[Big Data Learning Center] *Free* 67th & 68th Open House - Bootstrapping and Structural Equation Modeling | Hierarchical Linear Modeling

Learn about Text Analytics using KoreaPlus SPSS.

Learn Bayesian Inference, a core methodology of statistical reasoning, and the Hayashi Quantification Method specialized for categorical data analysis. Using SPSS Bayesian Statistics, you will practice various statistical analyses including binomial proportions, mean differences, correlation coefficients, regression models, and ANOVA, while mastering Quantification Methods 1–4 to derive internal insights from categorical variables.

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.

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.

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.

Learn about PSM 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.

Learn Bayesian Inference, a core methodology of statistical reasoning, and the Hayashi Quantification Method specialized for categorical data analysis. Using SPSS Bayesian Statistics, you will practice various statistical analyses including binomial proportions, mean differences, correlation coefficients, regression models, and ANOVA, while mastering Quantification Methods 1–4 to derive internal insights from categorical variables.

Data analysis is complete with this one tool. Get started with Orange AI data analysis without coding!

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

This course introduces students to the mathematical concepts and techniques essential in the financial industry, blending theoretical mathematics with practical applications. Learners will explore topics such as probability, statistics, and linear algebra, all within the context of finance.

This course introduces major approaches to symbol-based music theory and musicology. Learners will explore various topics, including music theory algorithms, music generation, and machine learning.

In this course, you will learn the mathematical principles and intuition of discrete stochastic processes and master how to create and analyze various models. You will explore discrete probability models that are useful in various fields such as engineering, physics, and biology.

This course covers key topics in discrete applied mathematics, focusing on counting, generating functions, probability, and more. Learners will engage with both mathematical concepts and effective communication through writing assignments.

Exam Prep supports your success. The lectures are based on the content included in the PDF materials and follow a flow of: Theoretical Explanation → Summary of Core Concepts → Solving Past Exam Questions. You can download the course materials from Challenge. The course is designed to allow learners to organize the information themselves and engage in repetitive learning.

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

This course covers the practical impact that quantitative methods have had on today's most important companies. Learners will study key quantitative methods that create a competitive advantage, such as data mining, dynamic optimization, and simulation.

This course explores the mathematical concepts and techniques essential for the financial industry, blending theoretical mathematics with practical applications. Learners will study topics such as linear algebra, probability, statistics, and stochastic processes, guided by both MIT mathematicians and industry professionals.

Feeling overwhelmed by where to start with complex data analysis? I will clearly guide you through how to utilize R and AI Tutors using the practical know-how I've built up.

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.

Understand the essence of data visualization as a balance between scientific accuracy and aesthetic expression, and systematically learn everything from color and symbology principles to graph design. By utilizing visual attributes based on cognitive science principles, you will complete your data communication skills to achieve reliability and persuasiveness in professional practice.

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

Through a systematic curriculum of 40 lectures, you will learn everything from the basic theories of AI to the applied AI technologies of each algorithm. Through mathematical intuition and R coding algorithm learning and practice, you will develop immediately applicable AI model development capabilities, covering everything from designing algorithm models for data analysis, machine learning, and their application tools to selecting and utilizing them.

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

['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.

This course builds on the foundations of programming in Python, emphasizing computational thinking and data science principles. Learners will explore how computation can effectively solve real-world problems, empowering them to write functional programs for various applications.