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Autor Tópico: R for Data Science Learn Data Manipulation With R  (Lida 267 vezes)

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R for Data Science Learn Data Manipulation With R
« em: 01 de Outubro de 2019, 18:36 »

R for Data Science: Learn Data Manipulation With R
.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | 2.32 GB
Duration: 4 hours | Genre: eLearning | Language: English
Learn data science with R programming, handle with data, manipulate the data and produce meaningful outcomes with R!

What you'll learn

    Data Manipulation
    Learn how to handle with big data
    Learn how to manipulate the data
    Learn how to produce meaningful outcomes
    Examine and manage data structures
    Handle wide variety of data science challenges
    Select columns and filter rows
    Arrange the order and create new variables
    Create, subset, convert or change any element within a vector or data frame
    Transform and manipulate an existing and real data
    Use the "tidyverse" package, which involves "dplyr", and other necessary data analysis package

Requirements

    No Previous Knowledge is needed!
    A Windows PC, Mac or Linux Computer
    Be able to download and install all the free software and tools needed to practice
    A strong work ethic, willingness to learn and plenty of excitement about the data mining
    Nothing else! It's just you, your computer and your ambition to get started today

Description

Welcome to Data Science with R: Learn Data Manipulation With R course.

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. If you want to advance in your career as a data scientist, R is a great place to start your data science journey.

R is not just a programming language, but it is also an interactive environment for doing data science. Moreover, R is a much more flexible language than many of its peers.

Throughout the course you will learn the most important tools in R that will allow you to do data science. By using the tools, you will be easily handling big data, manipulate it, and produce meaningful outcomes.

In this course, we will examine and manage data structures in R. You will also learn atomic vectors, lists, arrays, matrices, data frames, tibbles and factors and you will master on these. So, you will easily create, subset, convert or change any element within a vector or data frame.

Then, we will transform and manipulate a real data. For the manipulation, we will use the tidyverse package, which involves dplyr and other necessary packages.

At the end of course, you will be able to select columns, filter rows, arrange the order, create new variables, group by and summarize your data simultaneously.

In this course you will learn;

    Examining and Managing Data Structures in R

    Atomic vectors

    Lists

    Arrays

    Matrices

    Data frames

    Tibbles

    Factors

    Data Transformation in R

    Transform and manipulate a deal data

    Tidyverse and more

Why would you want to take this course?

Our answer is simple: The quality of teaching.

When you enroll, you will feel the OAK Academy's seasoned instructors expertise.

Fresh Content

It's no secret how technology is advancing at a rapid rate and it's crucial to stay on top of the latest knowledge. With this course you will always have a chance to follow latest trends.

Video and Audio Production Quality

All our contents are created/produced as high quality video/audio to provide you the best learning experience.

You will be,

· Seeing clearly

· Hearing clearly

· Moving through the course without distractions

See you in the course!

Who this course is for:

    Anyone interested in data sciences
    Anyone interested                                                                                                                                                                                                in statistical courses
    Statisticians, academic researchers, economists, analysts and business people
    Anyone who want to make inferences based on their financial data
    Professionals working in analytics or related fields
    Anyone who is particularly interested in big data, machine learning and data intelligence
    Specialists in various area who need to develop sophisticated graphical presentations of data
       

               
 
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