[NetMiner 4] Network Data Preprocessing

"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.

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.

Do you have the data but feel overwhelmed about how to read and process it in Python? Don't worry. You can handle it with the magic of Pandas. Pandas is the most powerful, efficient, and useful data processing library. Skill-UP your data preprocessing with Pandas! Get instant insights!

A lecture taught directly by the author of the book "SQL 200," published by Jungbo Munhwasa! Over the years, numerous data analysts have empowered their data retrieval skills through instructor Yeon-soo Yoo's SQL lectures.

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!

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.

Here are some key competencies that are often overlooked by others in the fiercely competitive and complex semiconductor process job market.

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.

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

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.

Through this lecture, students are expected to be able to check actual materials such as big data contests and self-introduction essays and determine the direction of their studies and job preparation.

We are now in an era where data speaks louder than intuition. Learn data analysis easily with ChatGPT!

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

We'll learn about the Tableau visualization tool needed for the Business Information Visualization Skills practical exam. Don't hesitate any longer and take on the challenge!

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

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.

"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.

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.

"Big Data + AI Full Stack Development" - The process of building big data that breathes life into AI is carried out in the form of a pilot project. We identify requirements, configure the platform step by step, and perform smart car data exploration and analysis.

Repetitive pipeline management in Data Warehouses (DW), now free yourself from that painful cycle with dbt! Building on the efficiency provided by dbt, become a data analytics engineer who creates higher value through business-centric data modeling, effective data lifecycle management, and more.

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.

SSIS, SSAS, MachineLearning, BI, ETL. You can learn important technologies that cannot be found in domestic books, YouTube, lectures, blogs, and academies. I also recommend it to those who are interested in employment at domestic large companies, American large companies, and American state funding agencies.