*** Notice *** Kindly notice that there is no need to send me an email for course enrollment. If you would like to take the course but were unable to successfully enroll, please come to class in the second week. We may deliver the authorization codes. == Fall 2025 == [The features in the course:] (1). Interdisciplinary statistical analysis on scientific and non-scientific data (2). Hands-on practice in class instructed by teaching assistances (3). Emphasis on students’ practical achievements and teamwork [The contents in this course:] This course introduces students to the applications of statistical methods across various fields, starting with a general introduction to statistics in the first half and transitioning to data analysis applications in the second half. The examples extend to topics in the humanities and social sciences. The course emphasizes interdisciplinary applications of statistics with some theoretical insights. For practice, teaching assistants will demonstrate practical examples. We hope students can understand basic statistics and use simple tools to interpret data effectively. By visualizing the results of data analysis, we aim to inspire your perspective on these datasets. The first phase focuses on foundational statistics. Topics include random sampling, which is essential for statistical data analysis; analysis of variance (ANOVA), commonly used to detect differences among three or more groups; linear regression and linear models, which are ubiquitous statistical techniques; and classification problems in machine learning, which are essentially a type of nonlinear statistical method. The second phase of the course expands into interdisciplinary applications of data analysis. Topics include sentiment analysis in the humanities, ethical considerations in statistical practices, and exploration of interdisciplinary statistical data resources. This phase aims to demonstrate how statistical thinking can be applied meaningfully across diverse academic fields. In these topics, the fundamental principles of statistics play a crucial role in achieving predictive analytics. [Course Difficulty Level:] This course is designed for students ranging from senior undergraduates to those in master’s programs. However, junior undergraduate students (freshman or sophomore year) are also welcome to participate. Junior undergraduates may enroll using an authorization code during the additional enrollment period. [Teaching methods in each class:] 90 mins: Lecture. 60 mins: Teaching assistants demonstrate examples; students engage in hands-on exercises and teamwork discussion.

1. Students analyze the data using common statistical methods. 2. Students operate at least one statistical tool. 3. Students extract useful information from the dataset and explain it using statistical methods.

No

2 hours

(Book1): An Introduction to Statistical Learning with Applications in Python, by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor, Springer Nature Switzerland AG 2023. (Book2): Introduction to Statistics and Data Analysis with Exercises, Solutions and Applications in R, by Christian Heumann, Michael Schomaker and Shalabh, Springer International Publishing Switzerland 2016. Reading schedule Week 2~5: Book1 Chapter 1 and 2; Book2 Chapter 5, 6, 7 Week 6~7: Book1 Chapter 3 and 4 Week 8~11: Book1 Chapter 6 and 7 (Book3): Python Data Analytics with Pandas, NumPy, and Matplotlib, by Fabio Nelli, 2018 (book4): Artificial Intelligence with Python, by Prateek Joshi, 2017

Ditto