Solve problems with R and AI Tutor! Master practical data analysis

Feeling overwhelmed by where to start with complex data analysis? I will clearly guide you through how to utilize R and AI Tutors using the practical know-how I've built up.

1 learners are taking this course

Level Beginner

Course period Unlimited

Statistics
Statistics
R
R
AI
AI
ChatGPT
ChatGPT
LLM
LLM
Statistics
Statistics
R
R
AI
AI
ChatGPT
ChatGPT
LLM
LLM

What you will gain after the course

  • Data preprocessing and processing skills using R and AI Tutor

  • Statistical Analysis and Interpretation of Results for Real-World Problem Solving

  • LLM-based efficient code writing and analysis automation

  • Effective Data Visualization for Deriving Business Insights

  • Planning and executing an intermediate-level data analysis project

From the basics of R data analysis
to AI-based practical problem solving

Take your data analysis skills to the next level.


Are you feeling overwhelmed about where to start with data analysis?
An instructor with extensive practical experience will show you how to use R and AI Tutor
to clearly analyze complex data and derive practical insights. Build real-world problem-solving skills by strengthening your systematic analytical abilities and AI utilization capabilities.


Solve problems with R and AI Tutor!
This course strengthens your practical data analysis capabilities.

You will systematically learn the entire process of data analysis, including data preprocessing, statistical analysis, visualization, and code automation, using R, AI Tutor, and LLM.
The course covers everything from the KoNLP, dplyr, and ggplot2 packages to AI-based prompt engineering. và cả kỹ thuật prompt dựa trên AI.



You will go beyond basic syntax to learn how to solve complex data problems encountered in the real world and maximize analysis efficiency through AI.
You will develop practical problem-solving skills, from planning data analysis projects to interpreting results.



From setting up the RStudio environment to data manipulation using dplyr, advanced visualization with ggplot2, text mining using KoNLP, and LLM-based R prompt engineering with AI Tutor,
you will gain practical experience by directly implementing the entire data analysis process.

Mastering Data Analysis
with R and AI Tutor

Section 1 - Setting Up the R Data Analysis Environment and Understanding the Basics

Learn the basic concepts of the R programming language, how to set up the development environment, and how to utilize the RStudio IDE. Complete the preparation for hands-on practice by installing essential tools for data analysis and configuring the environment.

Section 2 - Handling R Data Types and Core Structures

You will learn how to understand and manipulate various data types used in R (Numeric, Character, Logical) and core data structures such as vectors, factors, matrices, arrays, data frames, and lists.

Section 3 - Mastering External Data Integration and Input/Output (I/O)

You will learn practical data input/output techniques for importing various external data formats, such as TXT, CSV, and Excel, into R and exporting R objects to external files.

Section 4 - Korean Text Mining Practice Using KoNLP

Install the KoNLP package, set up the Java environment, and practice Korean morphological analysis, noun extraction, and text data visualization (bar charts, word clouds).

Section 5 - Data Preprocessing and Manipulation using dplyr

You will learn how to efficiently filter, select, sort, create derived variables, and summarize data using the filter(), select(), arrange(), mutate(), summarise(), and group_by() functions of the dplyr package.

Section 6 - Utilizing ggplot2-based Data Visualization Techniques

Learn how to create various types of graphs such as scatter plots, bar charts, line graphs, and box plots using the ggplot2 package, and gain in-depth knowledge of aesthetics settings and data visualization techniques.

Section 7 - Unstructured Data Cleaning and Advanced Text Mining Applications

Apply advanced text mining techniques, such as string processing using regular expressions, parsing hip-hop lyric data, applying color palettes with RColorBrewer, and word cloud visualization.

Section 8 - Basics of Inferential Statistics and Practical Application of Probability Distributions

Learn the basic concepts of probability and how to visualize and analyze binomial and normal distributions using R. Perform probability analysis, such as inventory management, using practical data.

Section 9 - LLM-based R-Statistics Prompt Engineering

Understand the principles of Large Language Models (LLMs) and learn how to design and build customized AI Tutors for R and statistical analysis. Master error debugging and the use of Gemini.

Are you feeling overwhelmed by complex data analysis and unsure where to start?
This course was created specifically for people like you.


✔️ Learners who want to develop practical data analysis skills using R and AI Tutor

  • Those who want to systematically learn everything from R basics to advanced analysis techniques

  • Those who want to improve their data analysis code writing and troubleshooting skills using an AI Tutor

  • Those who want to strengthen their statistical analysis and result interpretation skills by working with real-world data

✔️ Professionals who want to innovate their data analysis workflow with AI tools

  • Those who want to experience efficient code writing and analysis automation using LLMs such as ChatGPT

  • Those who want to efficiently perform the entire process of data preprocessing, manipulation, and visualization with AI

  • Those who want to take their data-driven business insight derivation skills to the next level

✔️ Aspiring data analysts who want to go beyond the beginner level and acquire practical problem-solving capabilities

  • Those who want to develop the ability to plan and execute complex data analysis projects by integrating R with an AI Tutor

  • Those who want to go beyond theoretical learning and gain experience solving various data problems that can be encountered in actual professional fields.

  • Those who want to learn effective visualization techniques to increase the persuasiveness of data analysis results


Don't hesitate in front of data analysis anymore.
With R and AI Tutor, you too can become an expert who discovers the hidden value of data.

Notes before taking the course


Practice Environment

  • R programming development environment: You must install the latest versions of R and RStudio.

  • Operating System: Supports universal operating systems such as Windows, macOS, and Linux.

  • PC Specifications: For smooth data analysis, 8GB or more of RAM and sufficient storage space are recommended.

Prerequisite Knowledge and Precautions

  • A basic understanding of data analysis is required.

  • It is recommended to have a beginner-level learning experience with the R programming language.

  • Experience using LLMs such as ChatGPT will be helpful for utilizing the AI Tutor.

Learning Materials

  • Utilize the practice data files (CSV, TXT, etc.) provided in the lecture.

  • Save the code snippets and analysis results generated within RStudio.

  • Refer to major R package documentation such as KoNLP, dplyr, and ggplot2.


Recommended for
these people

Who is this course right for?

  • Learners who want to take their data analysis skills to the next level using R and AI Tutor

  • Practitioners who want to innovate their data analysis workflows using AI tools

  • Aspiring data analysts who want to move beyond the beginner level and acquire practical problem-solving skills.

  • Planners and researchers who need to analyze complex data and derive business insights

Need to know before starting?

  • Basic computer literacy (file management, program installation, etc.)

  • Interest in data analysis and a willingness to learn

  • No prior knowledge of R programming or statistics required (learning from the basics)

Hello
This is ywjang23583

I worked as a developer at LG Electronics, a telecommunications company, for about 27 years. Since retiring, I have been teaching introductory software coding courses at various universities, as well as lecturing at vocational schools and government offices. Currently, I am teaching an IoT course at a vocational training school.

I would like to record and share lectures on the following topics.

1. R Statistics Basic/Advanced Course

2. Arduino for the sensor data collection part of IoT technology techniques

3. Raspberry Pi Technology

4. Basic/Advanced Course for AI Utilization (Understanding Basic Algorithms and Tool Usage)

5.Systematic platform implementation techniques for smart farm configuration

6. Tableau and PowerBI visualization techniques

7. Six Sigma technical techniques in the field

8. Building a Big Data Analysis Hadoop Ecosystem

More

Curriculum

All

33 lectures ∙ (16hr 20min)

Course Materials:

Lecture resources
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