dbt, The New Standard of Data Analytics Engineering

In this class, you will learn about the principles and methods of analyzing and processing various types of financial data using Python's Pandas library, apply them to situations you may encounter in the real world, and ultimately learn how to implement backtesting based on financial statement data (based on Kang Hwan-guk's book, "You Can Do Quant Investment"). As a result, you can break away from being a "passive investor" who simply follows what others say without verifying or basing the investment logic, and become a "self-directed and active investor" who can freely extract various elements necessary for strategy implementation from data and quantitatively analyze them using Python and Pandas.

In this class, you will learn techniques for retrieving (crawling) and processing data from various operating sites using Python, and apply them directly to various examples that you may encounter in the field. Through this, you will learn techniques that focus on solving practical problems based on an understanding of the basic principles of how the web works, rather than one-dimensional task automation by copying and pasting given codes, and accordingly, you will be able to collect and process data from any site in the future. You will also learn how to resolve various exceptions that occur in this process with code, and ultimately, you will be able to solve and automate various problems that occur in daily life with programming thinking.

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.

The first class in the curriculum, "Anyone Can Learn Python", is an introductory programming course for those with no programming/coding experience. Based on my experience with numerous classes taught to non-majors, this class focuses on the "story" and "principles" from the structure of a computer to how programs work and the role of programming languages in this process, allowing beginners to approach the unfamiliar concept of programming more familiarly. In addition, by adopting the Python language, which has recently been growing at an incredible rate, the barrier to entry for programming has been lowered even further, and when explaining the grammatical elements of the language, the focus is on "Why" and "How" rather than "What", so that it is not a simple grammar memorization class, but rather on real-world examples and practical applications. Based on the incorrect learning methods that beginners often use, it also covers tips on how you should view and learn programming in the future, and based on the trial and error I experienced while struggling and wasting time while teaching programming on my own in the past and the inner strength I gained from conducting offline classes for many years, it focuses on what beginners find difficult and how to convey it, so it will be of great help to those who are hesitant about getting started with Python.

If you work with data, SQL is a fundamental skill you must know. In this course, we will learn SQL from a data analysis perspective, focusing on hands-on practice. The practice will be conducted in Google Colab using DuckDB.

"Big Data + AI Full Stack Development" - The process of building big data that breathes life into AI is carried out in the form of a pilot project. We identify requirements, configure the platform step by step, and perform smart car data exploration and analysis.

Uncover true value in the age of data! 📊 Designing applications focused on data has now become essential. Develop the insight and skills your company wants with the latest trends and practical case studies. Start now with efficient data processing and design secrets! Your next step, leap into the data-driven world!

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

This course is designed for those aiming to obtain the Data Architecture Semi-Professional (DAsP) certification. It provides a simple and systematic overview of essential exam topics, ranging from basic data architecture concepts to data modeling, data quality, and governance. The curriculum is structured to minimize complex theories and focus on frequently tested concepts, making it accessible for non-majors and beginners to learn without pressure. Build your fundamental skills in understanding data structurally while preparing for the DAsP certification.

Through detailed lectures and hands-on practice on the core elements of SQL, we will provide you with a solid foundation to grow into a SQL analysis expert.

A beginner's guide to SQL coding! We'll teach you the secrets of writing, interpreting, and validating SQL.

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.

Learn the process of building data pipelines using Apache Airflow from basics to practical application. Understand Airflow's core concepts and architecture, and master advanced design patterns frequently used in practice such as dynamic DAGs, parallel processing, distributed processing, and Custom Operators through hands-on exercises. Set up a practice environment with Python and Docker, and develop practical skills to design and operate real workflows.

The "Beginner Java Developer Project Basic Terminology Guide" course is an introductory guide that helps development beginners and new Java developers easily understand essential IT terminology frequently encountered in the workplace and apply it directly to projects.

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.

Talking about ‘Developer, Things That Helped Me Grow’ _Seo Ji-yeon AI Engineer _Kim Young-jae Technical Fellow _Ha Jae-seung Senior Developer _Jo Eun Lead Developer _Kim Sang-woo Data Group Headquarters Manager _Gom Coach Agile Coach

SQLD Yellow Book 176 Problem-Solving Course for those who studied but can't solve problems. After completion, you'll go beyond passing to become an expert. SQLD All-in-One Pass!

You can quickly convert MATLAB code to PYTHON by extracting only the PYTHON grammar that matches the MATLAB grammar.

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!

"Level Up Your Python Alone!" Starting from Python basics, all the way to creating 2D games and an Ollama-based AI chatbot! This is a practice-focused introductory course where you learn core syntax like variables, conditional statements, and functions, and improve your skills by building things yourself.

Did you know that there are different types of data analysts? If you want to know everything about in-house data analysts and consulting data analysts, come to this seminar!

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

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