인프런 - 라이프타임 커리어 플랫폼

This is a basic course on Python Excel programming that you can learn with the xlsxwriter tutorial. You can analyze data and automate tasks using Python and Excel.

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.

This is a basic/introductory course on digital marketing. It covers how to design digital marketing based on data and various practical cases. It contains stories and content that I want to share from my 14 years of experience in various fields of marketing, from digital marketing agencies to in-house marketers at large corporations. It is recommended for those who want to understand digital marketing in general.

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 quickly learn the core concepts of Python and the Pandas library. Improve your programming skills by solving the practice problems at the end of each part!

Master the nationally certified data analysis associate specialist qualification exam in 2 weeks! Acquire the core theories and background knowledge of data, and learn advanced knowledge of data analysis using R, a statistical analysis tool.

The fact that you can analyze data more easily and effectively with Python Pandas instead of Excel! Learn the basic functions of the Pandas library and how to use it in practice through financial data analysis.

This is not an investment lecture. It covers data collection, analysis, and visualization through securities data. It covers various data formats and teaches various text preprocessing techniques. It covers visualization techniques for time series data and methods for expressing scale. It covers several techniques for interpreting stock price data. This lecture is a course that teaches you how to gain insights through data analysis. The content learned through stock price data is organized so that you can apply it to collecting, analyzing, and visualizing data such as demand, inventory, sales, and traffic volume, where time series is used.

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.

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)

Train SQL queries through 50 diverse problems and prepare for real-world analysis environments and coding tests. * If you are new to learning SQL, you will be able to follow along without difficulty by first taking SQL [개념반].

Learn Pandas' core features, comparing with SQL!

Master advanced SQL, solve 101 practical data analysis problems at a professional level together, and build a solid SQL data analysis skillset.

This course is designed for beginners and consists of easy explanations and various hands-on projects. It covers data collection using web crawling and LangChain tools, and summarization, extraction, sentiment analysis, and recommendation systems using LLM.

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.

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 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.

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.

Through this lecture, you can learn about understanding SQL and databases, SQL syntax, database design for practical application, database utilization through programming, and even data analysis application cases.

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.