[After Work Side Projects] Big Data Analytics Engineer Practical Exam (Task Types 1, 2, 3)

We invite you to the fascinating world of coding where you analyze and visualize financial statements using only prompts. Without a single line of code, you create and deploy a service in consultation with AI.

This is a Vibe Coding lecture that teaches how to create actual web services using public data with Google AI Studio (free), even without coding experience. We will create a complete service in Korean, utilizing the Public Facility Safety Information API, including search functionality, map integration, and AI analysis reports. You will learn the entire development process, from PRD planning to deployment, which can be immediately applied in real-world practice.

Have you ever regretted focusing only on notes instead of making eye contact, or failing to accurately record meeting details after an important client meeting? Don't worry anymore! In this lecture, we'll help you build your own CRM assistant using Vibe Coding to summarize key information with AI and neatly save meeting content. You'll learn how to drastically reduce cumbersome document creation time, maximize work productivity, and revolutionize customer management!

The only lecture that delves deeply into a specialized field! A subject-specific industry expert will show you the fastest way to pass the exam through a thorough analysis of the exam trends!

This introductory course, designed to be the easiest and most practical for Python beginners, covers intuitive dashboards using Streamlit, Google Cloud Platform for deployment, and more. Additionally, you can also prepare for the 빅데이터 분석기사 practical exam.

SQLD, where do I even start? A course for those who feel completely lost - you'll meet a transformed version of yourself after completing it. SQLD All-in-One Pass!

Not only will you master Python and GIS! A practical QGIS course, you'll even learn automation using both.

Learn a systematic approach to S&P 500 pair trading with Python. Lay the foundation for an investment strategy that excludes emotions through professional data analysis.

This is a basic Python course for starters who want to grow as data analysts. - Those who want to do data analysis but don't know Python - Those who want to learn machine learning with Python - Those preparing for the Big Data Analytics Engineer practical exam - Those who want to develop machine learning-based applications with Python

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

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 covers how to leverage AI in the food and pharmaceutical manufacturing industry to enhance productivity and quality. Through real-world case studies, you'll learn how AI is applied in key stages such as raw material management, production processes, quality inspection, and regulatory compliance, while understanding defect rate reduction and efficient operational strategies. Through this course, learners will master ▲characteristics of the food and pharmaceutical industry and the necessity of AI adoption ▲data-driven quality management ▲predictive maintenance and equipment optimization ▲regulatory compliance monitoring and risk management ▲analysis of domestic and international implementation cases, gaining strategies and insights that can be immediately applied in the field.

Revolutionize your stock investments with AI! In this lecture, you will directly build a real, working AI automated trading system targeting the top 25 NASDAQ stocks. We provide a special opportunity to gain experience and know-how through simulated investments with the 한국투자증권 API, and to perform AI stock automated trading with a real account. Even stock beginners will have no problem. We will easily explain basic stock concepts and why you should invest in US stocks, and provide all source code and remote support. From now on, investment is essential for the next 60 years of our lives. Create your own AI investment partner that will be with you!

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!

This course is for both beginners who have never been exposed to programming before and those who have some theoretical knowledge about Python. The main purpose of the course is to create interest and enjoyment in Python based on real projects. I hope that through this course, you will gain more ideas and have more fun with Python.

Time Series: Become a Data Analysis Expert! Let's be analysts strong in theory & practice!

Those who have learned the basics of Python will learn how to utilize Python after watching this lecture.

Building Your Own Data Analysis Portfolio with Free GEMINI CLI Vibe Coding Data Collection, Analysis, Reports, and Dashboard Creation for Work Automation Even if you're new to data collection, by learning the principles, prompt writing methods, and VSCode usage, you can perform data collection, analysis, visualization, reports, and dashboard creation like a data analyst. From Python Data Collection to Analysis Report Writing with GEMINI CLI and VSCode Prompt Writing and Error Resolution Methods for Foolproof Data Collection and Analysis

SQLD, where do I even start? This course is for those who feel completely lost - you'll meet a transformed version of yourself after completing it. SQLD All-in-One Pass!

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.

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 aims to acquire Python-based web crawling technology to collect data from web pages and improve data collection and analysis capabilities at work based on this.

🧩 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.