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Mastering Data Preprocessing with R – Complete Conquest for Practical Use Part 1 #BigDataAnalysisCertificationPracticalExam

Practical R Data Preprocessing for Non-Majors. After taking this course: You will be able to confidently clean any form of messy data. You will be able to build an entire workflow from data preprocessing to visualization. You will be able to shorten your work hours by automating repetitive preprocessing tasks. You will acquire code templates that can be applied immediately in practice. All exercises in this course use R and RStudio, which are open-source and free programs. Experience the powerful tools of data preprocessing firsthand without any cost burden. The class will be conducted by setting up the standard environment for practical data analysis. Note: [Practice Environment Guide] R and RStudio Installation Guide

Data Engineering
Data literacy

5회 미션 수행

질문하고 즉시 답을 얻어요.

aha30005480님과 함께해요!

Career Verified

Data Analysis · AI Specialist Instructor

  • Delivered numerous data analysis and statistics training sessions for companies and public institutions

  • Full-time instructor for the Big Data and AI program at a vocational training institute

  • Expert in practical work and education in data analysis, statistics, and social research

  • Data analysis and statistics hands-on training using SPSS, R, and Python

  • Operates courses preparing students for the Social Research Analyst and Big Data Analysis Engineer certification exams

  • Books: Social Survey Analyst Level 2 Practical Exam: Task-Based, SPSS, and Written-Response Questionnaire Design Methods, Gumin-sa; Social Survey Analyst Level 2 Written & Practical Exam (Written-Response); R Fundamental as a Data Analysis Tool; Data Scientist Series Step1~4; Building Big Data for AI Analysis (Structured and Unstructured Korean Data)

I am a professional instructor who delivers data analysis and statistics in an easy, practical way, from corporate and public institution training to certification preparation.

 

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Data Literacy with R – Mastering Preprocessing (Part I & II)

Have you been feeling overwhelmed, not knowing where to start with data analysis?

80% of data analysis is preprocessing. No matter how many great analysis techniques you know, you cannot get meaningful results if the data is messy. This course systematically covers essential data preprocessing techniques for practical work from start to finish using R.

This is not a lecture where you simply type along with the code. We will help you develop true data literacy by understanding why we process it this way and how it is used in practice.


This is a course consisting only of 'missions' without live sessions or videos.


The source and code can be downloaded below.

https://naver.me/5yhkc8u9

The video URL guide is available below the course.


Data Preprocessing with R – Complete Mastery for Immediate Practical Use Part I

Part I – Building the foundational strength to understand and clean data

Recommended for the following people

  • Beginners starting data analysis for the first time

  • Those who have learned R but found it difficult to apply in practice

  • Job seekers who have struggled with handling missing values and outliers

  • Graduate students and researchers who need to clean thesis or research data themselves

  • Office workers who feel the limitations of Excel and want to switch to R

Prior knowledge

No prior knowledge is required. However, if you have experience with basic R syntax and reading tabular data, you will find it even easier to follow the course.


To the students

I sincerely welcome all of you who have chosen to learn despite your busy daily lives. If you have any questions while listening to the lectures, please feel free to leave them at any time. I will be with you until the very end, until the day you can handle data with confidence.

3월

31일

챌린지 시작일

2026년 3월 31일 오후 03:00

챌린지 종료일

2026년 5월 31일 오후 02:30

챌린지 커리큘럼

All

5 lectures

Course Materials:

Lecture resources

챌린지에서 배워요

  • R Data Analysis Preprocessing A to Z – The Complete Guide to Exploration, Cleaning, Merging, and Derived Variables

  • Practical R Data Preprocessing: Learning Big Data Literacy Properly from the Start

Recommended for
these people

Who is this course right for?

  • Anyone who wants to improve their data literacy and everyone who wants to derive meaningful insights from data on their own.

  • Data analysis beginners who are learning R for the first time, or those who want to work with data but don't know where to start.

Need to know before starting?

  • There are no mandatory prerequisites. However, having even a basic understanding of the following will help you follow the course more easily.

  • It is good to have a basic understanding of R concepts such as basic syntax, variable declaration, vectors, and data frames. (It's okay if you don't know them; we will cover them together at the beginning of the lecture.)

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