巨量資料分析

This Big Data Analysis course will introduce you to various statistical learning methods and how to apply them to real-world data analysis. Please note the following important details: Focus on Statistical Learning: This course is designed to teach you statistical learning methods and their applications in data analysis. We will cover key concepts and techniques essential for understanding and working with large data sets. Python Programming: This course will not teach Python programming from scratch. Instead, we will provide some simple Python code examples for you to practice independently at home. If your primary goal is to learn Python programming, this course may not be suitable for you. We encourage you to seek alternative resources if Python coding is your main focus. Supplementary Materials: Supplementary materials will be provided to help you learn Python programming and coding for data analysis on your own. These materials will support, but not replace, your independent study efforts. Please make sure you understand these expectations before committing to this course. If you are not comfortable with the self-learning aspects or the course focus, consider your enrollment carefully.

Statistics

Classroom Rules for Big Data Analysis Course Camera Policy: As this is an online course, all students are required to have their cameras turned on at all times during the class. Students who do not turn on their camera or do not appear visibly on the camera will be considered absent for that session. Attendance Requirement: Any student who is absent more than twice during the entire semester will receive a failing grade for this course. Class Participation: Active participation is essential and will be part of your grade. There will be random classroom activities throughout the semester, and your participation in these activities will count toward your attendance and participation grades. First Class Attendance: The course has exceeded maximum enrollment, so attendance at the first class is mandatory. Any student who does not participate in the first class will be dropped from the course. Course Requirements and Questionnaire: Students must carefully read all the course requirements and complete the provided questionnaire. If you find the course load too heavy, you are encouraged to drop the course immediately. Do not enroll in this course with the intention of complaining about the workload later in the semester. Please review these rules carefully and ensure you are prepared to meet all course expectations. 擬修課及加選的同學,請先填表單 https://forms.gle/MKmapXW4fQ1UgCFw7 1. 需自備電腦並安裝Anaconda, Python等軟體,相關軟體使用安裝會在課堂上說明. 2. 本課程遠距採用google meet連結, 若人不在google 服務地區,請先連上陽明交大vpn (VPN說明網址 https://it.nycu.edu.tw/it-services/networks/ssl-vpn/ ) 再點選以下連結: 如何加入 Google Meet 會議 Big Data Analytics 9月 6日 (星期五) · 下午12:30 - 3:10 時區:Asia/Taipei Google Meet 會議參加資訊 視訊通話連結:https://meet.google.com/fev-sikb-cmo 或撥打以下電話號碼:‪(US) +1 929-277-6374‬ PIN 碼:‪569 930 358‬# 更多電話號碼:https://tel.meet/fev-sikb-cmo?pin=8889038914427

Homework Assignments: 50% Class Participation and Attendance: 10% Midterm Exam: 20% Final Project or Final Paper Presentation: 20% Course Assignments and Grading Your final grade in this Big Data Analysis course will be based on the following components: Homework Assignments (50%): There will be four homework assignments throughout the semester, which collectively account for 50% of your final grade. Homework assignments will involve applying statistical learning methods to analyze data and interpret results. All assignments must be submitted by the due date. Late submissions may result in a grade penalty. Class Participation and Attendance (10%): Class participation and attendance will account for 10% of your final grade. Active participation in random classroom activities throughout the semester is required. Please note the attendance policy: Any student who is absent more than twice during the semester will receive a failing grade for this course. Midterm Exam (20%): The midterm exam will cover all topics discussed up to the midpoint of the semester and will account for 20% of your final grade. The exam will test your understanding of statistical learning methods and their applications in data analysis. Final Project or Final Paper Presentation (20%): You have the option to either work on a Final Project or present a Final Paper. Final Project: This can be done in groups of two to three people. You must select a dataset and get approval from the instructor before starting the project. The project will involve applying the methods learned in class to a real-world dataset, analyzing the data, and presenting the findings. Final Paper Presentation: This can be done individually or in pairs, depending on the length of the paper. You will present a research article related to the course topics, demonstrating your understanding and analysis of the content.

James, G., Witten, D., Hastie, T., Tibshirani, R. & Taylor, J. (2023) An introduction to Statistical Learning with Applications in Python. Springer

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聯絡方式

paulachen@nycu.edu.tw