Class introduction

[100% student satisfaction! Once you've learned the basic grammar of R and the overall concept of data analysis through [R Data Analysis Starting from the Basics - Introduction], let's go one step further through the intermediate course. We have prepared an intermediate class to improve R data literacy through predictive modeling, text mining, etc.

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Course effect

In addition to structured data, you can also learn how to deal with unstructured data.
You can create data analysis reports that reflect the content of statistics and machine learning.
Instead of just copy/pasting code, you can improve your computational thinking by writing and learning to code together.

Recommended target

Those who want to learn more in-depth data analysis
Those who want to analyze data using R - Everyone who wants to learn statistics and machine learning
Graduate students who have trouble writing papers using R data analysis - Those who want meaningful statistical verification

Pre-course notes

Course environment: Windows or Mac does not matter
Program used: R Studio
Prior knowledge: You must know basic R grammar. (dplyr, ggplot2, and reshape libraries can be used)
If you don't have enough basic R grammar, we recommend that you take an introductory class in advance.

3 reasons why this class is special

Lectures incorporating vivid experiences

I incorporated all of my experience in 1) more than 100 lectures, 2) data analysis and development practices experienced at the company, and 3) my experience through doctoral programs in statistics and industrial engineering into the class. With proven teaching skills that reach 100% student satisfaction, we will guide you on the best path so that you can quickly learn only the content you need for practical work.

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Step by step roadmap for 'Beginner - Intermediate-Advanced'

1. introductory

Based on an understanding of data analysis processes and processes, you will learn data preprocessing and visualization techniques using R The final step is to create data analysis reports and dashboards.

2. Intermediate (current level)

You will conduct modeling for predictive analysis based on statistics for practical data analysis, and learn machine learning to advance it.

3. Advanced (coming soon)

It focuses on practical project handling techniques based on elementary and intermediate courses. Learn various analytical techniques, including unstructured data, and develop skills to handle data freely.

Curriculum that captures both theory and practice

The curriculum is structured by arranging theory and practice in a balanced manner based on well-structured textbooks written by myself. In addition to simply learning theory, students learn the entire process of data analysis by examining examples used in practice in order and conducting theory and practice together.

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< Practical example of writing an analysis report >

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< Examples of course materials >

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< Data visualization Lab examples >

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< Machine learning practice examples >

Satisfaction is the best! R Data Analysis Intermediate: Statistics/Machine Learning

Satisfaction is the best! R Data Analysis Intermediate: Statistics/Machine Learning