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Big Data and Statistical Analysis Using R

It covers basic data concepts, useful R functions and packages, and data analysis exercises, to allow anyone to perform big data analysis using R programming.

(5.0) 1 reviews

3 learners

Level Beginner

Course period Unlimited

  • kpcre
R
R
Big Data
Big Data
R
R
Big Data
Big Data

What you will gain after the course

  • Through R, one can acquire data scraping and crawling techniques to extract desired data.

  • Can learn data analysis methods using R and create statistical data.

  • Various types of data can be collected and utilized from a business perspective.

The secret to big data collection and statistics, R programming!

We are producing and accessing a lot of information through smart information devices. Depending on how quickly we analyze the data, how we use it in the right place, and what criteria we use to extract the data, it can change marketing, planning, sales, sales, and even the company's vision and direction. R programming is commonly and frequently used for efficient collection and statistics of big data. However, there is still a high barrier to entry due to the prejudice that it is difficult for non-majors and office workers. This course covers basic concepts of data, useful functions and packages of R, and data analysis practice so that anyone can analyze big data using R programming.

Field-oriented education

  • We present R installation, basic functions, data understanding, and visualization methods so that anyone from beginners to non-majors can easily learn data analysis and R programming.


  • The learning is structured so that you can review and apply the learning content through practical examples, and think and solve problems on your own.


Presenting key points centered on practice

  • Topic-based learning is possible through micro-learning centered on core keywords that can be immediately applied in practice.

Course Contents


  1. Understanding R

  2. Setting up the R language development environment

  3. Concept of variable and vector

  4. Creating vectors, functions

  5. Understanding data types (1)

  6. Understanding data types (2)

  7. Using the matrix

  8. Using data frames (1)

  9. Using data frames (2)

  10. Data Input/Output (1)

  11. Data Input/Output (2)

  12. Control statements and functions

  13. Basic Graph (1) - Bar Graph

  14. Basic graph (2) - Histogram, multi-graph

  15. Basic graph (3) - Pie graph, line graph

  16. Basic graph (4) - Box plot, scatter plot

  17. Data Analysis

  18. Data preprocessing (1)

  19. Data preprocessing (2)

  20. Advanced Graph

  21. Utilizing ggplot2

  22. Data Analysis Cases

  23. Data collection and analysis practice (1)

  24. Data collection and analysis practice (2)


Differentiated content provided through instructor recruitment verified through offline education

We have invited lecturers who are well-received for their in-depth lectures at companies, public institutions, and universities to present differentiated content.


CEO Park Sung-baek

Current) Code Campus CEO

Currently, instructor of Python division at Korea Productivity Center

Former instructor at Ssangyong Education Center, SIS Co., Ltd.

Former Mobile Lab Information Education Center Instructor

Former) Instructor at Kyungshilyeon Hi-Tel Information Education Center


Recommended for
these people

Who is this course right for?

  • IT System and Data Management Manager

  • Marketing information collection and analyst

  • Practitioners seeking efficient Big Data collection and analysis

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747

Learners

108

Reviews

9

Answers

4.7

Rating

192

Courses

As an affiliate of the Korea Productivity Center established in 1987, we provide essential job training for employees of corporations and public institutions.

We have developed job-duty-task-based educational content to strengthen practical skills, based on the actual 'work' that occurs in real corporate business environments.

Experience job training on a whole new level!

Homepage : https://www.kpcice.or.kr

Curriculum

All

59 lectures ∙ (14hr 56min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

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1 reviews

5.0

1 reviews

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    porommy3600

    Reviews 1

    Average Rating 5.0

    Edited

    5

    71% enrolled

    $100.10

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