[From Concept to Practice] Introduction to Recommendation Systems

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 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 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.

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.

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

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

How can data be leveraged in side projects? Hear the stories of 'Geumim' and 'Skull' at this seminar!

How can data be used in content marketing? At this May seminar, you can hear case studies from data analysts on using data from company blogs and newsletters!

How do IT startups make data-driven decisions? At the April seminar, you can hear examples from DataLean and Postype!

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.

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.

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

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!

🧩 Rather than focusing on complex formulas or theory-centric explanations, the goal is to learn the core concepts of recommendation systems by directly implementing programs. 🛠️ Through a total of 12 diverse and practical examples, we have progressively designed recommendation systems that can be utilized in real-world environments, including content-based recommendation, collaborative filtering, and deep learning recommendation.

Pandas is one of the most widely used libraries in Python. Pandas, which has become an essential element for data analysis, you can learn everything about Pandas, the essential library for Python data analysis, through the <Pandas in One Shot> lecture.

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.

I want to create an algorithm trading bot that makes unlimited money and run it automatically. Where should I start?

Learn how to understand advanced machine learning models without math and implement them easily in Python.

Learn Pandas' core features, comparing with SQL!

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.

This course is a course that practices the basics of machine learning mathematics and statistics using Python. Machine learning and deep learning require programming skills, but they also require a mathematical background to understand the principles. Through this course, you can gradually learn the basic mathematics required for artificial intelligence.

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.

Analyze in an environment like Excel and automatically create Python code. This lecture will help you understand data analysis by comparing it to Pandas and Seaborn.

In the [Learn Machine Learning with AutoKeras] lecture, you can learn about AutoKeras, which was developed from the Keras library previously used in machine learning, and use it directly through examples.

Make AI data analysis and modeling results into a Web Program.

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.

ADsP Certification All-In-One Course! Ultra-Short Term Completion! Reflecting 2025 August 46th Exam! (Continuous Updates!)

Learn how to process big data from a Silicon Valley software engineer & how to develop big data code with Apache Spark using Python. I am a 14-year software developer who handles everything from web applications to big data and SRE & DevOps with Python. Don't miss this opportunity to learn about Apache Spark, which is essential for big data professionals, in an easy and in-depth way using Python!

Master the Big Data Analysis Engineer certification, an essential but never easy-to-self-study credential, in a short period by utilizing R, an easy-to-learn data analysis tool!!

🚀 Practical AI Anomaly Detection with Databricks! 💡 Stop using expensive and slow models! From large-scale data optimization to real-time deployment, complete an anomaly detection AI that can be used immediately in corporate practice.

The automotive and parts manufacturing industry is a field where innovation is essential amid fierce global competition and complex supply chains. This course focuses on how AI technology can be applied in the field, explaining actual cases including production efficiency improvement, quality inspection automation, equipment predictive maintenance, and supply chain optimization. From the foundational understanding needed for AI implementation to strategies and risk management considerations for field application, this course covers topics step by step, providing knowledge and insights that practitioners and managers can immediately utilize.

Through this course, students will learn various reinforcement learning theories and implement them themselves, as well as create a reinforcement learning environment to test the reinforcement learning algorithm implemented using Unity Machine Learning Agents.

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.