[Big Data Learning Center] *Free* 69th & 70th Open House - Bayesian Statistics | Quantification of Categories

Learn multi-level data analysis using Structural Equation Modeling (SEM) bootstrapping techniques and Hierarchical Linear Modeling (HLM). This is an advanced course designed to help you grow into a statistical expert capable of analyzing complex research data, covering everything from advanced AMOS features to the practical application of HLM. You will master both theory and practice through real-world research cases in the fields of education, psychology, sociology, and health.

The 65th Open House is about showing how data analysis is enriched by changing the type of variables. It mainly covers scoring, binning, and optimal scaling. The 66th Open House is about learning in detail about the concept and understanding of marginal effects and subgroup analysis.

You will learn through hands-on practice everything from the basic usage of KNIME to advanced data analysis techniques, and acquire analysis skills that can be applied to actual data.

Learn about PSM using KoreaPlus SPSS.

Learn about Text Analytics using KoreaPlus SPSS.

The 63rd Open House explains the concept of effect size and SPSS's effect size output. It also covers the concept of sample size and power and SPSS's power analysis method. The 64th Open House learns in detail about the extension and evolution of linear regression.

ADsP Certification All-In-One Course! Ultra-Short Completion! Reference Book: "Everyone's ADsP Cheat Sheet" https://wikidocs.net/book/18829

This course is for those who want to study machine learning but lack statistical knowledge.

The 63rd Open House explains the concept of effect size and SPSS's effect size output. It also covers the concept of sample size and power and SPSS's power analysis method. The 64th Open House learns in detail about the extension and evolution of linear regression.

How should we integrate the results of different diagnostic methods when they are different? Are there any diagnostic methods that are unnecessary among the various diagnostic methods? We will provide answers to these questions.

This course is a hands-on program for practicing the fundamentals of machine learning mathematics and statistics using Python. While machine learning and deep learning require programming skills, a mathematical background is necessary to understand the underlying principles. Through this course, you can step-by-step master the foundational mathematics required for artificial intelligence.

Learn multi-level data analysis using Structural Equation Modeling (SEM) bootstrapping techniques and Hierarchical Linear Modeling (HLM). This is an advanced course designed to help you grow into a statistical expert capable of analyzing complex research data, covering everything from advanced AMOS features to the practical application of HLM. You will master both theory and practice through real-world research cases in the fields of education, psychology, sociology, and health.

This course is ideal for those who want to focus on theory and context rather than practice, aiming to organize their knowledge of deep learning in depth and grasp the big picture. It makes the mathematical and statistical foundations of deep learning easy to understand and provides intuitive interpretations of key modern models—such as AutoEncoder, GAN, Transformer, and AlphaGo—from a developer's perspective.

The 65th Open House is about showing how data analysis is enriched by changing the type of variables. It mainly covers scoring, binning, and optimal scaling. The 66th Open House is about learning in detail about the concept and understanding of marginal effects and subgroup analysis.

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.

ADsP Summary + Past Exam Solutions. Reflects August 2025 46th Exam! (Continuously Updated!)

In the era of AI and Big Data, are you utilizing your organization's data effectively? This course builds essential capabilities for reading and interpreting data, covering everything from the foundations of statistical thinking to the core principles of machine learning and deep learning. Grow into a practical leader capable of discovering patterns amidst volatility and making data-driven decisions.

Let's cultivate professional expertise in programming, algorithms, and statistical analysis!

['Thought Architect' Roadmap for Translating Technical Truth into Business Value] 1. Introduction: Analysis of Constraints in Transferring Technical Truth to Business Value A significant bottleneck frequently observed across corporate sectors and engineering sites (R&D) is the phenomenon where innovative achievements, backed by massive resources and research capabilities, are discarded because they fail to overcome the 'communication threshold.' This is judged to stem from information asymmetry: a situation where management does not sufficiently perceive the depth of deep-tech, and technical personnel fail to appropriately utilize business language. This masterclass redefines the 'text-based communication competency' of engineers and scientists not as a mere administrative act of recording, but as part of a precise 'Engineering Design' process. Through this, we aim to present a mechanism by which an engineer's technical insights are transformed beyond simple records into core assets that drive the organization's strategic decision-making. 2. [Diagnosis] Analysis of Three Major Cognitive Defects Hindering Technical Communication ① Disconnection of Information Transfer due to the Curse of Knowledge This phenomenon occurs when experts become immersed in their own advanced level of knowledge and overlook the cognitive background of the information recipient. Consequently, reports centered on obscure formulas and technical acronyms are repeated, which is highly likely to result in structural losses such as loss of contact with decision-makers, delays in project approval, and failure in resource allocation. ② Value Loss due to Lack of Logical Architecture and Data Fragmentation By regarding document preparation as a simple post-hoc administrative procedure, errors of listing information without systematic design are frequently observed. These structural defects significantly damage the value of R&D achievements during the delivery process, tending to lead not only to the undervaluation of an individual engineer's capabilities but also to the loss of tangible and intangible assets for the entire organization. ③ Technical Reliability Crisis due to Deepening AI Dependency If generative AI, introduced to improve productivity, causes hallucinations without understanding the technical context, 'technical reliability'—the core value of engineering—can be seriously undermined. Utilizing AI without an accompanying logical verification system is highly likely to act as a potential system risk rather than a work efficiency tool. 3. [Prescription] Strategic Communication System for the Thought Architect ① Engineering Design of Thought for Unstructured Information A process of structuring abstract experimental data and hypotheses based on engineering system design principles is required. Bottom Line Up Front (BLUF): By placing core values and recommendations at the forefront of the document, we aim for an information structure optimized so that management can perform rapid and accurate decision-making (capital investment, mass production approval, etc.). Mutually Exclusive, Collectively Exhaustive (MECE): By adhering to principles that prevent logical overlap and omission, we ensure the logical completeness of the report by designing a seamless flow of thought. ② Global Standard Compliance and Quality Assurance Objectivity and integrity of data must be secured by internalizing standard protocols recognized by the global scientific community and global corporations. Standard Technical Reporting Structure (IMRaD): Maintain the consistency and reliability of the engineering narrative through a rigorous formal structure leading from Introduction, Methods, Results, to Discussion. 5 Principles of Sentence Quality (5C): By applying Correctness, Clarity, Conciseness, Consistency, and Completeness as quality control filters, we establish a Plain Language system that even non-experts can understand immediately. ③ S.E.E.D Prompt Architecture for Securing AI Command To operate AI not as a simple ghostwriting tool but as an assistant performing logical operations, a dedicated interface design must be involved. S.E.E.D Framework: Precisely control the quality of AI outputs by systematically structuring and inputting Situation, Expectation, Engineering Structure, and Data. Human-in-the-Loop: Establish a protocol where humans perform verification as the final approver to block the possibility of AI errors, thereby firmly maintaining technical authority. 4. [Execution] Strategies for Securing Cognitive Sovereignty and Workflow Optimization Communication competency acts as a core mechanism for preserving an engineer's cognitive functions beyond mere technical means. Responsibility-based Sandwich Workflow: Humans must perform context design (Top Bun) and final verification (Bottom Bun), delegating only the intermediate data processing (Meat) stage to AI to promote the activation of the human-unique Executive Control Network (ECN). RQTDW Learning Protocol for Knowledge Internalization: Apply the five stages of Read, Question, Think (facing contradictions), Discuss (virtual discussion), and Write to practical work to avoid simple acceptance of information and actively internalize knowledge. SIFT Model-based Precision Fact-Checking: Filter AI hallucinations and maintain technical precision through the process of Source (checking the origin), Investigate (understanding context), Find (cross-checking), and Trace. 5. Conclusion: Transition from Technical Expert to Engineering Leader The era of practitioners who simply perform given technical tasks is coming to an end. The ability to translate and communicate complex and difficult technical truths into a 'language of value' acceptable to organizations and society can be defined as a core survival strategy and a noble calling for modern engineers. Text communication is the Final Interface that projects an engineer's intellectual capacity to the outside world, beyond a simple act of recording. Through this masterclass, we expect you to prove your technical value and emerge as a powerful 'Engineering Leader' who drives organizational change. This system, which integrates 40 years of engineering insight, will become a strategic asset that completes your status as a professional.

As the popularity and interest in AB Testing explodes, we will introduce Korea's first AB Testing lecture right now!