인프런 - 라이프타임 커리어 플랫폼

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 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.

From basics to advanced data analysis! Based on what you've learned, analyze data yourself using data from Jeju Island!

We will teach you the key points of SQL data analysis methods in an easy-to-understand manner through analysis projects and theories.

The Scala course provides essential skills and knowledge for professionals working in the fields of big data and data science, offering an excellent opportunity to learn efficient data processing and analysis methods through Scala's powerful programming language capabilities. Scala is widely used in big data processing tools like Apache Spark, and through this course, participants will be able to acquire advanced skills required in the big data ecosystem, thereby enhancing their competitiveness. For developers and data scientists who want to build a successful career in a data-centric world, this course will be a crucial learning opportunity not to be missed.

How to Read the World with Data: Explore Your Own Data Science! Dive deep and practically into the tools and techniques that form the core of modern data science. In particular, you will learn how to analyze data and implement algorithms using Anaconda, Numpy, Pandas, and Scikit-learn, essential components of data science.

Do you want to create a tool that can help you with your real-world investments using Python? In this course, you will learn how to create an interactive web dashboard for financial data analysis for intermediate Python users and learn about pair trading strategies for in-depth quantitative work. We hope that you will be able to create your own useful analysis tools to help you analyze financial data more powerfully and make wise investment decisions.

Based on the key indicators defined based on data, learning through experiments and rapidly iterating to grow the service. This is a 101 lecture covering the basics of growth hacking.

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

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)

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

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

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!

This course will help you understand the analytical capabilities and technologies required for decision-making by managers in the digital transformation era and apply them in actual work.

There are frequently appearing problem patterns in the Big Data Analytics Engineer practical exam. And there are also a few lines of code that you must memorize before going to the test center. If passing the exam is your goal, be sure to take this course.

By skimming through the content of the Tableau certification, you can understand the overall functions of Tableau in a short period of time.

What is Kaggle? Every year, Kaggle conducts a survey of users around the world. When you want to learn data science, you may have many questions. Is it too late to start? Which language should I choose between Python and R? How much is the salary? Find out by reading the responses from users around the world! With just a few powerful features of Pandas, you can perform frequency analysis and data visualization.

A theory-practice QGIS course covering analysis basics, advanced functions, and visualization introduction for QGIS utilization. Start QGIS by taking this unprecedented all-in-one course!!

This is an introductory BigQuery (SQL) course for beginners. Recommended for those curious about data processing methods. We teach SQL from a data analysis and data engineering perspective rather than a backend development perspective.