MySQL Master Course for Data Analysts and IT Engineers!

SSIS, SSAS, MachineLearning, BI, ETL. You can learn important technologies that cannot be found in domestic books, YouTube, lectures, blogs, and academies. I also recommend it to those who are interested in employment at domestic large companies, American large companies, and American state funding agencies.

SQL data analysis! We will teach you how to solve practical problems that are applicable in the field, quickly and easily!

Introduction to SQL Data Analysis! Quick and easy to understand with practical problem solving!

Sometimes, data processing is blocked and frustrating. If you are not a professional programmer or developer, learning unfamiliar fields such as Python or Java is not easy and there is not enough time. This is for those who only know Excel and SQL basics when it comes to frequently used things. This is the first part of the SQL query book that you can find and use when you need it.

Learn how to write SQL queries and work with databases using the MariaDB client development tool HeidiSQL.

By implementing various practical data analysis cases using SQL, you can simultaneously improve your data analysis and SQL utilization skills.

In this lecture, we aim to create a Web Scraping API server using the Naver Shopping site. We will show you how to configure a managed API server by connecting MySQL and configuring a 3-tier architecture!!

We will tell you everything about how to build a server and perform uninterrupted deployment for stateful services such as Chijijik and AfreecaTV.

I've created a 'Redis Introduction/Practical (Query Performance Optimization)' course that non-majors can easily understand and immediately apply in practice!

It's now an era where data speaks, not intuition. Learn data analysis easily with ChatGPT!

This is a code lab-based class where you will directly build a big data system or distributed processing system cluster (HDFS, Zookeeper, Spark, Zeppelin) that guarantees high availability.

If you want to be recognized as a machine learning expert based on large-scale data, from understanding the core framework of Spark machine learning, to SQL-based data processing through difficult practical problems, to data analysis through business domain analysis, and to the ability to implement optimized machine learning models, please join this course.

Make the most of relational database systems!

This course teaches MS-SQL through baseball/company databases.

From understanding SQL to using actual functions, Complete preparation for SQLD certification with the latest past exam questions!

This course is a course that practices the basics of machine learning mathematics and statistics using Python. Machine learning and deep learning require programming skills, but they also require a mathematical background to understand the principles. Through this course, you can gradually learn the basic mathematics required for artificial intelligence.

For those who feel overwhelmed preparing for the SQLD exam, this is a comprehensive course that analyzes the entire Norang-i scope, organizes necessary theory, conducts practice-focused classes, and solves all problems systematically. With short, concise clips and topic-based segmented structure, you can build skills that work directly in the exam room.

Here’s an example of the synergy that can be created when combining Tableau Desktop and Server/Online. Let’s build a pipeline together to see how data can lead to concrete actions!

This is a special lecture that can lay the theoretical foundation to simultaneously prepare for the Microsoft AZ-900 certification, and as the latest content reflecting the scope of questions as of May 2025, by providing content related to core data concepts, Azure's relational data, Azure's non-relational data, and Azure's analytical workloads in a form combining theory and practice, it can be utilized as a meaningful educational opportunity to not only obtain the certification but also take the first step towards becoming a data professional.

We explored the essential Python syntax necessary for big data analysis and, through various examples, presented methods such as big data file processing and visualization.