Introduction to Data Analysis

Let's develop the ability to find rational decision-making models based on mathematical thinking by understanding various elements of AI and data, and solving real-world problems using ChatGPT and no-code programming!

Let's build the skills to develop web applications using React and cultivate the capability to apply them in practice!

Java Programming Based on Object-Oriented Concepts for Computer Science Majors

Basic manners, etiquette, and communication skills training to foster global leaders leading the era of the Fourth Industrial Revolution.

Let's understand the major algorithms of machine learning and deep learning, and get to know artificial intelligence!

Experience text-based coding through block coding and let's control an Arduino!

Let's learn various data structures and algorithms and develop practical skills by implementing them through programming!

Let's design, implement, and develop while learning the basic and object-oriented concepts of the C++ language!

Modern C++ Programming based on Object-Oriented Concepts for Computer Science Majors

Let's use the R language, which processes data easily and conveniently, to help even non-computer science majors develop their skills in utilizing computer programs related to their own fields!

It improves logical thinking skills and cultivates the capacity to converge one's major with information and communication technology in the era of the Fourth Industrial Revolution.

Are you a digital talent interested in the Internet of Things (IoT)? Let's get started with IoT through Arduino!

Let's acquire the Python coding skills necessary for artificial intelligence by learning the basics of the Python programming language!

Let's look into how to deploy Abyss for non-majors!

Are you a digital talent interested in coding and the Internet of Things (IoT)? Start your journey into programming with MicroPython!

Let's understand machine learning and deep learning model algorithms by using Keras!

Let's develop data-driven administrative practical skills to empirically solve administrative and policy problems by analyzing public data with R and Python!

Let's develop basic field knowledge of clothing studies and the skills of a fashion specialist through an understanding of AI-based textile CAD!

Are you a digital talent interested in coding and development? Get started with programming through p5.js & ml5.js!

Let's become creators using ChatGPT and generative AI!

Have you studied Python syntax but felt lost when it came to actually handling data? You know you need to learn NumPy and Pandas, but if you've been wondering where to start and how to connect them, this PART 2 is the answer. In PART 2 of our 50-lecture data analysis curriculum, you will experience the step-by-step process of loading, cleaning, processing, and statistically interpreting real data. You won't just learn libraries and syntax; you will establish criteria for which tools to choose in specific situations. PART 2 is the turning point where you move from simply knowing Python code to actually being able to handle data.

Let's build a solid foundation in Python and solve data analysis problems!

This is a course for beginners in stock data analysis. Get started with stock data analysis using Python!

Learn a systematic approach to S&P 500 pair trading with Python. Lay the foundation for an investment strategy that excludes emotions through professional data analysis.

We guide non-majors and beginners to quickly obtain the Big Data Analysis Certification (Practical Exam)! Keep the theory light and the practice solid—focusing on core points that are guaranteed to appear on the exam through past questions, without the need for complex background knowledge.

Learn NoSQL technology for handling big data, one of the fundamental skills in full-stack and data science technologies used by modern startups. MongoDB is the easiest and fastest NoSQL technology to utilize. In this course, you will learn the basics of MongoDB in a short time and master the skills to handle and utilize MongoDB with Python.

This lecture is a follow-up lecture to 'Python Data-Based Stock Quant Investment Part 1'. If Part 1 was more of an introduction, Part 2 is an in-depth lecture that focuses on the entire flow of practical strategy implementation and quantitative investment development. This class focuses on advanced Pandas techniques for handling time series data, and how to implement signal-based strategies and static/dynamic asset allocation strategies that require adjusting asset weights at various intervals based on this. Furthermore, it goes beyond strategy implementation and learns about 'code framework' that directly verifies and backtests various investment strategies with minimal code modifications, how to extend this to improve it so that it can lead to actual investment, and what to watch out for in this process. In addition to the programming component, you can experience the best Python quant investment flow that you cannot find in investment books, blogs, YouTube, etc. by deeply covering theoretical contents such as the two types of return concepts (simple return, log return) and evaluation indicators related to backtesting.

Through this course, you can learn SQL and database fundamentals, SQL syntax, database design for practical applications, database utilization through programming, and data analysis use cases.

Let's use the R language, which processes data easily and conveniently, to help even non-computer science majors develop their skills in utilizing computer programs related to their own fields!

If you're just learning Python and don't know what you can do or what you should do? Let's learn how to use web technologies with Python, such as web crawling, automation, and APIs.

This course aims to acquire Python-based web crawling technology to collect data from web pages and improve data collection and analysis capabilities at work based on this.

Build data-driven investment strategies with Python quant investing and put them into practice. You can implement various asset allocation strategies and ultimately create your own unique investment strategy.

You can learn the skills and know-how needed to improve work efficiency by utilizing Open API and Python.

Based on the instructor's own failed experiences when first learning machine learning, this is a newly designed course that differs from existing lectures, making it easy to understand machine learning and apply it to real-world problems

This course is a hands-on program for practicing the fundamentals of machine learning mathematics and statistics using Python. While machine learning and deep learning require programming skills, a mathematical background is necessary to understand the underlying principles. Through this course, you can step-by-step master the foundational mathematics required for artificial intelligence.

This is a Python basics course for starters who want to grow as data analysts. - Those who want to do data analysis but don't know Python - Those who want to learn machine learning with Python - Those preparing for the Big Data Analytics Engineer practical exam - Those who want to develop machine learning-based applications with Python

* 100% Practical Curriculum: Focused on hands-on data handling rather than grammar-based learning * Essential Machine Learning Prep: A complete summary of data preprocessing techniques required before AI training * Data Analysis Tool Mastery: Conquer core functions of Numpy (numerical operations) and Pandas (data manipulation) * Deriving Visualization Insights: Transferring data visualization skills using Matplotlib and Seaborn * Building Statistical Foundations: Learning basic statistics and group analysis techniques to identify data characteristics

This course is designed for beginners and consists of easy explanations and various hands-on projects. It covers data collection using web crawling and LangChain tools, and summarization, extraction, sentiment analysis, and recommendation systems using LLM.

AI that thinks and adapts? Now you can build it yourself. Master the core of reinforcement learning with intuitive explanations and code, not complex theories, and grow into a 'practical developer' through finance and industry projects.

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