[2026-D-PEC UP_PASS] Data Architecture Semi-Professional (DAsP)_New Renewal Completed

The AWS Certified AI Practitioner practice exam course is a core summary program designed to help you pass the certification. First, you will quickly review the basic concepts of AI/ML and Generative AI, and learn the features and use cases of key AWS AI services that frequently appear on the exam (e.g., SageMaker, Rekognition, Polly, etc.). Based on this, you will practice determining which AI solutions are suitable for various real-world scenarios, while also covering topics related to the responsible use of AI models and data security. Finally, you will learn problem-solving strategies for each type of exam question and prepare for the actual test through mock exams. This course allows you to prepare for the exam efficiently in a short period of time.

"Nationally Certified Industrial Security Manager" New Renewal Version Final Update Completed!! This course is designed to help you obtain the Nationally Certified Industrial Security Manager certification by providing a systematic understanding of industrial security from a management perspective. Rather than focusing on rote memorization, the content is structured so that even non-security majors can understand the concepts, structure, and flow of industrial security. It is designed not only for exam preparation but also for practical application and career expansion.

This course does not simply list concepts. 👉 It explains the entire flow of IT systems based on "how a service actually operates." User Request → Server Processing → Data Storage → Result Return Web/App → API → DB → Cloud 👉 Centered around this flow, it connects where, why, and how each technology is used.

This course explains IT terminology and system structures frequently used in the workplace in a way that even non-majors can understand at once. It is designed to help you understand the flow of corporate IT structures—from servers and databases to APIs, security, and the cloud—rather than just learning individual terms. We will help you build foundational IT skills that are immediately applicable to your work, so you will no longer be stuck due to technical jargon in IT meetings and tasks.

AI Tech DNA Series: Core Principles of Computer Basics from an AI Perspective is an introductory course designed to help you easily understand the fundamental computer concepts necessary for the AI era. By explaining basic principles such as CPU, memory, storage, networks, and data structures through real-life analogies and an AI-focused lens, it builds the solid foundational strength needed to advance into Python, AI, security, and the cloud.

[Reflecting the latest related regulations and guidelines presented by the Korea Financial Security Institute, Financial Supervisory Service, and Personal Information Protection Commission] In the rapidly changing digital financial environment, security has become not an option but a core survival strategy. This course covers the new directions of financial security changing with digital transformation, strategic response methods, and practical approaches to establishing a security culture within organizations. We will examine the policy directions of the Korea Financial Security Institute, the latest regulatory trends, and global security trends together, learning security innovation strategies centered on people and organizations beyond technology-focused approaches.

[Reflecting the latest related regulations and guidelines presented by the Korea Financial Security Institute, Financial Supervisory Service, and Personal Information Protection Commission] This is a course where you learn how an organization's security governance and internal control systems should operate through actual financial security incident cases. You will analyze the causes of incidents and response processes, and based on this, learn effective security policy establishment, risk management, and compliance response methods. The goal is to build security culture and control systems across the entire organization, going beyond technical responses.

In an era of explosive data growth and crucial real-time processing, edge computing has established itself as an essential technology, no longer an option. This course is designed to systematically teach everything, from the core concepts of edge computing to deployment strategies for its application in real business environments. Through this course, students will firmly solidify the fundamental principles of edge computing and acquire practical knowledge and know-how applicable to real-world tasks and projects. Take the most certain first step towards becoming an edge computing expert, a core technology in the future IT environment!

What do you learn in the 'Management Information Visualization Professional' certification course? This certification course focuses on developing the ability to analyze data effectively, visualize it for easy understanding, and communicate necessary information clearly and efficiently for important decision-making within companies or organizations. In simple terms, it is a process of learning the skills to not just look at data, but to uncover hidden meanings within it and express them through charts, tables, and graphs so they can be easily explained to others. Through this course, you will: Develop the power of strategic thinking through data, build practical skills that can be applied immediately to real-world tasks, and enhance your professional expertise and competitiveness. Through this lecture, cultivate the ability to connect management with data and take a step forward as an expert who understands and explains data.

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!

The Big Data Analysis Certification is a national certification exam held twice a year. For those preparing for the exam, I have provided detailed lectures covering everything from the basics to core concepts. The difficulty of the exam increases with each session. To address this, I have provided various essential contents starting from the fundamentals to help improve the passing rate.

At a time when cultivating data analysis experts is essential, this course is designed to help participants obtain the state-certified 'Advanced Data Analytics Semi-Professional' (ADsP) certification.

We guide non-majors and beginners to quickly obtain the Big Data Analysis Certification (Practical Exam)! Keep the theory light and the practice solid—focusing on core points that are guaranteed to appear on the exam through past questions, without the need for complex background knowledge.

We will help those preparing for the Big Data Analysis Engineer Practical Exam with R to prepare for the exam. We will select and inform you of only the essential parts based on the questions that were on the Big Data Analysis Engineer Practical Exam. If you complete the course, you will be one step closer to passing the Big Data Analysis Engineer Practical Exam. We have reviewed the recent exam questions and uploaded related scripts and data. Also check out the latest exam information.

Understand the core of big data business and provide insights on how to utilize it in marketing strategies in the field.

SQLD, where should I start? For those of you feeling completely overwhelmed, this is the lecture for you. You will meet a transformed version of yourself after completing the course. SQLD All-in-One Pass!

We explored the essential Python syntax necessary for big data analysis and, through various examples, presented methods such as big data file processing and visualization.

Finally, the Probability and Statistics course for everyone is here! Learn the principles of probability and statistics step by step through theory and coding practice. Reference book "Probability and Statistics for Everyone" https://wikidocs.net/book/18165

This course is a hands-on training program that leverages ChatGPT to analyze numerical and textual data and transform it into practical business reports, comprehensively covering everything from basic data structuring to pattern insight extraction, KPI analysis, automated customer feedback classification, and building automated templates for recurring reports, allowing you to develop both data interpretation and text conversion skills simultaneously. It maximizes learning effectiveness by providing data visualization hints and designing customized templates for teams and individuals in parallel.

Is it enough to just search for past exam questions and pass the Google Analytics Individual Qualification (GAIQ)? A certification holds little meaning if you obtain it without knowing how GA4 is actually used in practice or how to query and utilize data. This course focuses on practical application, allowing you to directly examine reports and GA demo accounts, and even set up a demo shopping mall yourself. You will build the skills necessary for the field while solving the latest 2026 GAIQ exam questions.

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 lecture is designed for job seekers preparing for Evaluation and Analysis roles in Samsung Electronics' DS Division (Memory, S.LSI, Foundry, TSP, AVP) and PE, AE, and Solution PE roles at SK hynix, who are struggling with the differences between business units and job functions. You will learn how to categorize job titles by company based on the actual workflow of Product Evaluation roles and how to translate your major-related experiences into professional terminology suitable for resumes and interviews. We will help you establish an application strategy by connecting experiences in Analog circuit design, Verilog/FPGA, process experiments, and data analysis with key industry terms such as Yield, Wafer Map, Shmoo Plot, and Failure Analysis.

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.

[Process Optimization Roadmap: Eliminating Unnecessary Capital Investment and Converting 'Hidden Factory' Opportunity Costs into Tangible Profits] 1. Introduction: Transition to Intelligent Processes and the Prerequisite of Standard Operating Procedures For modern enterprises to maximize business value by adopting Artificial Intelligence (AI) technology, the rigorous standardization of physical processes and work procedures on the shop floor must come first. An intelligent system built upon an unstructured data environment lacking standardization will struggle to achieve optimal performance; instead, it is highly likely to amplify process uncertainty and act as a risk to the entire system. This training course aims to present a strategic methodology for discovering latent profit sources within the so-called 'Hidden Factory' and converting them into an engineering foundation that AI can autonomously control and optimize. 2. [Analysis] Quantitative Data-Based Field Diagnosis and Analysis of 'Baseline Losses in Critical Indicators' Many manufacturing sites tend to overlook potential operational losses by settling for superficial performance indicators. When Overall Equipment Effectiveness (OEE) is precisely analyzed through engineering decomposition techniques, the following structural losses are identified: Availability 90%: While external uptime is secured, power loss due to minor stoppages occurs continuously. Performance 90%: A slowdown in actual speed compared to theoretical cycle time has become chronic, yet there is no reference point to recognize and improve this. Quality 90%: A defect rate reaching 10% is evaluated as a critical figure that undermines the reliability of the entire process. The actual OEE, calculated by the multiplier effect of these three indicators, is only 72.9%, meaning the remaining 27.1% of opportunity cost is buried within the 'Hidden Factory'—unidentified by data. Therefore, the primary task of AI adoption is to quantify the source of this waste and ensure process transparency. 3. [Critique] Redefining Strategic Priorities: The Conflict Between Operational Excellence (OPEX) and Capital Expenditure (CAPEX) Prioritizing the expansion of new facilities (CAPEX) as a solution to declining productivity can be a strategically dangerous decision. Adding lines to a low-efficiency structure where OEE remains at the 60–70% level results in replicating fundamental inefficiencies, leading to an exponential increase in management costs. Before large-scale capital injection, maximizing Operational Excellence (OPEX) to break through the physical limits of existing assets must take precedence. Only when standard operating procedures capable of achieving the world-class standard of 85% OEE are established can a virtuous cycle be built—one that dramatically improves production capacity through integration with AI without additional investment. 4. [Evaluation] Integration of Data Architecture: Establishing an ISA-95 Based Decision-Making System The disconnection between the financial indicators of Enterprise Resource Planning (ERP) and the operational data of the Manufacturing Execution System (MES) is a core cause of information distortion and delayed decision-making. To resolve this, the international standard ISA-95 architecture is applied to organically integrate management strategy with the physical production site. Establishment of a Single Source of Truth: Complete a data pipeline where all dynamic field data is synchronized in real-time with the enterprise management system. Conversion of Indicators into Financial Value: By intuitively linking the impact of minor field stoppages to actual operating profit and cash flow on financial statements, an objective, data-driven performance evaluation and decision-making system is established. 5. [Execution] 4-Step Strategic Roadmap for Intelligent Process Management Implement an advanced strategy that fuses AI technology with human engineering insight based on strictly standardized procedures. Predictive Maintenance and Utilization of P-F Intervals: Build a predictive maintenance system by monitoring the P-F Interval—the period from a potential failure (a precursor to equipment breakdown) to a functional failure—in real-time. Digital Twin-Based Simulation and Minimization of Trial and Error: Practice a 'Zero-Trial' strategy that prevents physical losses by verifying various scenarios within a virtual environment before implementing process changes. Quantitative Control of the 6 Big Losses: Continuously track and manage the six core waste factors that hinder profitability—equipment failure, setup/adjustments, idling/minor stops, reduced speed, process defects, and reduced yield/rework—through AI. Digital Advancement of Total Productive Maintenance (TPM): Enhance the reliability of Human-in-the-Loop systems by establishing a proactive culture where field operators voluntarily detect and respond to signs of equipment abnormality. 6. Conclusion: Securing Technological Leadership Through Standardized System Architecture Technical knowledge can be transferred through education, but the insight to dominate process flow only manifests upon a sophisticatedly designed standard system. This masterclass aims to provide a precise engineering blueprint for transforming your production site into an intelligent asset. Establishing standardized work procedures and ensuring data integrity are essential prerequisites for the AI era. Only on a firm systemic foundation will AI function as a true intelligent production site that drives corporate growth.

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.

It covers basic data concepts, useful R functions and packages, and data analysis exercises, to allow anyone to perform big data analysis using R programming.

Revolutionize your stock investing with AI! In this course, you will directly build a functional AI automated trading system targeting the top 25 Nasdaq stocks. We provide a special opportunity to gain experience and know-how through mock trading using the Korea Investment & Securities API, and then transition to AI automated trading with a real account. Even stock market beginners will have no problem. We explain basic stock concepts and the reasons why you should invest in US stocks in an easy-to-understand way, while providing all source codes and even remote support. From now on, investing is essential for the next 60 years of our lives. Create your own AI investment partner to join you on this journey!

Students who want to learn Hadoop and Big Data will celebrate the amazing advancements in experiencing the world of Big Data through this course!