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**Course Description**

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)

**What are the 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

**What am I going to get from this course? **

- 90 videos (15+ hours)
- To educate you on the fundamentals of R
- 140+ exercise problems
- To accelerate your learning of R through practice

**Who is the target audience? **

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

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