[NetMiner 5] Social Network Analysis

"A first step for beginners using NetMiner 5—from basic usage to analyzing research data, all in one!&quot

"How to Quickly Identify Social Opinion Trends! Training in News and Community Data Analysis" • News data contains important information such as the latest events, economic changes, political trends, and natural disasters, enabling companies or individuals to quickly identify and understand these changes. Online community data consists of text data containing users’ opinions and information on various topics, providing social and cultural insights. • News and community data, along with text analysis and text network analysis methods, can be used for various purposes, including market analysis, policy and decision-making, public response analysis, trend analysis, education and research, crisis response, and disaster management.

This course teaches how to collect, model, and analyze social media data using NetMiner. It covers how to collect and organize YouTube data using NetMiner’s SNS Data Collector, understand and model the types and structures of social media data, and analyze social media influence, topics, and trends through theory, case studies, and hands-on practice.

This course teaches how to identify research trends through social network analysis by directly collecting and preprocessing paper data. Through hands-on practice, you will learn how to collect data using the Bibliographic Information Collector (Biblio Data Collector), transform a two-mode relationship connecting “papers” and “authors” into a one-mode network connecting “authors” and “authors,” and extract words from unstructured text data such as abstracts, followed by keyword network analysis and visualization.

This course uses Python, a programming language well suited for big data analysis, to directly collect, process, and analyze social big data from the web, social media, and other sources. Python programming exercises are conducted in a step-by-step, follow-along format, making it easy for humanities and social science researchers with no programming experience to learn. By completing this course, you will be able to master Python, collect data, and analyze big data.

This course covers methods for analyzing unstructured text data, such as news articles, tweets, and speeches, using NetMiner’s text network analysis features. You will build a theoretical foundation in the entire text network analysis process, from extracting words from unstructured text data and discovering topics to constructing and visualizing word networks and selecting key keywords. You will also practice applying these analytical methods to real-world news article and Twitter data using NetMiner.

Although the majority of real-world data consists of '2-mode' relationships—such as buyer–product, user–content, and word–document relationships—have you encountered distorted metrics or limitations in data transformation from blindly applying conventional 1-mode analysis methods? We present clear solutions ranging from the core principles of 2-mode networks to data conversion and direct analysis (Direct/Bipartite) approaches, as well as hands-on practice in user profiling and association analysis using NetMiner. Spend less time struggling with data conversion and metric calculation, and take your research and professional insights to the next level with unparalleled 2-mode network analysis capabilities spanning text, bibliographic, and SNS data.

• This course introduces methodologies for analyzing roles and positions within networks—topics that have been extensively studied in SNA—and helps participants understand the underlying principles through hands-on exercises using NetMiner, while also exploring practical applications. • Through this course, participants can measure brokerage roles from various perspectives, determine actors’ roles and positions within a network based on relationship patterns, and summarize the entire network through the relationships among role groups, thereby uncovering the key structures that comprise complex networks.

This course covers how to statistically interpret the results of network data analysis and how to test the significance of statistical measures. When applying traditional statistical methods such as cross-tabulation, analysis of variance, correlation analysis, and regression analysis, researchers need to understand how the characteristics of network data and network indices affect significance testing and use appropriate methods. The course consists of examples covering analytical topics frequently encountered by researchers—such as analyzing associations between variables, testing the significance of network indices, and analyzing associations among multiple networks—along with hands-on exercises using NetMiner.

A group in which entities are densely connected within a network is called a cohesive group. This course explores in detail the frequently used analysis methods for identifying cohesive groups with many connections, including cliques, k-cores, components, and communities. Through this course, discover key groups in network data or identify closely connected clusters.

Network centrality refers to the importance of a particular node within a network. There are various ways to measure it, and this course provides an in-depth look at seven representative and widely used centrality indices, along with analyses using real-world data. Through the course, learn how to select and apply centrality indices suited to various types of network data.

The most basic approach in network data analysis is to analyze network characteristics. Starting with the frequencies of nodes and links, this course explores in detail the principles and meanings of various indices that describe structural features of networks, including the distribution of the number of connections, density, shortest distance, clustering coefficient, and more. If the structure of a network can be measured numerically, it becomes possible to compare it with other networks. Through this course, define the characteristics of your network data from various perspectives.

To visually inspect network data, explore its structure, and effectively present analytical results, you need to understand and apply a variety of visualization methods. NetMiner provides features that effectively represent network data and analysis results. Through this course, learn how to present your network analysis results aesthetically and communicate the key points effectively.

To obtain accurate and meaningful results and insights through network analysis, data must be processed appropriately according to the direction and methods of the analysis. This course explains the essential preprocessing methods you need to know when preparing and entering network data, and provides hands-on practice with various data cleaning, extraction, and transformation methods using NetMiner and real-world data.

Starting with the question “What are social networks and social network analysis (SNA)?,” this course explains the key theories and analytical methods related to them, the specific process of understanding network data and constructing it from observed phenomena, and the research design process. As the first step in social network analysis, data modeling is highly important. Through this course, gain a thorough understanding of “network data,” which may be unfamiliar, and try designing a process for creating or collecting network data directly from a topic of interest.

How can you analyze data in research papers and identify research trends? This course covers quantitative research trend analysis, from collecting bibliographic information to network analysis and topic modeling, using NetMiner 5 and Biblio Data Collector. By developing bibliographic data processing skills and network analysis techniques, you can identify research trends objectively and efficiently.

This course introduces the analysis tool NetMiner and covers its basic usage. Through hands-on practice with opening and editing data files, importing external data, and performing visualization and analysis, it helps reduce the apprehension and difficulty experienced by those encountering NetMiner for the first time.

"A first step for beginners using NetMiner 5—from basic usage to analyzing research data, all in one!&quot

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.

Visual design techniques that go beyond data visualization to instantly persuade others From visual cognitive design to future scenario analysis and AI workflows, all at once

We will study AX (AI Transformation) strategies that go beyond simply utilizing AI tools to fundamentally redesign an organization's work processes and decision-making systems. We will analyze AI transformation cases from global companies across various industries, such as retail, finance, and healthcare, and establish actionable strategies and roadmaps applicable to our organization.

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!

We explain 32 data visualization techniques very quickly and easily from a non-expert's perspective!

Are you struggling with inefficiencies caused by fragmented AI tools and manual tasks? Drawing on the expertise I’ve gained through hands-on experience, I’ll build integrated automation workflows to maximize your productivity.

Let's understand data analysis methods using Python, analyze and visualize various types of data on our own, and organize a data story outline!

This is a special lecture that can lay the theoretical foundation to simultaneously prepare for the Microsoft AZ-900 certification, and as the latest content reflecting the scope of questions as of May 2025, by providing content related to core data concepts, Azure's relational data, Azure's non-relational data, and Azure's analytical workloads in a form combining theory and practice, it can be utilized as a meaningful educational opportunity to not only obtain the certification but also take the first step towards becoming a data professional.

['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 is designed to help learners effectively visualize business data and develop the capability to support decision-making through visualization. Learners will study various theoretical backgrounds ranging from information visualization concepts to infographic principles, visual cognition and design principles, and Gestalt laws, and systematically acquire visualization techniques that can be applied in practice. In particular, by learning about color and visual variables, information design principles (symmetry, contrast, harmony, balance), typography and layout design (grid and inverted pyramid structure), learners will be able to apply these skills when creating actual business reports, presentation materials, and visualization dashboards. Through this, learners will develop the ability to create visual materials with high information delivery power, going beyond simple data analysis.

"Data analysis—don't stay trapped in complex Python syntax and Excel functions anymore." This course is a practical masterclass that automates the entire process of data processing, from Excel and Google Sheets to Python-linked analysis, by hiring the latest AI agents as your own dedicated data analysts. Instead of difficult coding knowledge, we introduce the latest 'Vibe Coding' method—commanding as if having a conversation in natural language—allowing even non-majors to enter the world of data analysis without any barriers. Through comprehensive hands-on practice using actual business data—covering everything from data preprocessing and visualization to report writing—experience a powerful AI workflow that you can apply to your work starting tomorrow. A lecture utilizing AI agents, Excel data analysis, chatGPT, Gemini, (Python), Genspark, Google Colab, chatbots, Google Antigravity, and notebookLM.

Learn how to use AI tools to strategically analyze raw data and visualize it persuasively—without complex or tedious statistical or coding knowledge. Starting with preprocessing messy raw data, you’ll complete complex tasks such as exploratory data analysis (EDA) and correlation analysis through just a few AI conversations, dramatically reducing the time required for practical analysis. Through concise, hands-on theory and scenario-based exercises, develop your own end-to-end professional skills for transforming numbers into powerful tools of business persuasion.

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.

In this course, you can learn a wide range of topics, from basic Excel functions to advanced formulas, data visualization, and dashboard creation, with a focus on hands-on practice. By providing templates and solution files, self-study is possible, and you can maximize work efficiency by mastering data analysis skills that can be immediately applied in real-world scenarios.

"Have you given up on data analysis after only studying the tools?" PMs at large corporations only need to look at the data already implemented by the analysis team. However, PMs at startups or side projects are different. They must build the 'foundation' themselves—from designing logs and labeling them to collecting the data. "Everyone says data is important, but what exactly should I implement and where in my service?" Stop getting lost in abstract concepts. Checking login history is the true beginning of Data-Driven Decision Making (DDDM).

Data Visualization Storytelling That's Perfectly Achievable with Excel Alone

An easy-to-understand introduction to data lecture by Korea's first data humanist, Shin Do-yong! Let's learn data-based thinking to properly read and utilize massive amounts of data!