Business Information Visualization Skills Practical Tableau Quick Start!

You will learn how to develop data/machine learning web applications through 5 practical projects. You can easily and quickly build and deploy web applications with Python and Streamlit. (Updated in June 2025)

Time Series: Become a Data Analysis Expert! Let's be analysts strong in theory & practice!

ADsP Summary + Past Exam Solutions. Reflects August 2025 46th Exam! (Continuously Updated!)

ADsP Certification All-In-One Course! Ultra-Short Completion! Reference Book: "Everyone's ADsP Cheat Sheet" https://wikidocs.net/book/18829

I'll explore ADsP certification passing strategies and learn summarized memorization subjects. Reference book: "Everyone's ADsP Cheat Sheet" https://wikidocs.net/book/18829

Finally, the Probability and Statistics course for everyone is here! Learn the principles of probability and statistics step by step through theory and coding practice. Reference book "Probability and Statistics for Everyone" https://wikidocs.net/book/18165

What do you learn in the 'Business Information Visualization Certification' course? This certification course focuses on developing the ability to analyze data well, visualize it in an easy-to-understand way, and clearly and effectively communicate the information needed when making important decisions in a company or organization. Simply put, it's a course where you learn the skills to not just look at data, but to discover the hidden meanings within it and express them through pictures, tables, graphs, etc., so you can explain them well to others. Through this course, you will: Develop the ability to think strategically through data, build practical skills that can be immediately applied to actual work, and enhance your professional expertise and competitiveness. Through this lecture, develop the ability to connect business and data, and take one more step toward becoming an expert who understands and explains data.

This course was created for those who are preparing for the Big Data Analyst practical exam. It will help you pass the exam by learning as concisely as possible, and it will also provide an opportunity for those who are considering their career path to lightly learn about coding and machine learning.

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.

I'll explore ADsP certification passing strategies and learn summarized memorization subjects. Reference book: "Everyone's ADsP Cheat Sheet" https://wikidocs.net/book/18829

The Certified Big Data Analyst exam is a nationally recognized certification administered twice a year. This course provides detailed lectures covering everything from the basics to the core concepts to help you prepare for the practical exam. The exam difficulty increases with each iteration. To address this, we provide a range of essential content, starting with the fundamentals, to improve your chances of passing.

Students who want to learn Hadoop and Big Data will celebrate the amazing advancements in experiencing the world of Big Data through this course!

Through this lecture, students are expected to be able to check actual materials such as big data contests and self-introduction essays and determine the direction of their studies and job preparation.

Here are some key competencies that are often overlooked by others in the fiercely competitive and complex semiconductor process job market.

This is a lecture on the National Technical Qualification Big Data Analysis Technician Practical with Python. We hope you all pass!

SQLD, where do I even start? This course is for those who feel completely lost - you'll meet a transformed version of yourself after completing it. SQLD All-in-One Pass!

This course covers practical strategies for leveraging AI in the machinery and metal processing industry to enhance productivity and quality. It analyzes key process data from CNC, welding, cutting, and other operations, explaining how AI technology is applied to reduce defect rates, enable predictive maintenance, and manage worker safety through real-world case studies. Learners will understand AI-based processing optimization methods that can be immediately implemented in the field, from small and medium enterprises to large corporations, and gain insights to realize manufacturing innovation in global competition.

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.

This is a practical project lecture that collects data and analyzes big data using Arduino and wind sensors.

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.

Time Series: Become a Data Analysis Expert! Let's be analysts strong in theory & practice!

Do you want to get a job as a data analyst? Based on my experience, let's take a look at the tasks for each data role in real companies.

Repetitive pipeline management in Data Warehouses (DW), now free yourself from that painful cycle with dbt! Building on the efficiency provided by dbt, become a data analytics engineer who creates higher value through business-centric data modeling, effective data lifecycle management, and more.

Finally, the Probability and Statistics course for everyone is here! Learn the principles of probability and statistics step by step through theory and coding practice. Reference book "Probability and Statistics for Everyone" https://wikidocs.net/book/18165

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 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.