R users! We just launched another course: Working with Dates and Times in R by Charlotte Wickham! Dates and times are abundant in data and essential for answering questions that start with when, how long, or how often. However, they can be tricky, as they come in a variety of formats and can behave in unintuitive ways. This course teaches you the essentials of parsing, manipulating, and computing with dates and times in R. By the end, you’ll have mastered the lubridate package, a member of the tidyverse, specifically designed to handle dates and times. You’ll also have applied your new skills to explore how often R versions are released, when the weather is good in Auckland (the birthplace of R), and how long monarchs ruled in Britain. Take me to chapter 1! Working with Dates and Times in…
Original Post: New Course: Working with Dates & Times in R

Pipes in R Tutorial For Beginners

You might have already seen or used the pipe operator when you’re working with packages such as dplyr, magrittr,… But do you know where pipes and the famous %>% operator come from, what they exactly are, or how, when and why you should use them? Can you also come up with some alternatives? This tutorial will give you an introduction to pipes in R and will cover the following topics: Are you interested in learning more about manipulating data in R with dplyr? Take a look at DataCamp’s Data Manipulation in R with dplyr course. Pipe Operator in R: Introduction To understand what the pipe operator in R is and what you can do with it, it’s necessary to consider the full picture, to learn the history behind it. Questions such as “where does this weird combination of symbols come…
Original Post: Pipes in R Tutorial For Beginners

New R Course: Introduction to the Tidyverse!

Hi! Big announcement today as we just launched Introduction to the Tidyverse R course by David Robinson! This is an introduction to the programming language R, focused on a powerful set of tools known as the “tidyverse”. In the course you’ll learn the intertwined processes of data manipulation and visualization through the tools dplyr and ggplot2. You’ll learn to manipulate data by filtering, sorting and summarizing a real dataset of historical country data in order to answer exploratory questions. You’ll then learn to turn this processed data into informative line plots, bar plots, histograms, and more with the ggplot2 package. This gives a taste both of the value of exploratory data analysis and the power of tidyverse tools. This is a suitable introduction for people who have no previous experience in R and are interested in learning to perform data…
Original Post: New R Course: Introduction to the Tidyverse!

New DataCamp Course: Working with Web Data in R

Hi there! We just launched Working with Web Data in R by Oliver Keyes and Charlotte Wickham, our latest R course! Most of the useful data in the world, from economic data to news content to geographic information, lives somewhere on the internet – and this course will teach you how to access it. You’ll explore how to work with APIs (computer-readable interfaces to websites), access data from Wikipedia and other sources, and build your own simple API client. For those occasions where APIs are not available, you’ll find out how to use R to scrape information out of web pages. In the process, you’ll learn how to get data out of even the most stubborn website, and how to turn it into a format ready for further analysis. The packages you’ll use and learn your way around are rvest,…
Original Post: New DataCamp Course: Working with Web Data in R