For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| STA3026 | Design of Experiments | 3 | 6 | Major | Bachelor | 3-4 | Korean | Yes | |
| Students learn how to plan, design and conduct experiments efficiently and effectively, and analyze the resulting data to obtain objective conclusions. Both design and statistical analysis issues are discussed. Students will be expected to utilize standard statistical software packages for computational purposes. | |||||||||
| STA3027 | Intermediate regression analysis and practice | 3 | 6 | Major | Bachelor | 3-4 | - | No | |
| This course is for undergraduate students who already studied ‘Introduction of Regression Analysis’. Students will learn theories of Regression Analysis by using SAS. First, this course provide elementary data analysis methods such as t-test, chi-squared test, and correlation analysis. Then, both simple and multiple regression analyses are reviewed. Students practice to analyze and discuss with SAS outputs of regression analysis with illustrative examples. Various regression disgonostics are studied including linear logistic regression model analysis. Real-life data analysis will be focused throughout the course. | |||||||||
| STA3028 | Statistics Co-op 1 | 2 | 4 | Major | Bachelor | - | No | ||
| Students taking this course will participate in an internship provided by a pre-approved institution. By doing so, they can enhance their capabilities of applying statistics theories and knowledge learned in classrooms. | |||||||||
| STA3029 | Statistics Co-op 2 | 3 | 6 | Major | Bachelor | - | No | ||
| Students taking this course will participate in an internship provided by a pre-approved institution. By doing so, they can enhance their capabilities of applying statistics theories and knowledge learned in classrooms. | |||||||||
| STA3030 | Statistics Individual Research 1 | 2 | 4 | Major | Bachelor | - | No | ||
| This is an independent study course for students who have finished an excellent accomplishment of the course requirements. Students will design and study their own research works under guidance of a tutor. | |||||||||
| STA3031 | Statistics Individual Research 2 | 2 | 4 | Major | Bachelor | - | No | ||
| This is an independent study course for students who have finished an excellent accomplishment of the course requirements. Students will design and study their own research works under guidance of a tutor. | |||||||||
| STA3034 | Large Data Management and Data Visualization | 3 | 6 | Major | Bachelor | - | No | ||
| This course will serve as a practicum for basic visualization and large data management. The goal of the course is to make students marketable as an entry level data scientist or scientific researcher where they are expected to perform data analysis for the ever changing environment. The students will get fluent in R, a popular language and environment for statistical computing, for end product implementation in real life setting, learn communication tools for reproducible analysis and development environment. In particular, the course will cover data post-processing, markdowns, creating R packages, data visualization tools, large data aggregation, data collection by using APIs. | |||||||||
| STA3035 | Introduction to Deep Learning | 3 | 6 | Major | Bachelor | 3-4 | Korean | Yes | |
| This course covers a wide variety of topics in deep learning and statistical modeling. While mathematical methods and theoretical aspects will be covered, the primary goal is to provide students with the tools and principles needed to solve the data science problems found in practice. This course also serves as a foundation on which more specialized courses and further independent study can build. | |||||||||
| STA3036 | Practice in Statistical Modelling and Machine Learning | 3 | 6 | Major | Bachelor | 3-4 | Korean | Yes | |
| This course mainly introduces various statistical models for various types of data and machine learning techniques that are not covered in ‘Statistical data mining’ course. Specifically, it includes machine learning methods such as association analysis, support vector machine, ensemble learning techniques, etc. and statistical models such as generalized linear models, nonlinear models, network models, spatial models, etc. In this course, students will practice what they have learned and analyze real datasets using the statistical models and learning methods from the class. The ultimate goal of this course is that students have attitudes and skills required for data scientists. | |||||||||
| STA3037 | Regression analysis for data science | 3 | 6 | Major | Bachelor | 3-4 | - | No | |
| Our overall goal is to use a various set of regression modeling tools and statistical theory to explore and analyze data with R for data science. The course includes a review and discussion of exploratory methods, informal techniques for summarizing and viewing data. We then consider simple linear regression, some linear algebra, and multiple linear regression. For all models, we will examine the underlying assumptions. More specifically, do the data support the assumptions? Do they contradict them? What are the consequences for inference? Finally, we will explore advanced topics such as model selection, penalized regression, high dimensional regression, bootstrapping regression, polynomial /nonparametric regression, etc, using real data such as genomics and finance data. | |||||||||
| STA3038 | Statistics and Data Science | 3 | 6 | Major | Bachelor | 3-4 | - | No | |
| This course mainly introduces essential topics required for data scientists. In this course, students will analyze real datasets from various fields to apply what they have learned in class. Specifically, it includes data preparation, computing and modeling, Machine learning, and applications to biostatistics and financial statistics, etc. The ultimate goal of this course is to learn essential attitudes and subjects in Mathematics, Statistics, Computing, and Collaboration, required for data scientists. | |||||||||
| STA3039 | Statistics Co-op 3 | 6 | 12 | Major | Bachelor | - | No | ||
| Students taking this course will participate in an internship provided by a pre-approved institution. By doing so, they can enhance their capabilities of applying statistics theories and knowledge learned in classrooms. | |||||||||
| STA3040 | Statistics Co-op 4 | 9 | 18 | Major | Bachelor | - | No | ||
| Students taking this course will participate in an internship provided by a pre-approved institution. By doing so, they can enhance their capabilities of applying statistics theories and knowledge learned in classrooms. | |||||||||
| STA4004 | Advanced Mathematical Statistics | 3 | 6 | Major | Bachelor/Master | 1 | Korean | Yes | |
| Advanced topics including the concept of probability, random variable and several statistical probability density functions are studied. Characteristics and relationships among the probability density functions are explained to apply to real world. Also random sample and distribution of sample mean will be discussed. | |||||||||
| STA4004 | Advanced Mathematical Statistics | 3 | 6 | Major | Bachelor/Master | 1 | Statistics | Korean | Yes |
| Advanced topics including the concept of probability, random variable and several statistical probability density functions are studied. Characteristics and relationships among the probability density functions are explained to apply to real world. Also random sample and distribution of sample mean will be discussed. | |||||||||