Practical Data Science Part 2. Data Preprocessing

The digital transformation (DT) and introduction of artificial intelligence (AI) in companies begin with the construction of machine learning models. However, the scope of machine learning technology is very broad, and in order to select the optimal method, it is necessary to clearly understand the basic concepts. In this lecture, we will introduce the core contents necessary to clearly understand the basic concepts of machine learning, focusing on five examples.

This course is for those who need to introduce data analysis, machine learning, AI, etc. to their work but are not familiar with Python programming. You will systematically learn the core functions of Python required to become a data scientist in a short period of time.

With the advent of the AI era, building data pipelines has become a core competency that determines a company's competitiveness. Learn how to build efficient data pipelines using Airflow, the most widely used tool, directly from a Silicon Valley expert (formerly Head of the Data Team at Udemy, currently a professor in the Data Masters program at San Jose State University) with practical experience and extensive lecturing experience.

This course covers everything from core recommendation system algorithms to practical implementation. - Content-based filtering - Collaborative filtering and deep learning-based recommendation model implementation - Two-step recommender systems implementation - Hands-on practice using PyTorch/RecBole - Industry know-how and recommendation result visualization

This is a course to learn about Airflow, an Orchestration tool for efficiently building and managing data pipelines. Welcome to the Airflow Master Class, where even beginners can learn step-by-step!

Do you have data but feel lost on how to read and process it in Python? Don't worry. Pandas can handle it with its magic. Pandas is the most powerful, efficient, and useful data processing library. Skill-UP your data preprocessing with Pandas! Insights galore!

Learn NoSQL technology for handling big data, one of the fundamental skills in full-stack and data science technologies used by modern startups. MongoDB is the easiest and fastest NoSQL technology to utilize. In this course, you will learn the basics of MongoDB in a short time and master the skills to handle and utilize MongoDB with Python.

You will develop the ability to define problems based on data and clearly explain the rationale and decision-making process behind them. Additionally, rather than focusing solely on the performance of a single model, you will acquire a pipeline-oriented mindset that evaluates the completeness and reliability of the entire machine learning workflow. Furthermore, you will strengthen your problem-solving skills by tracing back the causes of errors when they occur and deriving improvement directions, and through end-to-end project experience, you will acquire practical ML pipeline capabilities that can be immediately applied in real-world settings.

Apache Superset is an open-source BI tool that lets you quickly create charts and dashboards by connecting multiple databases. You can visualize data without knowing code, and SQL Lab allows you to run queries directly, making it useful for everyone from beginners to professionals. The dashboards you create can be shared immediately with permission settings, making it perfect for collaboration. Learning Superset gives you the ability to make data-driven decisions quickly and build practical dashboards you can use right away at work. In short, it's perfect for anyone who wants to make data "speak." 🚀

Analyze over 40 types of economic indicators and stock data with AI to create your own powerful stock price analysis model that predicts not only S&P 500 and QQQ ETF, but also individual stocks!