| Course Code |
Title |
Course Credits |
Year |
Category |
| 04756 |
Introduction to Statistics (1) |
3 |
1 |
Basic Major |
|
Covers essential statistical concepts and theories that first-year students must understand as a foundation for the major. |
| 04757 |
Introduction to Statistics (2) |
3 |
1 |
Basic Major |
|
Introduces principles of statistical inference (estimation, hypothesis testing) and fundamental concepts of experimental design, regression, correlation, and categorical data analysis. |
| 21848 |
Statistical Mathematics (1) |
3 |
1 |
Basic Major |
|
Covers basic concepts of calculus (functions, limits, continuity, differentiation, integration) as mathematical foundations for probability and statistics. |
| 21849 |
Statistical Mathematics (2) |
3 |
1 |
Basic Major |
|
Focuses on linear algebra concepts essential for statistics and data science, including matrices, vectors, inverses, eigenvalues, and applications. |
| 56097 |
Statistics for AI |
3 |
1 |
Major |
|
Introduces regression and logistic models, probability distributions, and their connection to machine learning and deep learning methods (e.g., neural networks, CNNs, autoencoders, RNNs, transformers such as BERT). |
| 11823 |
Mathematical Statistics (1) |
3 |
2 |
Required Major |
|
Studies probability theory, distribution theory, and limit distributions forming the basis of statistical theory. |
| 11825 |
Mathematical Statistics (2) |
3 |
2 |
Required Major |
|
Covers estimation and hypothesis testing, including related conditions and testing methods. |
| 25039 |
Regression Analysis |
3 |
2 |
Required Major |
|
Introduces regression methods (simple, multiple, logistic), regression diagnostics, multicollinearity, dummy variables, transformations, with hands-on analysis in R. |
| 36450 |
Applied Probability |
3 |
2 |
Major |
|
Covers probability theory, conditional probability, conditional expectation, and basics of Markov chains. |
| 53994 |
Introduction to Statistical Learning |
3 |
2 |
Major |
|
Introduces statistical learning theory and algorithms such as regression, classification, model selection, and dimension reduction. |
| 56092 |
Programming for AI |
3 |
2 |
Major |
|
Uses Python for data preprocessing, visualization, and analysis. |
| 56091 |
Statistical Programming |
3 |
2 |
Major |
|
Introduces statistical models and methods for data analysis with practical training in R. |
| 09488 |
Nonparametrics |
3 |
3 |
Major |
|
Covers methods that do not assume a specific population distribution. |
|
Statistical Computing |
3 |
3 |
Major |
|
Covers random variable generation, MCMC, optimization, and density estimation for statistical modeling and analysis. |
| 08509 |
Analysis of Categorical Data |
3 |
3 |
Major |
|
Covers analysis of categorical data including cross-tabulation, logistic regression, logit and probit models, with applications in social sciences, medicine, and industry. |
| 05257 |
Multivariate Statistical Analysis |
3 |
3 |
Required Major |
|
Introduces multivariate statistical methods such as MANOVA, PCA, factor analysis, discriminant analysis, cluster analysis, and multivariate regression. |
| 56095 |
Actuarial Statistics |
3 |
3 |
Major |
|
Introduces actuarial mathematics for life insurance, covering survival models, life tables, annuities, premium calculations, and valuation. |
| 53992 |
Experimental Design |
3 |
3 |
Major |
|
Covers experimental design principles: factorial design, ANOVA, block design, and interaction analysis. |
| 21870 |
Statistical Quality Control |
3 |
3 |
Major |
|
Focuses on statistical process control methods for quality management in industrial applications. |
|
Data Preprocessing and Machine Learning |
3 |
3 |
Major |
|
Python-based preprocessing and machine learning theory with hands-on practice using scikit-learn and Google Colab. |
| 57128 |
Deep Learning for AI |
3 |
3 |
Major |
|
Provides practical knowledge for data scientists, covering GitHub, Markdown, and basics of deep learning. |
| 10576 |
Biostatistics |
3 |
4 |
Major |
|
This course introduces statistical analysis methods according to study design in medical research and provides an overview of clinical trial design. Key topics include cohort and case–control studies, Phase I–III clinical trials, sample size and power calculations, and a brief introduction to survival analysis. |
| 53991 |
Time Series Analysis |
3 |
4 |
Major |
|
Covers ARMA models, stationarity, autocorrelation, and forecasting techniques, with applications in economics, finance, and climate. |
| 34340 |
Bayesian Statistics |
3 |
4 |
Major |
|
Introductory course on Bayesian concepts, contrasting with frequentist methods, focusing on conceptual understanding rather than computation. |
| 56093 |
Data Mining |
3 |
4 |
Major |
|
Covers supervised and unsupervised learning methods for classification, prediction, clustering, and dimension reduction using R and Python. |
| 57130 |
Deep Learning for Image Data Analysis |
3 |
4 |
Major |
|
Covers CNNs, ResNet, Inception, YOLO, Mask R-CNN, U-Net, GANs, VAEs, and LSTMs for classification, object detection, generation, and video analysis. |
| 57131 |
Deep Learning for Text Data Analysis |
3 |
4 |
Major |
|
Covers NLP concepts, implementation of models such as BERT and GPT, with applications to sentiment analysis, chatbots, and similarity tasks in Korean and English. |
