# Introduction to R

### Requirements

• Windows/Mac/Linux

• Basic proficiency in math - vectors, matrices, algebra

• Basic proficiency in statistics - probability distributions, linear modeling, etc

• A high speed internet connection

Description

UPDATE : As of Nov 22, 2018, this course is now free! Many thanks to all my existing students who made it possible for the wider audience to benefit from the course material

With " Introduction to R ", you will gain a solid grounding of the fundamentals of the R language!

This course has about 90 videos and 140+ exercise questions , over 10 chapters. To begin with, you will learn to Download and Install R (and R studio) on your computer. Then I show you some basic things in your first R session.

From there, you will review topics in increasing order of difficulty, starting with Data/Object Types and Operations , Importing into R , and Loops and Conditions .

Next, you will be introduced to the use of R in Analytics , where you will learn a little about each object type in R and use that in Data Mining/Analytical Operations.

After that, you will learn the use of R in Statistics , where you will see about using R to evaluate Descriptive Statistics, Probability Distributions, Hypothesis Testing, Linear Modeling, Generalized Linear Models, Non-Linear Regression, and Trees.

Following that, the next topic will be Graphics , where you will learn to create 2-dimensional Univariate and Multi-variate plots. You will also learn about formatting various parts of a plot, covering a range of topics like Plot Layout, Region, Points, Lines, Axes, Text, Color and so on.

At that point, the course finishes off with two topics: Exporting out of R , and Creating Functions .

Each chapter is designed to teach you several concepts, and these have been grouped into sub-sections. A sub-section usually has the following:

• A Concept Video
• An Exercise Sheet
• An Exercise Video (with answers)

Why take a course to learn R?

When I look to advancing my R knowledge today, I still face the same sort of situation as when I originally started to use R. Back when I was learning R, my approach was learn by doing. There was a lot of free material out there (and I refer to that early in the course) that gave me a framework, but the wording was highly technical in nature. Even with the R help and the free material, it took me up to a couple of months of experimentation to gain a certain level of proficiency. What I would have liked at that time was a way to learn the fundamentals quicker . I have designed this course with exactly that in mind.

Why my course?

For those of you that are new to R, this course will cover enough breadth/depth in R to give you a solid grounding. I use simple language to explain the concepts. Also, I give you 140+ exercise questions many of which are based on real world data for practice to get you up and running quickly, all in a single package. This course is designed to get you functional with R in little over a week .

For those beginners with some experience that have learnt R through experimentation, this course is designed to complement what you know, and round out your understanding of the same.

Who this course is for:

• Enterprise Data Analysts
• Students
• Anyone interested in Data Mining, Statistics, Data Visualization

https://www.udemy.com/course/introduction-to-r/