Data-driven storytelling

This content provides basic instructions on how to visualize data using Tableau Public. It is designed to be easily accessible for non-data majors to follow along, and it guides you on how to create effective reports through visualization charts that can be applied immediately in practice.

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.

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 Part 3 of Creating a Python Trading Room for Quant Investment, you will learn about portfolio theory and concepts necessary for real investment, as well as how to implement them with code. In Parts 1 & 2, you mainly learned about code implementation methods for analyzing financial data, and in Part 3, you will learn about 'portfolios', which are an important part of quantitative investment. With the question of why you should create a portfolio, you will learn portfolio theory, implement it in Python, and practice the code necessary for real portfolio investment. I hope this lecture will be helpful for your own portfolio investment.

This is a replay of the eleventh webinar on how to use the free data visualization tool 'HEARTCOUNT' that requires no coding. Instead of spending time learning data or learning tools, spend it finding the information you need from your data for your work.

On your way to work tomorrow morning, your phone will have “Today’s stocks selected by the system and the reasons why” waiting for you. An AI-powered Korean stock auto-trading system that reads foreign and institutional buying trends and handles everything from purchasing to taking profits and cutting losses automatically—you build it yourself, making it truly yours, rather than relying on a program made by someone else.

You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.

"Why does my automated trading system collapse in a declining market?" I, too, have wrestled deeply with the same question. Most automated trading logic on the market may appear to generate profits during bull markets, but the moment market conditions change, it loses its ability to adapt. To solve this problem, I combined the expertise I developed through two years of hands-on research with the latest AI technology. In this course, you will learn how to design a 'Transformer-based deep learning model' that simultaneously reads macroeconomic trends and the movements of individual stocks, and how to automatically train and deploy it in a free GPU environment. In particular, by introducing sophisticated, hedge-fund-level data normalization techniques, you will build an 'intelligent comprehensive scoring system' that goes beyond simple indicators by incorporating short-selling balance and order-flow data. On top of that, an ‘AI safety gate’ infused with the insight of a Wall Street trader with 20 years of experience provides the final confirmation from the perspective of a human trader just before a purchase. Going beyond simply buying and selling, you will implement the entire process of an institution-grade algorithmic system with dynamic take-profit and stop-loss logic, eight self-recovery mechanisms, and real-time Slack reporting—all with your own hands.

In this course, you can learn a wide range of topics, from basic Excel functions to advanced formulas, data visualization, and dashboard creation, with a focus on hands-on practice. By providing templates and solution files, self-study is possible, and you can maximize work efficiency by mastering data analysis skills that can be immediately applied in real-world scenarios.

Revolutionize your stock investing with AI! In this course, you’ll build a fully functional AI automated trading system that targets the top 25 Nasdaq stocks. Gain experience and know-how through paper trading with the Korea Investment & Securities API, and take advantage of this special opportunity to conduct AI-powered automated stock trading with a real account. Even if you’re a complete beginner, there’s no problem. We’ll explain the basic concepts of stocks and why you should invest in U.S. stocks in an easy-to-understand way, and provide all source code along with remote support. From now on, investing is essential for the next 60 years of our lives. Build your own AI investment partner to accompany you!

🚀 Practical AI Anomaly Detection with Databricks! 💡 Stop using expensive and slow models! From large-scale data optimization to real-time deployment, complete an anomaly detection AI that can be used immediately in corporate practice.

Creating your own data analysis portfolio with Google Antigravity CLI harness engineering and vibe coding: data collection, analysis, reporting, dashboard creation, and task automation. Even if you are new to data collection, once you master the principles, prompt engineering, and how to use Antigravity, you can perform data collection, analysis, visualization, reporting, and dashboard creation just like a data analyst. From Python data collection to report writing learned with Antigravity IDE and CLI. Prompt writing and error resolution methods for foolproof data collection and analysis.

This course is for both beginners who have never been exposed to programming before and those who have some theoretical knowledge about Python. The main purpose of the course is to create interest and enjoyment in Python based on real projects. I hope that through this course, you will gain more ideas and have more fun with Python.

This is a basic Python Excel programming course learned through an xlsxwriter tutorial. You can analyze data and automate tasks using Python and Excel.

Course Title: Big Data Analysis Engineer Practical Exam Pass Codebook This is a practical, certification-preparation course for candidates preparing for the Big Data Analysis Engineer practical exam. To reduce the burden of having to study extensive theory and content that is not directly necessary for the exam, this course focuses only on the key concepts and code needed to solve actual exam problems. The course consists of 23 lessons and covers everything step by step, from Python basics to Task Type 1 (data processing), Task Type 2 (machine learning), and Task Type 3 (statistical analysis). Rather than simply presenting completed code, the course explains the role of each section of code and why it is written, enabling students to follow along directly and develop their problem-solving skills. Main Course Content - Essential Python and pandas fundamentals required for the exam - Task Type 1 data processing and practical problem solving - Task Type 2 data preprocessing, machine learning modeling, and submission of prediction results - Task Type 3 statistical tests and regression analysis - Practical problem solving and code review for each session - How to use the exam practice environment and final review immediately before the exam Learning Materials Provided - An e-book summarizing key concepts and solution code - Python practice files for hands-on learning - Problems and explanations for each session - Final review materials for before the exam The primary goal of this course is to help students avoid wasting time studying unnecessary material and enable them to write code independently and solve problems at the exam site. The course is structured so that even candidates with limited Python fundamentals can learn in order, allowing them to prepare for everything from learning essential code to solving practical problems in one course.

Let's cultivate professional expertise in programming, algorithms, and statistical analysis!

"From the basics to 7 domain projects, completing the A to Z of deep learning (41 lectures in total)." Go beyond simply calling models and learn how to build clean, efficient deep learning pipelines using PyTorch Lightning, the current industry trend. Grow into a confident AI engineer capable of handling any data by directly implementing 7 projects, ranging from stock price prediction to generative AI, medical imaging, and sound analysis.

We explain 32 data visualization techniques very quickly and easily from a non-expert's perspective!

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

We’ll guide non-majors and beginners to quickly earn the Big Data Analysis Engineer practical certification! Keep the theory light and the practical training solid. Even without complex background knowledge, you’ll focus on past exam questions and study only the key points that are sure to appear on the test.