No-Code AI Data Analysis Using Orange - Lv.0 Introduction to AI-Based Data Analysis

Apply the latest trends in generative AI directly to your work! The bible of prompt engineering lectures, containing only the essentials.

If you design the tests first, AI handles the implementation. A practical development approach that captures both speed and quality!

A complete roadmap for obtaining the Big Data Analysis Engineer certification! We provide a systematic learning process for passing the exam, from data analysis beginners to experts.

First steps with Claude AI: Doing it right from the start for immediate practical use! If you want to work smarter, start with Claude AI.

Have you ever thought, "I wish I had Iron Man's perfect assistant, Jarvis..."?! Complete everyone's Jarvis to help you handle your work wisely!

AI writes on Slack and Notion for me? Work smarter with MCP, which goes beyond the limits of existing AI!

Excel is enough, even if you don't know how to code! This course enables non-majors to perform data analysis using Excel, covering everything from statistics to machine learning and Power Query.

Criteria for organizing complex data at a glance: Cognitive-based practical visualization designed with Excel and AI.

Now is the time to answer based on data rather than experience! If you quantify reliability with statistical knowledge and present it as evidence, your persuasiveness will go up infinitely!

Sensational video content created with just a single line of text! A quick and easy introduction to SNS content creation starting with Kling AI

Delegate the pressure of overflowing financial data and reports to an AI agent. From corporate analysis to report automation, an overwhelming workflow for financial experts that changes the game of practical business!

This course helps you make the most rational business decisions through data, from data connection to visualization. Master Power BI data analysis and visualization to derive valuable insights!

AI agents, the latest trend in generative AI! How to stay ahead with Cursor, which handles all your development tasks, and MCP, which expands its capabilities!

The Gemini agent that works across Google Drive! Put the finishing touches on your Google AI workflow with overwhelming productivity.

$0 model fees, infinite visuals completed with NanoBanana All-in-one visual creating for diverse content designs

From understanding big data to actual analysis planning, start preparing for your certification right away with systematic training!

Understand machine learning concepts, AI learning principles, and mathematical principles for model implementation to fully utilize deep learning!

Notes keep piling up, but work only gets busier. Structure your notes with Obsidian and transform your workflow to be faster and easier.

This course teaches you how to derive business insights or share derived insights by creating visual reports in the form of dashboards that support automated user interaction based on Google Data Studio, a free service.

Put an end to daunting data analysis with ‘Exploratory Visualization’! Drastically increase the speed of practical analysis without difficult coding.

From building analysis models to application with Orange! Learn easily and quickly! You can master the core technologies of data science without coding!

Don't be afraid of coding or data analysis anymore! Develop your data analysis skills without coding using Orange.

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

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.

The chemical and fine chemical manufacturing industry is a sector that requires complex processes and high safety standards. This course covers strategies for leveraging AI to increase productivity, reduce costs, and enhance safety. You can systematically learn practical skills centered on AI application cases that can be immediately applied in actual field settings, including process data analysis, quality prediction, equipment predictive maintenance, and environmental regulation compliance.

A course to understand and practice machine learning for efficiently conducting complex analysis and predictions! Even machine learning, which may seem difficult and complex, can be easy and fun to master with Masocampus's <Python Machine Learning>.

Analyzing customers, markets, and revenue patterns—you no longer need to learn it yourself. Just ask AI, and expert-level analysis and strategies are completed in an instant!

An introductory AI lecture tailored for those new to artificial intelligence! It easily summarizes core concepts from data to generative AI. The goal is not to build AI, but to achieve a level of practical understanding where you can ask the right questions and explain the technology.

[Systematization of Engineering Intuition and Intelligent Problem-Solving Methodology Roadmap] 1. Introduction: Shift in the Problem-Solving Paradigm (The Birth of the Problem Architect) The complexity of modern industry has reached a level that exceeds the cognitive capacity of individual engineers, clearly exposing the limitations of "troubleshooting," which is a simple reactive repair approach. To derive meaningful solutions in manufacturing and R&D sites where tens of thousands of variables are intertwined, it is essential to evolve into a "Problem Architect" who goes beyond solving occurring problems to eliminating the problem-generating structure itself at the design stage. This masterclass combines 40 years of engineering intuition with cutting-edge artificial intelligence technology to present specific intelligent problem-solving techniques that transform uncertainty into engineering necessity. 2. Step-by-Step Intelligent Problem-Solving Techniques (The Methodology) ① Data-Driven Causal Identification Technique: DMAIC 4.0 The traditional Six Sigma methodology—Define, Measure, Analyze, Improve, and Control—is advanced by combining it with the computational power of modern AI. Selection of Key Process Input Variables (KPIV): AI algorithms are utilized to extract key factors with actual causal relationships, rather than mere correlations, from among thousands of variables. Securing Statistical Consistency: We establish an analytical process that proves capabilities with a Process Capability Index (Cpk) of 1.33 or higher through data, realizing "zero defects through statistical necessity" rather than "accidental good products." ② Virtual Verification and Lead Time Reduction Technique: Zero-Trial Strategy To minimize physical trial and error, we introduce verification techniques in a Digital Twin environment, which perfectly replicates real-world processes in a digital space. Cost-Free Limit Testing: Optimal process conditions are derived by performing tens of thousands of simulations in a virtual environment without interrupting actual production lines or destroying prototypes. Multi-Agent System-based RCA: We apply a "time leverage" technique that innovatively shortens the lead time required for Root Cause Analysis (RCA) by deploying multiple AI agents that simultaneously perform search, reasoning, and cross-verification. ③ Logical Thinking and Knowledge Assetization Technique: Pyramid Narrative and Knowledge Graphs This is a knowledge structuring technique that ensures field problem-solving experiences do not remain in individual memories but spread as the intelligence of the entire organization. Pyramid Principle and SCQA Framework: By combining Barbara Minto's Pyramid Principle with the Situation, Complication, Question, and Answer (SCQA) structure, complex technical issues are reconstructed into logical narratives that management can immediately accept. Building Knowledge Graphs: Fragmented technical reports and drawing data are converted into a graph structure capable of real-time reasoning. Through this, an organizational learning system is established where problem-solving cases from a specific point are immediately propagated (Yokoten) to production bases worldwide. ④ Transparency-Based Decision Support Technique: Explainable AI (XAI) This technique involves verifying AI outputs based on engineering logic rather than blindly accepting them. Chain of Thought Visualization: By transparently disclosing (Glass Box) the step-by-step logical development process the AI went through to reach its final conclusion, the basis for decision-making is secured. Human-in-the-Loop Governance: AI serves as a Co-pilot suggesting optimal alternatives, while the system is designed so that the final trigger is approved by human engineering insight and ethical judgment. 3. Human-Centered Cognitive Sovereignty Acquisition Techniques As the automation rate of systems increases, active response techniques are required to prevent the decline of the cognitive capabilities of the humans operating them. Sandwich Workflow: Instead of delegating the entire work process to AI, a structural work technique is applied where humans preemptively occupy context design (Top Bun) and final value judgment (Bottom Bun) to prevent cognitive paralysis. RQTDW Learning Protocol: By mandating five stages—Read, Question, Think (confronting contradictions), Discuss (virtual debate), and Write—into the practical process, we reject simple acceptance of information and actively internalize knowledge into the brain. Intentional Cognitive Friction: To maintain a critical distance from the overly smooth answers provided by AI, an adversarial verification process using a Critique Agent is conducted. 4. Practical Application: Super-Gap Leadership for "Designing Out" Problems The ultimate maturity of problem-solving lies not in solving problems well after they occur, but in "Designing Out" the system structure so that problems cannot occur. Data Infrastructure and Autonomous Operation Design: We block the possibility of human error by building an autonomous operation system that ranges from real-time data collection to feedback loop-based correction. Organizational Intelligence: By converting individual expert know-how into standardized algorithms and knowledge graphs, we secure an upwardly standardized problem-solving capability for the entire organization. 5. Conclusion: Future Competitiveness Completed by Engineering Rigor While intelligence is manifested through AI technology, the vessel that contains that intelligence and makes it operate according to its purpose is only a rigorous and sophisticated engineering problem-solving methodology. This masterclass will move away from technology adoption that relies on luck or probability and present a path to becoming a "True AI Architect" who perfectly commands systems with data and logic. We hope you establish your status as a super-intelligent helmsman who dominates technology and eliminates problems.

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.

You can automate repetitive and tedious tasks using GPTs. Even non-developers can automate more complex tasks with GPTs by linking various external automation services!

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.

AI Workflow Automation + Passive Income Generation in Just One Click!!! The ultimate tool for workflow automation, Make—were you hesitant to start because of its complex settings and features? Implement the automation of your dreams yourself with step-by-step lessons that even beginners can easily follow.

Repetitive tasks like organizing Excel sheets, sending emails, and categorizing receipts… Are you still doing them manually? Now, leave those tasks to n8n and AI, and focus on what really matters. This course is designed to help anyone implement workflow automation using n8n—a tool that requires no coding—and apply it directly to real-world tasks. In just one hour, you will implement automation from A to Z yourself. Instead of a long 10-hour lecture, we give you only the essential core! To save your time, we focus solely on practical, real-world workflows.

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.

Learn the problem definition and hypothesis formulation process, core competencies for data analysis!

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

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.

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

Learn various features of Looker Studio (formerly Google Data Studio) using GA4 demo data. This includes explanations and hands-on practice for Looker Studio's visualization elements. In particular, marketers or planners who are not familiar with SQL can learn easy ways to connect and use BigQuery data with Looker Studio. You can learn methods to create dashboards using Looker Studio.