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R Data Pre-Processing & Data Management - Shape your Data! (Udemy.com)

Learn how to prepare your data for great analytics in R.

4.3
665 reviews

Created by: R-Tutorials Training

Last updated November 2018

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What you will learn

  • import data into R in several ways while also beeing able to identify a suitable import tool
  • select and implement a proper object class (data.frame, data.table, data_frame)
  • convert your data into (and understand) a tidy data format
  • filter and query your data based on a wide range of parameters
  • join 2 data tables together with dplyr 2 table verb syntax
  • use SQL code within R
  • translate basic R into SQL
  • work with dates and time
  • work with strings using regular expressions
  • detecting outliers in datasets

How it compares

Against the other 1,210 rated Udemy courses in Business on CourseDuck:

  • Rating4.3 out of 5higher than 15% of them
  • Reviews665#574 of 1,211 by number of reviews
  • Length6.4 hourslonger than 77% of them
  • Last updatedNovember 2018earlier than 92% of them

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Course Description

Lets get your data in shape!

Data Pre-Processing is the very first step in data analytics. You

cannot escape it, it is too important. Unfortunately this topic is

widely overlooked and information is hard to find.

With this course I will change this!

Data Pre-Processing as taught in this course has the following steps:

1. Data Import: this might sound trivial but if you consider

all the different data formats out there you can imagine that this can

be confusing. In the course we will take a look at a standard way of

importing csv files, we will learn about the very fast fread method and I

will show you what you can do if you have more exotic file formats to

handle.

2. Selecting the object class: a standard data.
frame might be

fine for easy standard tasks, but there are more advanced classes out

there like the data.
table. Especially with those huge datasets nowadays,

a data.
frame might not do it anymore. Alternatives will be demonstrated

in this course.

3. Getting your data in a tidy form: a tidy dataset has 1 row

for each observation and 1 column for each variable. This might sound

trivial, but in your daily work you will find instances where this

simple rule is not followed. Often times you will not even notice that

the dataset is not tidy in its layout. We will learn how tidyr can help

you in getting your data into a clean and tidy format.

4. Querying and filtering: when you have a huge dataset you

need to filter for the desired parameters. We will learn about the

combination of parameters and implementation of advanced filtering

methods. Especially data.
table has proven effective for that sort of

querying on huge datasets, therefore we will focus on this package in

the querying section.

5. Data joins: when your data is spread over 2 different tables

but you want to join them together based on given criteria, you will

need joins for that. There are several methods of data joins in R, but

here we will take a look at dplyr and the 2 table verbs which are such a

great tool to work with 2 tables at the same time.

6. Integrating and interacting with SQL: R is great at

interacting with SQL. And SQL is of course the leading database

language, which you will have to learn sooner or later as a data

scientist. I will show you how to use SQL code within R and there is

even a R to SQL translator for standard R code. And we will set up a

SQLite database from within R.
7. Outlier detection: Datasets often contain values outside a plausible range. Faulty data generation or entry happens regularly. Statistical methods of outlier detection help to identify these values. We will take a look at the implemention of these.
8. Character strings as well as dates and time have their own rules when it comes to pre-processing. In this course we will also take a look at these types of data and how to effectively handle it in R.

How do you best prepare yourself for this course?

You only need a basic knowledge of R to fully benefit from this

course. Once you know the basics of RStudio and R you are ready to

follow along with the course material. Of course you will also get the R

scripts which makes it even easier.

The screencasts are made in RStudio so you should get this program on

top of R. Add on packages required are listed in the course.

Again, if you want to make sure that you have proper data with a tidy

format, take a look at this course. It will make your analytics with R

much easier!

Who this course is for:
Data pre-processing is a crucial step of data related work - therefore this course is intended for all R users
Read More All Levels All Levels product length iconTime: 6.4h

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Instructor Details

R-Tutorials Training

R-Tutorials is your provider of choice when it comes toanalytics training courses! Try it out our 100,000+ students love it.
We focus onData Science tutorials. Offering severalR courses for every skill level, we are among Udemy's topR training provider. On top of that courses on Tableau, Excel and a Data Science career guide are available.
All of our courses contain exercises to give you the opportunity to try out the material on your own. You will also get downloadable script pdfs to recap the lessons.
The courses aretaught by our main instructor Martin trained biostatistician and enthusiastic data scientist /R user.
Should you have any questions, you are invited to check out our website, you can open a discussion in the course or you can simply drop us a pm.
We are hereto help you boost your career withanalytics training Just learn and enjoy.

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Reviews

4.3

665 ratings on Udemy

Select a bar to show only those reviews.Select the bar again to show every rating.

Write a review

By Leonardo Luiz Calado on 9/24/2020

This course is very important to practice fundamentals data organization and treatment. Great tool for this.

By Pule Molise on 8/24/2020

Just what I needed

By Re'vyn Kelly on 8/21/2020

Good overview of Data Pre-Processing and helpful for setting up data before manipulating it. The examples are easy to follow and duplicate. The explanations are also helpful as to why you would do the things that are being shown. The SQL portion was really brief and would have been helpful to see R interact with an actual SQL database.

By Debdip Roy on 7/18/2020

Complex queries are not simplified in many cases , if you are expecting all exparts to join your course , please do mention the same. does not make sense

By Jaroslaw Dziegielewski on 7/18/2020

Rather good, but some info not updated (e.g. olson_time_zones was removed from lubridate few years ago).

By Channing Powers on 7/1/2020

This course is a must for anyone planning to work in the Data industry, using the R programming language.

By Bronwyn Barker on 6/27/2020

excellent course!

By Arief Yudaprawira on 6/22/2020

Very helpful in their daily application of data processing

By Barbara Leighnor on 6/21/2020

I really appreciated all the opportunities for practice

By Columbus Ohaeri on 6/7/2020

good course, just about met my expectation

By Frederico M Cohrs on 6/2/2020

Claro que s o incio. Eu esperava um curso do tipo mos na massa. Como s o incio, vejamos a sequncia.

By Dhirendra Singh on 5/27/2020

Need more practical scenarios examples as reference materials

Showing 12 of 138 reviews

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Overall Score : 86 / 100

R Data Pre-Processing & Data Management - Shape your Data!
Visit course on Udemy Write a review
  • Video Duration: icon 6.4h
  • Price: $54.99
  • Provider: icon Udemy
  • Updated: icon November 2018
  • Level: All Levels All Levels
  • Language: icon English
  • Certificate: icon Yes
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