인프런 - 라이프타임 커리어 플랫폼

From the core of Kafka to in-depth content on internal mechanisms, the course is structured so that even beginners can quickly reach the expert level through detailed theoretical explanations, hands-on practice, and practical Kafka application development practice.

Datalian's SQL basics lecture with over 10,000 cumulative students and rich online/offline lecture experience. Learn SQL basics theory and solve 10 HackerRank problems together.

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!

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.

Through in-depth theoretical explanations of Kafka Connect and detailed practical training that can be used immediately in the field, we will help you grow into an expert in building data linkage and data pipelines based on Kafka Connect that are needed in the field.

You will be able to extract the data you want directly without the help of a developer. We will provide everything you need for analysis, from simple queries to joins. Start learning SQL with a direct lecture from the author of the book <Planner's SQL>.

Unlike existing lectures, this course has been newly designed based on the instructor's initial failures in learning machine learning, enabling easy understanding and application to real-world problems.

Learn NoSQL technology, which can handle Big Data, one of the fundamental technologies in full-stack and data science utilized by modern startups. MongoDB is the easiest and quickest technology to utilize among NoSQL databases. In this lecture, you will learn the basics of MongoDB in a short time, and also acquire the skills to manipulate and utilize MongoDB with Python.

This is a lecture on data visualization using the Matplotlib library.

Leverage Python to create your own indicators and a convenient tool for securities analysis!

If you want to be recognized as a machine learning expert based on large-scale data, from understanding the core framework of Spark machine learning, to SQL-based data processing through difficult practical problems, to data analysis through business domain analysis, and to the ability to implement optimized machine learning models, please join this course.

Data Science(DS) & Artificial Intelligence(AI) job role analysis, plus a learning & employment roadmap for building a career in these roles.

Learn about the basics of Python required for data analysis, the data analysis framework (pandas), and the data analysis process.

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)

Not only will you master Python and GIS! A practical QGIS course, you'll even learn automation using both.

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.

This course is designed for beginners, students, and those who want to use Polars, aiming to acquire the necessary skills for data analysis, from basic concepts of the Python-based data analysis library Polars to simple data processing and exploratory data analysis (EDA).

Anyone can learn data analysis with Chat GPT. You can learn how to apply it in practice through various analysis exercises. (Absolutely!! ChatGPT paid version is required!)

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