Data science with R: tidyverse

About Data science with R: tidyverse

Data Science skills

are still one of the most

in-demand skills

on the job market today. Many people see only the fun part of data science, tasks like: "search for data insight", "reveal the hidden truth behind the data", "build predictive models", "apply machine learning algorithms", and so on. The reality, which is known to most data scientists, is, that when you deal with

real data

, the most

time-consuming operations

of any data science project are: "

data importing

", "

data cleaning

", "

data wrangling

", "

data exploring

" and so on. So it is

necessary

to have an

adequate tool

for addressing given data-related tasks. What if I say, there is a freely accessible tool, that falls into the provided description above!

R

is one of the

most in-demand programming languages

when it comes to

applied statistics

,

data science

,

data exploration,

etc. If you combine

R

with R's

collection of libraries

called

tidyverse

, you get one of the

deadliest tools

, which was designed for

data science-related tasks

. All

tidyverse

libraries share a

unique philosophy

,

grammar

, and

data types

. Therefore libraries can be used

side by side

, and

enable

you to write

efficient

and more

optimized R code

, which will help you

finish projects faster

. This course includes several

chapters

, each chapter introduces

different aspects

of

data-related tasks

, with the proper

tidyverse

tool to help you deal with a given task. Also, the course brings to the table

theory related to

the

topic

, and

practical examples

, which are

covered

in

R

. If you dive into the course, you will be engaged with

many different data science challenges

, here are just a few of them from the

course

:

Tidy data

, how to clean your data with

tidyverse

?

Grammar of data wrangling

.

How to

wrangle data

with

dplyr

and

tidyr

.

Create

table

-like objects called

tibble.

Import

and

parse data

with

readr

and

other libraries

.

Deal with

strings

in R using

stringr

.

Apply

Regular Expressions concepts

when dealing with strings.

Deal with

categorical variables

using

forcats

.

Grammar of Data Visualization

.

Explore data

and

draw statistical plots

using

ggplot2

.

Use concepts of

functional programming

, and

map functions

using

purrr

.

Efficiently deal

with

lists

with the help of

purrr

.

Practical applications of relational data.

Use

dplyr

for relational data.

Tidy evaluation

inside tidyverse.

Apply

tidyverse tools

for the

final practical

data science

project

. Course includes:

over

25 hours

of

lecture videos

,

R scripts

and

additional data

(provided in the course material),

engagement with

assignments

at the end of each chapter,

assignments walkthrough videos

(where you can check your results). All being said this makes one of

Udemy's most comprehensive courses

for

data science-related tasks

using

R

and

tidyverse

.

Enroll today and become the master of R's tidyverse!!!