dbt, 데이터 분석 엔지니어링의 새로운 표준

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.

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.

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.

SQLD Yellow Book 176 Problem Solving Lecture for You Who Studied but Cannot Solve Problems. After Completing the Course, You Will Become an Expert Beyond Passing. SQLD All-in-One Pass!

Real-time data processing is now essential, not optional! Learn to handle real-time streaming smartly with Apache Flink. Batch? Streaming? Even complex concepts can be understood easily and quickly. Hands-on practice configuration where you'll get the hang of it by directly working with Kafka and DB integration! In a world driven by data, start first with Flink.

We introduce methods for planners and marketers without data analysis experience or technical skills to try data analysis at the most basic level, complete with various examples. Built on years of hands-on practice with over 2,000 participants in more than 100 companies and public institutions, the content is designed around the most practical analysis methods for data non-experts.

We will explain ElasticSearch, LogStash, and Kibana from the basics. In the final step, you will find yourself using the ELK stack to analyze world population and stocks.

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.

Learn various features of Looker Studio (formerly Google Data Studio) using GA4 demo data. This includes explanations and hands-on practice with Looker Studio's visualization elements. In particular, marketers and planners who are not familiar with SQL can learn easy ways to integrate and use BigQuery data with Looker Studio. You can learn methods to create dashboards using Looker Studio.

This is a course for PMs who want to utilize data. This course covers the entire process of utilizing data in the PM role, including the flow of work from day 1 of a job change through the start and end of a project. It is an introductory data course that covers data-driven thinking, logical thinking, metric definition, log design, experiment design, creating a data culture, and more, to help you develop critical thinking skills. Although created as a course for PMs, it is structured with content that data analysts would also find beneficial (in fact, many analysts have taken the course). In addition, many people in marketing, design, and business planning roles are also taking the course.

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

Data Analysis that Determines Online MD Performance! Learn essential data analysis and strategy formulation techniques necessary for MDs to maximize sales and profit in the e-commerce market.

This is packed with things I want to tell junior PM/POs who are struggling because they don't have a good mentor. I will generously share various techniques and Mental Models used by effective PM/POs.

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!

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.

I will guide non-majors and beginners to quickly pass the Big Data Analysis Engineer practical exam! Theory lightly, practice thoroughly. Even without complex background knowledge, focusing on past questions, we intensively study only the points sure to appear on the exam.

This is a specific and practical course for Google Analytics beginners. Instead of focusing on fragmented features, you can learn the practical analysis process using GA4 by conducting scenario-based analysis. It is continuously being updated to match the latest GA4 revisions!

It improves logical thinking skills, develops an understanding of how to use programming tools, and develops the ability to design algorithms for problem solving.

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!)

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.