Udemy R Programming

Prohibitive · May 22, 2022

You will learn the most important tools in R that will allow you

to do data science
You will learn to install R studio
You will have the tools to tackle a wide variety of data science

challenges, using the best parts of R.
You will learn to import your data into R
You will learn how to Tidy the data. Tidying your data means

storing it in a consistent form that matches the semantics of the

dataset with the way it is stored.
You will learn Visualisation, it is a fundamentally human

activity. A good visualisation will show you things that you did

not expect, or raise new questions about the data
You will learn Models, they are complementary tools to

visualisation. Once you have made your questions sufficiently

precise, you can use a model to answer them. Models are a

fundamentally mathematical or computational tool, so they

generally scale well.

Requirements

You should be generally numerically literate, and it�s helpful if

you have some programming experience already.

Description

Data science is an exciting discipline that allows you to turn raw

data into understanding, insight, and knowledge. The goal of �R

for Data Science� is to help you learn the most important tools in

R that will allow you to do data science. After going through this

course, you�ll have the tools to tackle a wide variety of data

science challenges, using the best parts of R.

What you will learn

Data science is a huge field, and there�s no way you can master it

by going through a single course. The goal of this course is to

give you a solid foundation in the most important tools

First, you must import your data into R. This typically means that

you take data stored in a file, database, or web API, and load it

into a data frame in R. If you can�t get your data into R, you

can�t do data science on it!

Once you�ve imported your data, it is a good idea to tidy it.

Tidying your data means storing it in a consistent form that

matches the semantics of the dataset with the way it is stored. In

brief, when your data is tidy, each column is a variable, and each

row is an observation. Tidy data is important because the

consistent structure lets you focus your struggle on questions

about the data, not fighting to get the data into the right form

for different functions.

Once you have tidy data, a common first step is to transform it.

Transformation includes narrowing in on observations of interest

(like all people in one city, or all data from the last year),

creating new variables that are functions of existing variables

(like computing speed from distance and time), and calculating a

set of summary statistics (like counts or means). Together,

tidying and transforming are called wrangling, because getting

your data in a form that�s natural to work with often feels like a

fight!

Once you have tidy data with the variables you need, there are two

main engines of knowledge generation: visualization and modelling.

These have complementary strengths and weaknesses so any real

analysis will iterate between them many times.

Prerequisites:

You should be generally numerically literate, and it�s helpful if

you have some programming experience already.
Who this course is for:

Beginner R programmers curious about data science

[b]Download File[b]
https://rapidgator.net/file/31fdd235c96fbfe2855bfc8085bd92dd
https://rapidgator.net/file/93060990c7654a3ce9f690748656fda9
https://rapidgator.net/file/d0ee33f37e849be97912139491dee5a7
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Udemy R Programming

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