*** 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 first week. We may deliver the authorization codes. The unsuccessful enrollment status will be announced after the preliminary course selection on January 17th. == Spring 2025 == [The features in the course:] (1). Interdisciplinary on non-scientific and scientific fields. (2). Hands-on practice in class instructed by proficient Teaching Assistances. (3). Invite the experts in industrials to share their experiences. [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 social sciences, such as social issue analysis and digital literacy. The course emphasizes interdisciplinary applications of statistics with some theoretical insights. For practice, teaching assistants will demonstrate practical examples, and guest speakers from the industry will be invited to share real-world stories of statistical data analysis. 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 introduces interdisciplinary data applications, including modern artificial intelligence, applications in the humanities and social sciences, empirical legal studies, and case studies on social issues. In these topics, the fundamental principles of statistics play a crucial role in achieving predictive analytics. We also invite experts and industry professionals to the classroom in a forum-style setting to share their insights and experiences from real-world cases. This approach aims to enrich students’ learning experience and broaden their perspectives. [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:] 100 mins: Lecture. 50 mins: Teaching Assistants demonstrate examples. Students engage in hands-on exercises and teamwork discussion.

TBD

N/A

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