[2027-Digital Competency Enhancement_PASS] Data Architecture Semi-Professional (DAsP)

This course is designed for those who wish to obtain the nationally certified Advanced Data Analytics Semi-Professional (ADSP) certification. As the need for practical data analysis experts in both public and private sectors grows, this course is structured to help participants objectively verify their data analysis skills—essential for data utilization across society—and successfully apply for and obtain the certification.

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.

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.

This course was created for those who are preparing for the Big Data Analyst practical exam. It will help you pass the exam by learning as concisely as possible, and it will also provide an opportunity for those who are considering their career path to lightly learn about coding and machine learning.

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.

[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 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.

This is a detailed and practical course for beginners to Google Analytics. Rather than covering isolated features, it guides you through scenario-based analyses so you can learn the practical process of conducting analysis with GA4. It is continuously updated to reflect the latest changes to GA4!

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.

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!

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

1. AI practice using an AutoML platform that performs data analysis and AI modeling without coding 2. Carrying out the entire process from data exploration, visualization, and preprocessing for data analysis to AI modeling 3. This course is optimized for passing AICE Basic, an AI certification organized by KT

Do you want to get a job as a data analyst? Based on my experience, let's take a look at the tasks for each data role in real companies.

: "From environment setup to 7 practical projects, a deep learning full-stack journey across data domains (35 lectures in total)" Go beyond simply following example code and learn how to cook various types of real-world data using deep learning. Starting from building CPU/GPU development environments, you will cover waste sorting classification (Image), stock price prediction (Time-series), ECG anomaly detection (AE), animation stylization (GAN), medical image segmentation (U-Net), and sound classification (Audio)! Systematically master core deep learning algorithms through hands-on projects.

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!

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

Data Visualization Storytelling That's Perfectly Achievable with Excel Alone

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.

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.

Have you ever clicked on an Etherscan link after seeing a post saying, "A whale has moved funds to an exchange"? You likely closed the window immediately after seeing a screen filled with nothing but incomprehensible strings of characters. This course covers how to read that screen. An instructor with 14 years of experience in the security field has packed 15 lessons on how to directly verify wallets, transactions, and tokens using only free tools, without a single line of code. Now, judge for yourself using original records instead of relying on what others say.