## Undergraduate

**Classification : Requisite Class Number : 326.212 Credit : 3 Grade : 1**

**Classification : Requisite Class Number : 326.211 Credit : 3 Grade : 2**

**Classification : Elective Class Number : 326.214 Credit : 3 Grade : 2**

**Classification : Requisite Class Number : 326.311 Credit : 3 Grade : 3**

**Classification : Requisite Class Number : 326.312 Credit : 3 Grade : 3**

**Classification : Requisite Class Number : 326.313 Credit : 3 Grade : 3**

**Classification : Elective Class Number : 326.314 Credit : 3 Grade : 3**

**Classification : Elective Class Number : 326.315 Credit : 3 Grade : 3**

**Classification : Elective Class Number : 326.316 Credit : 3 Grade : 3**

**Classification : Elective Class Number : 326.411 Credit : 3 Grade : 4**

**Classification : Elective Class Number : 326.412 Credit : 3 Grade : 4**

**Classification : Elective Class Number : 326.413 Credit : 3 Grade : 4**

**Classification : Elective Class Number : 326.414 Credit : 3 Grade : 4**

**Classification : Elective Class Number : 326.415 Credit : 3 Grade : 4**

**Classification : Elective Class Number : 326.416 Credit : 3 Grade : 4**

**Classification : Elective Class Number : 326.418 Credit : 3 Grade : 4**

**Classification : Elective Class Number : M0000.000500 Credit : 3 Grade : 4**

**Classification : Elective Class Number : M1399.000100 Credit : 3 Grade : 4**

**Classification : Elective Class Number : M1399.000600 Credit : 3 Grade : 3**

The course aims to provide students with an understanding and application of a variety of basic methods of data-driven research and analysis. This course intends to give an overview of various data analysis methods and approaches including refinement in problem solving, data collection and cleaning, exploratory data analysis, visualization, statistical inference and prediction, and decision-making. Also, students are expected to apply the methods to real data, experience the whole process of data analysis, and develop problem-solving skills.

**Classification : Elective Class Number : M1399.000700 Credit : 3 Grade : 4**

This course aims to expand to data analysis with a complex structure to understand theoretical explanation and limitations of the methods and to develop the ability to apply the correct analysis method. Select an appropriate analysis method for new data, implement a methodology through software, and improve the ability to deliver results to non-data science majors. To this end, we combine topics and deal with various statistical methodologies. 1. Frequentism and Bayesian Decision Theory 2. Statistical hypothesis testing 3. Multiple testing method to control FWER and false discovery rate 4. Statistical inference using empirical Bayes 5. Confidence interval 6. Various machine learning techniques 7. General linear model 8. Dimensional reduction methods are included in the curriculum.