Statistics

This course examines the use of data science tools to summarize, visualize, and analyze data. Sensible workflows and clear interpretations are emphasized.

This course covers basic knowledge of statistics that is essential for exploring communication phenomena empirically and scientifically. Students develop practical statistical analysis skills. Please be aware: this course assumes that students are familiar with communication theory in general and understand social science research methods at a basic level. 

Based on the basic understanding of social science research methods, students will cultivate theoretical knowledge of basic statistical techniques and conduct practical analysis training using R.  

Topics include Communication Phenomena, Theory and Research Methods, and the Theory of Statistics; Basics of statistics; Probability and Probability Distribution; Principles of Statistical Reasoning: Estimation, Hypothesis Testing, Methods of statistical inference; Analysis and Inference of Discrete Data; Regression; Regression Analysis; Fundamentals of ANOVA.  

Prerequisite: Introduction to Mass Communication

This course introduces the basic concepts of statistics to systematically analyze the essential characteristics and interrelationships of economic data. 

The main goal of this course is to understand statistical analysis of data and to apply to various issues using Excel. The topics include the basic concept of probability and statistics with the application of practical cases 

Several Excel homework assignments will be assigned during the semester that will involve qualitative discussions and working through quantitative analyses and computations.  Please be aware that to complete the homework, you will need to learn some Excel functions by yourself. 

This course provides an introductory overview of probability theory, presented in a mathematically rigorous manner. Starting from the definitions of events, random variables, independence, and expectation, we also cover some basic applications such as weak convergence, the law of large numbers, characteristic functions, the central limit theorem, etc. 

Prerequisites: Elementary level of calculus (required), analysis (required), and linear algebra (optional). 

This course offers a study of the key concepts and methods of Statistical Learning by focusing on regression and classification in high-dimensional settings. Students model and analyze complex data, apply supervised and unsupervised learning techniques, and use computational tools for data analysis. This course puts special emphasis on problem formulation, variable selection, and practical implementation using modern software. 

Pre-requisites: Basics of Statistics 

This course offers a study of the principles and techniques of statistical graphics and data visualization. It discusses how to select and create effective visual representations for univariate, bivariate, and multivariate data. Topics include: graphical perception; the grammar of statistical graphs; exploratory data analysis; advanced data exploration such as maps and network charts; practical applications in statistics.