인프런 - 라이프타임 커리어 플랫폼

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.

Inflearn cumulative students over 30,000, Datarian's SQL basics course with rich online/offline teaching experience. Learn SQL basic theory and solve 10 SQL problems together.

There was a rumor that Ediya would open a store near Starbucks. How different would the locations of Ediya and Starbucks be? Will the real estate price fluctuation trend from 2013 to 2019 be reflected in the apartment sales price? What kind of parks are there in my neighborhood? How can I utilize the data in the public data portal? The goal is to become familiar with Python and various data analysis libraries by handling various types of data through public data.

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

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.

We conduct app log analysis using data from Google Analytics 4 and Firebase. It includes useful content for practical use, such as arrays, window functions, funnels, retention, Google Sheets, and more.

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.

We will cover Amplitude, currently used by leading companies in various industries for user behavior data analysis. This includes methods for creating a demo account, as well as generating and utilizing charts in Amplitude.

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.

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>.

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.

For those looking to learn Data Science, Big Data, and Crawling, (1) we will summarize the most essential Python core syntax in a short time, and (2) you will become familiar with Python and crawling through practical crawling programming.

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

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

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!

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

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.

By implementing various practical data analysis cases using SQL, you can simultaneously improve your data analysis and SQL utilization skills.

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 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.