データ分析のためのパンダス：基礎からデータ分析まで

Datalian's advanced SQL problem-solving lecture with over 10,000 cumulative students and rich online/offline lecture experience. We will solve several problems together to practice advanced SQL content.

Datalian's SQL intermediate problem-solving lecture with over 10,000 cumulative students and rich online/offline lecture experience. We will solve several problems together to practice the SQL intermediate theory.

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

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.

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

Datarian's Python basics course with over 10,000 cumulative students and rich online/offline lecture experience. It boldly omits unnecessary details such as program installation so that even non-majors can learn easily, and only teaches the core of Python. This is the best course for those who are learning Python for the purpose of data analysis.

Datalian's SQL practical lecture with over 10,000 cumulative students and rich online/offline lecture experience. How is SQL used in practice? Let's become a data analyst who solves problems using real data from Microsoft's Yammer service.

Is there any job where the work seen from the outside differs as much from the actual work as it does for a data analyst? We have prepared a March seminar for those who are curious about what data analysts do and what tasks they perform.

We will reveal the actual data analysis portfolio of successful job seekers and the portfolio-related questions they received during interviews!

No need for developers! It doesn't matter if you don't know development. I'll show you how anyone can easily install GA4 with just a few clicks.

Solve 25 SQL problems of various difficulty levels. This is the Advent of SQL 2024 explanation lecture. Upon finishing the lecture, you can complete the Advent calendar and obtain a badge.

If you are thinking about using data in your work, get some great tips from this seminar.

Did you know that there are different types of data analysts? If you want to know everything about in-house data analysts and consulting data analysts, come to this seminar!

If you are curious about the various possibilities and future prospects of generative AI, including ChatGPT and Clova X, that can be used for data analysis, come to our seminar this May!

Practitioners who are curious about how other companies conduct funnel analysis, gather here! Meet the case of the global No. 1 alarm app, ‘Alarmy’, with 75 million cumulative downloads.

Are you worried about whether you can become a data analyst as a non-major? Hear from a non-major senior analyst!

If you are worried about where to start and where to start studying because you need to take a SQL coding test to become a data analyst, then come to this seminar!

In the cookie-less era, how will marketing change? If you don't know how to start marketing performance analysis, attend the November seminar.

How can performance be measured in offline advertising? From how to measure the performance of elevator TV ads, to the commonalities and differences in data observed in online/offline marketing! Hear about it at this October seminar!

Data analyst job prep: Lost on where to begin? Get hints at this September seminar!

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.

You can call, analyze, and visualize data from public data. You can learn how to collect transaction information on all green onions traded in the wholesale market from public data and perform data preprocessing, data analysis, and data visualization.

Introductory lecture on recommendation systems by Datalian, who has over 10,000 cumulative students and rich online/offline lecture experience. Learn the basic theory of recommendation systems and practice movie data analysis together.

A course to understand and practice machine learning that efficiently performs complex analysis and prediction! Machine learning, which seemed difficult and complicated, can be easily and enjoyably learned in its core with Maso Campus' <Python Machine Learning>.

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.

With Python Quant Investment, you can create investment strategies based on data and invest according to the strategy. You can implement various asset allocation strategies and ultimately create your own investment strategy.

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.

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 how to collect data from the web and create a supervised learning model for machine learning!

I will guide non-majors and beginners to quickly pass the Big Data Analysis Engineer practical exam! Theory lightly, practice thoroughly. Even without complex background knowledge, focusing on past questions, we intensively study only the points sure to appear on the exam.