Data Literacy: Statistical Thinking and Data Analysis Fundamentals for the AI Era

This course is designed for those who are new to React, an essential skill in frontend development. We break down difficult concepts into easy-to-understand terms, allowing you to naturally master everything from the basic principles of React to practical application through hands-on learning. By addressing the common pain points where beginners often get stuck and providing project-based practice, we help you grow into a confident React developer.

This is a course on building your own stock and cryptocurrency automated trading programs using ChatGPT, even without prior coding knowledge. You will gain both AI development skills and financial investment knowledge through hands-on practice covering the entire process, from data collection to predictive model development, stock recommendation systems, and actual automated trading.

Learn how to reduce marketing risks through AI and efficiently execute the entire process, from branding and personalized marketing to content creation. From the basics of prompt engineering to practical application, we present essential AI utilization methods for marketing professionals.

You will systematically learn the principles and practical applications of the latest generative AI technologies, from ChatGPT to image and video generation AI and no-code tools. Through seven core topics, including prompt engineering, Copilot utilization, and AI extensions, you can maximize work efficiency and develop creative problem-solving skills.

We explain the operating principles of complex IT systems, such as servers, networks, clouds, and AI infrastructure, using simple everyday analogies. Even non-experts can systematically learn the core literacy needed to communicate smoothly with technical experts and make logical decisions in a digital environment.

We provide an easy understanding of the concepts and principles of generative AI from a humanities perspective, including literature, film, music, and history, while exploring the cultural impacts and life changes AI will bring. We offer practical insights and strategies on how individuals and companies should respond and prepare for the AI era.

Explore how LLM applications are shifting the development paradigm. Understand why LLMs are becoming a sustainable core technology through comparisons with existing technologies, and learn about the essential skills and mindset shifts required for developers. Gain insights from expert instructors to grasp the essence and future direction of LLM application development.

This is a special lecture that can lay the theoretical foundation to simultaneously prepare for the Microsoft AZ-900 certification, and as the latest content reflecting the scope of questions as of May 2025, by providing content related to core data concepts, Azure's relational data, Azure's non-relational data, and Azure's analytical workloads in a form combining theory and practice, it can be utilized as a meaningful educational opportunity to not only obtain the certification but also take the first step towards becoming a data professional.

[Problems This Course Solves] - The limitations of planners who have ideas but cannot build them directly - Inefficient decision-making based on imagination: "Please make it like this" - Bottlenecks caused by requesting and waiting for the development team for every data analysis or API verification - The reality of using AI tools but remaining at the level of simple ChatGPT Q&A [What You Will Learn] 1. Redesigning the Planning Process in the AI Era - Traditional: Planning → Design → Development (Sequential waiting) - Shift: Writing PRD → Generating Prototype → Decision-making based on the actual product → Delivery after confirmation - Moving from planning explained through documents to planning demonstrated through actual products 2. Designing AI Agents for PMs - Harness Engineering: Designing a working structure rather than just asking AI questions - CLAUDE.md, Rules, Hooks, MCP, Plugins — The roles and design principles of each layer - Agent Router: A structure where a single request is automatically assigned to the optimal agent - PGE Pipeline: Planner → Generator → Evaluator, quality control design for large-scale tasks 3. Building Your Own AI Team - Planning/Research, Product, Design, Development, Marketing — 20+ agents across 5 columns - Actual workflows where a solo planner manages a team of experts - Connecting external tools via MCP (Notion, Figma, Slack, data sources) - Automating repetitive tasks with one-command "Skills" 4. Practical Automation of Supplementary Tasks - GA4 + Clarity + GSC + BigQuery → Integrated dashboard + automatic weekly insight generation - Direct calling of in-house APIs → Scenario verification → Quality evaluation reports - BigQuery cohort analysis → Aha Moment hypothesis verification - A/B testing, Notion synchronization, UI/UX audit automation --- Who should take this course: - PM/PO/Service Planners tired of waiting times and review cycles in the planning process - Those who want to properly design AI as a professional work tool - Those who want to make data-driven decisions themselves but lack analysis team resources - Non-developers who want to move past their fear of the "terminal" [Prerequisites] - None. We guide you through everything from installation to setup. - No coding experience required. We focus on design and application from a PM's perspective. [Changes After Taking the Course] - Increase decision-making speed by creating working prototypes directly from a single PRD.md - Design and operate your own team of AI agents - Directly automate data analysis, API verification, and report generation

"Smart Factory—it sounds cool, but can't quite picture it in your head?" The news talks about the 4th Industrial Revolution and AI factories every day, but when you actually look for information, it's full of "alien languages" like PLC, MES, ERP, and Edge Computing. What non-majors need isn't a complex circuit diagram, but a 'map' that shows how the whole system works. This course strips away difficult engineering jargon and explains everything about smart factories through intuitive illustrations and analogies that anyone can understand.

Easy and powerful data analysis anytime, anywhere! Simple with Orange 3, even if you don't know how to code!

Customer analysis completed with Excel and Orange, from cross-selling to A/B testing and churn risk management all at once

The shipbuilding and maritime industry is a representative field of traditional heavy industry, but it is securing new competitiveness through the combination of digital transformation and artificial intelligence (AI) technology. This course is structured around specific application cases actually used in the field, such as AI-based shipyard production management, ship design automation, maritime logistics optimization, and predictive maintenance system construction. With a field-centered approach, you can understand not just simple theory but **'how AI actually works in shipyards'**. Additionally, it covers data infrastructure construction, model training, and constraints specific to the marine environment that must be considered during the AI technology implementation phase.

If you're wondering, "I did the user research... but is this result truly representative of all users?" then pay attention! We will show you exactly how to add persuasiveness to your designs based on data. ✅ We explain complex statistical terms in very simple language. ✅ We carefully introduce the 3 core hypothesis tests essential for UX practice, covering everything from principles to hands-on practice. ✅ We show you how to easily analyze user data using AI—without costs or hallucinations.

This course covers practical strategies for leveraging AI in the machinery and metal processing industry to enhance productivity and quality. It analyzes key process data from CNC, welding, cutting, and other operations, explaining how AI technology is applied to reduce defect rates, enable predictive maintenance, and manage worker safety through real-world case studies. Learners will understand AI-based processing optimization methods that can be immediately implemented in the field, from small and medium enterprises to large corporations, and gain insights to realize manufacturing innovation in global competition.

This is a Python basics 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

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.

Data Analysis that Determines Online MD Performance! Learn essential data analysis and strategy formulation techniques necessary for MDs to maximize sales and profit in the e-commerce market.

This is a course for PMs who want to utilize data. It covers the entire process of using data in a PM role, capturing the full workflow from your first day at a new job to the beginning and end of a project. It is an introductory data course designed to build your thinking power, covering everything from data-driven and logical thinking to metric definition, log design, experiment design, and building a data culture. Although it was created for PMs, the content is also highly beneficial for data analysts (in fact, many analysts have already taken this course). Additionally, many professionals in marketing, design, and business planning roles are also taking the course.

The electronics and semiconductor industry is a field where data-driven management and innovation are particularly crucial due to ultra-precision processes and complex supply chains. This course covers practical strategies that can be directly applied in electronics and semiconductor manufacturing, including defect detection, process optimization, predictive maintenance, and supply chain management using AI technology. Along with real-world cases from global companies, the course also presents low-cost, high-efficiency AI implementation methods that small and medium-sized enterprises can realistically utilize. Through this, students will be able to understand and apply AI-based manufacturing strategies that not only improve productivity and reduce costs, but also build future competitiveness.

This lecture teaches how to generate images using DALL-E3 (DALL-E). Through basic functions, a 3-step prompt strategy, and hands-on image generation practice, participants can become creative image creators.

This course is designed to teach the entire process of 'AI Training Data Construction,' which is essential for successfully executing AI modeling projects. Learners will not simply remain at the theoretical knowledge level, but will experience step-by-step the data construction processes required in actual practice. Specifically, the course covers the entire process from AI training data planning → acquisition → storage → preprocessing → labeling → pseudonymization and transformation through hands-on practice, structured to develop practical skills that can be immediately applied in the field. Additionally, learners will systematically study the most important data management capabilities in AI projects, such as data quality management and security issues, personal information de-identification processing, and dataset optimization. Through this, learners can grow from simple data collectors into data specialists who can proactively plan and execute AI projects. 👉 Through this course, learners will understand how to construct high-quality training data that determines AI model performance and acquire practical capabilities to effectively apply this in real work environments.

Gain insights from DataOven CEO Ha Yongho, a first-generation data analyst who has demonstrated numerous insights and successes. This is a meetup video for data professionals, held on July 18, 2024.

This is a practical introductory course designed to help general office workers utilize generative AI as a business partner rather than just a simple tool. The objective is to develop the ability to use AI safely while complying with security and copyright standards, based on an accurate understanding of its operating principles and limitations. From the core principles of prompt engineering to streamlining text-based tasks such as planning, reporting, emailing, and minutes-taking, as well as Excel data analysis and the use of images and PDF documents, students will learn step-by-step. This helps drastically reduce time spent on repetitive tasks and secures the practical capability to handle multiple roles. Beyond individual use, the course covers how to build team-level work templates and standards. It is an action-oriented curriculum designed to ensure that participants possess work automation assets that can be applied immediately to their jobs after completion, leading to productivity improvements across the entire organization.

Let's begin developing with Copilot (copilot). With AI, even elementary school students can follow along!

"Have you given up on data analysis after only studying the tools?" PMs at large corporations only need to look at the data already implemented by the analysis team. However, PMs at startups or side projects are different. They must build the 'foundation' themselves—from designing logs and labeling them to collecting the data. "Everyone says data is important, but what exactly should I implement and where in my service?" Stop getting lost in abstract concepts. Checking login history is the true beginning of Data-Driven Decision Making (DDDM).