Statistics is the quantitative tool on which psychologists rely very much to provide scientific inference about human behaviors. In order to equip students with necessary statistics knowledge, fundamental topics are addressed in the link. Including the concepts of probability distribution, parameter estimation, hypothesis testing, the concepts and computation of effect size and power, and the introduction of Chi-square distribution and its application in the test of independence and goodness of fit. Important focus, the correlation analysis, regression analysis, and the analysis of variance also be included. All the content can be applied to statistical software R.

Experimental methods are essential in psychology as they establish causal relationships. This course focuses on experimental design, covering hypotheses, variables, and different structures. It also emphasizes selecting appropriate statistical analyses for various designs to help students conduct research. Through case studies, students learn to apply these methods effectively. The course provides a systematic approach to experimental procedures, ensuring students understand key concepts and develop analytical skills. With a strong focus on data analysis, it prepares students to design and interpret experiments in psychological research, making it a crucial part of their academic training.

Deep learning neural networks are widely used in AI and machine learning, mimicking biological neurons to enable self-learning. They excel in tasks like classification, image recognition, and language analysis. Due to their similarity to neural structures, psychologists use them to study cognitive functions, for example memory and perception. This course covers the development, design, learning algorithms, and applications of neural networks, guiding students from building individual computation nodes to designing complete network architectures for various problem-solving tasks.

Since the era of Web 2.0, people have got used to sharing their lives on miscellaneous platforms on the internet, such as bulletin board systems, personal blogs, social media, and so on. The texts, music, and photographs that people post on social media thus become an enormous database of human behaviors for psychologists to study. This course will focus on introducing how to collect data from websites, how to analyze those data in appropriate ways, and how to do text analysis (including sentiment analysis).

Bayesian statistics has achieved great success in both research and industry, playing a key role in AI. Leading journals advocate its use over traditional methods, and companies like Google and Microsoft apply it to consumer behavior analysis. This course covers Bayesian concepts, probability distributions, inference, simulation, latent class analysis, hierarchical models, item response theory, and Dirichlet process mixtures. Students will learn to implement these methods using R and JAGS, gaining skills to quickly reach an international level in Bayesian analysis.

Psychology research relies on statistics and computing, from surveys to behavioral and physiological studies. Advances in technology and the internet have expanded research possibilities, making digital literacy crucial for psychology students. This course focuses on R programming, covering basic commands, data structures, loops, custom functions, web scraping, and text analysis. Students will develop essential computational skills to meet the evolving demands of psychological research.