This article on a complete tutorial to learn Data Science in R from scratch, was posted by Manish Saraswat. Manish who works in marketing and Data Science at Analytics Vidhya believes that education can change this world. R, Data Science and Machine Learning keep him busy.
R is a powerful language used widely for data analysis and statistical computing. It was developed in early 90s. Since then, endless efforts have been made to improve R’s user interface. The journey of R language from a rudimentary text editor to interactive R Studio and more recently Jupyter Notebooks has engaged many data science communities across the world.
tl;dr Today, we are open-sourcing TrailDB, a core library powering AdRoll. TrailDB makes it fast and fun to handle event data. Find it at traildb.io.
TrailDB was created at AdRoll to power processing of time-series of events. You can use it, for instance, to
- Compute metrics, such as the bounce rate
- Analyze usage patterns
- Detect anomalies
- Cluster and predict user behavior
Since 2014, AdRoll has used TrailDB to store and query over 20 trillion events originating from the web.
This is a tutorial (previously known as “Some hints for the R beginner”) for beginning to learn the R programming language. It is a tree of pages — move through the pages in whatever way best suits your style of learning.
You are probably impatient to learn R — most people are. That’s fine. But note that trying to skim past the basics that are presented here will almost surely take longer in the end.
This page has several sections, they can be put into the four categories: General, Objects, Actions, Help.
“A curated list of awesome Machine Learning frameworks, libraries and software…”
This chapter explains the purpose of some of the most commonly used statistical tests and how to implement them in R.
“There have been dozens of articles written comparing Python and R from a subjective standpoint. We’ll add our own views at some point, but this article aims to look at the languages more objectively. We’ll analyze a dataset side by side in Python and R, and show what code is needed in both languages to achieve the same result. This will let us understand the strengths and weaknesses of each language without the conjecture. At Dataquest, we teach both languages, and think both have a place in a data science toolkit.
We’ll be analyzing a dataset of NBA players and their performance in the 2013-2014 season. You can download the file here. For each step in the analysis, we’ll show the Python and R code, along with some explanation and discussion of the different approaches. Without further ado, let’s get this head to head matchup started!…”
“Pulled from the web, here is a great collection of eBooks (most of which have a physical version that you can purchase on Amazon) written on the topics of Data Science, Business Analytics, Data Mining, Big Data, Machine Learning, Algorithms, Data Science Tools, and Programming Languages for Data Science.
While every single book in this list is provided for free, if you find any particularly helpful consider purchasing the printed version. The authors spent a great deal of time putting these resources together and I’m sure they would all appreciate the support!…”