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

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

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

Anyone who likes math, raise your hand! Anyone who wants to learn probability and statistics, raise your hand!

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 focuses on the modeling and analysis of random phenomena and processes, emphasizing statistical inference and probabilistic thinking. Learners will explore essential models and tools that combine mathematical rigor with conceptual understanding.

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

Learn about PSM using KoreaPlus SPSS.

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.

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.

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.

This course covers the practical impact that quantitative methods have had on today's most important companies. Learners will study key quantitative methods that create a competitive advantage, such as data mining, dynamic optimization, and simulation.

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.

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.

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.

In the era of AI and Big Data, are you utilizing your organization's data effectively? This course builds essential capabilities for reading and interpreting data, covering everything from the foundations of statistical thinking to the core principles of machine learning and deep learning. Grow into a practical leader capable of discovering patterns amidst volatility and making data-driven decisions.

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.

Learn the core functions of SPSS in a short period to pass the Social Survey Analyst Level 2 practical exam. Master SPSS know-how along with problem-solving techniques—ranging from data entry to descriptive statistics, cross-tabulation, t-tests, ANOVA, and correlation/regression analysis—to perfectly prepare for the practical exam.

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

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.

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