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. 

The course is designed for senior undergraduates that are interested in qualitative methods and of some research experience. The course introduces two major approaches to analysing qualitative data, namely, grounded-theory based coding approach and chronological sequence-based process data analysis. As well, the course also covers related topics to provide comprehensive guidance to students, including the philosophy of qualitative methods, collection of qualitative data, reporting qualitative findings, and ethical issues in qualitative data analysis.

This course builds on Stochastic Processes I and introduces an array of stochastic models with biomedical and other real world applications. Topics include Poisson process, compound Poisson process, marked Poisson process, point process, epidemic models, continuous time Markov chain, birth and death processes, martingale. The course requires students to take prerequisites.

This course teaches liberal arts students to understand the basic notions of probability theory and statistics, and to be able to comprehend the meaning of an elementary statistical analysis. While some mathematics is unavoidable to handle probabilities and statistics, the course focuses on comprehending simple analyses concerning randomness, subjective and objective probabilities, parameter estimation, confidence. After a short introduction of elementary probability theory, the most important discrete and continuous distributions, the law of large numbers and the central limit theorem, it discusses the basics of statistics, parameter estimation, confidence, and Bayesian statistics.