[Big Data Learning Center] *Free* 69th & 70th Open House - Bayesian Statistics | Quantification of Categories

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.

Learn multi-level data analysis using Structural Equation Modeling (SEM) bootstrapping techniques and Hierarchical Linear Modeling (HLM). This is an advanced course designed to help you grow into a statistical expert capable of analyzing complex research data, covering everything from advanced AMOS features to the practical application of HLM. You will master both theory and practice through real-world research cases in the fields of education, psychology, sociology, and health.

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.

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 about PSM using KoreaPlus SPSS.

Learn about Text Analytics using KoreaPlus SPSS.

Time Series: Become a Data Analysis Expert! Let's be analysts strong in theory & practice!

This course is for those who want to study machine learning but lack statistical knowledge.

This course covers fundamental discrete mathematics for computer science and engineering. It focuses on mathematical definitions, proofs, and applicable methods, covering various topics such as logic symbols, proof techniques, sets, relations, and graph theory.

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.

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.

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

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.

Learn multi-level data analysis using Structural Equation Modeling (SEM) bootstrapping techniques and Hierarchical Linear Modeling (HLM). This is an advanced course designed to help you grow into a statistical expert capable of analyzing complex research data, covering everything from advanced AMOS features to the practical application of HLM. You will master both theory and practice through real-world research cases in the fields of education, psychology, sociology, and health.

This course is ideal for those who want to focus on theory and context rather than practice, aiming to organize their knowledge of deep learning in depth and grasp the big picture. It makes the mathematical and statistical foundations of deep learning easy to understand and provides intuitive interpretations of key modern models—such as AutoEncoder, GAN, Transformer, and AlphaGo—from a developer's perspective.

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.

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

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.

This course introduces methods for harnessing data to answer questions of cultural, social, economic, and policy interest. Learners will study essential concepts in probability and statistics, along with modern data analysis techniques such as regression, econometrics, and data visualization.

This is an SPSS practice-oriented course that even non-statistics majors can easily follow. From data entry to regression analysis, you will learn the core functions of the practical exam step-by-step to successfully pass the Social Survey Analyst Level 2 practical exam.

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

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.

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.

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.

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.

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