NCHU Course Outline

Course Name	(中) 數據科學方法(5139)
	(Eng.) Fundamental Methodology for Data Science
Offering Dept	Graduate Institute of Data Science and Information Computing
Course Type	Elective			Credits	3	Teacher	TSAI, HUNG-HSU
Department	Department of Applied Mathematics (Data Science and Computing Program) / Undergraduate			Language	Chinese	Semester	2025-FALL
Course Description	This Data Science Course is introductory to Data Science. The syllabus of the course will introduce students to methods of processing data before dealing data, and concepts of data science algorithms for machine learning that help to gain some meaningful insights from structure or unstructured data. Statistics courses just analyze the history of the data, but with the help of data science courses and machine learning algorithms, students can predict future trends (profit, loss, or other insights). Therefore, the prediction results help students who want to learn how to make data-driven decisions and analyze ways to maximize the profit of an organization using data. The course will introduce the machine learning and/or deep learning algorithms for artificial intelligent approaches of learning from data.
Prerequisites

Relevance of Course Objectives and Core Learning Outcomes(%)			Teaching and Assessment Methods for Course Objectives
Course Objectives	Competency Indicators	Ratio(%)	Teaching Methods	Assessment Methods
Let students understand basic math background for data science. Let students understand approaches of learning from data. Let students understand the trend on the development of data science. Let students develop data models for real problems.	1.Basic Knowledge in Mathematical Sciences 4.Professional Knowledge in Scientific Computation	50 50	topic Discussion / Production Discussion Lecturing	Written Presentation Attendance Oral Presentation Assignment Internship
Course Content and Homework/Schedule/Tests Schedule
Week Course Content Week 1 Week #1: Introduction of syllabus, Data Science, building programming environment for data science, Computer assignment, Computer Project, Journal paper study and presentation, Aidea, https://aidata.nchu.edu.tw 1~2 classmates for each team Introduction of Artificial Intelligence and Big Data Data and data preparation Week 2 Week #2: Introduction of machine learning and its applications Applications of Machine Learning Association rule Naïve Bayes classifier Logistic Regression Decision Trees Random Forests K Nearest Neighbors algorithm Dimensionality reduction Clustering Week 3 Week #3-#4: Introduction to Neural network and its application (please refer to the URL, https://airobot.ccu.edu.tw/chapter-2-從感知網路說起/) Introduction to Neural network Architecture of Perceptrons Multi-layers architecture of Perceptrons Matrix computation for Perceptrons Recognition for Perceptron Network Learning algorithm for Perceptron Network Application on artificial Neural network - Handwriting image recognition Week 4 Week #3-#4: Introduction to Neural network and its application Perceptron Multi-layers Perceptron Support Vector Machine Support Vector Regression Back-propagation(BP) Gradient-vanishing Regulation Week 5 Week #5:Deep Learning- using Convolutional Neural Network (CNN) on the design of classifications and practical exercises) Introduction to deep learning Deep learning for CNN Practical exercises for CNN Using Keras CNN on recognition for MNIST handwritten digit database Keras Cifar-10 image database Using Keras CNN on recognition for Cifar-10 image database Week 6 Week #6: Models based on CNN LeNet VGGNet Residual Network DenseNet U-Net InceptionNet(GoogLeNet) Fully Convolutional Networks(FCNs) MobileNet V1 EfficientNet MaskRCNN Capnet VAE GAN Week 7 Week #07-#09: midterm report Computer project #1 report and presentation for each team Draft of project report with word and presentation by ppt for each team Present 1st paper by ppt for each student Week 8 Week #07-#09: midterm report Computer project #1 report and presentation for each team Draft of project report with word and presentation by ppt for each team Present 1st paper by ppt for each student Week 9 Week #07-#09: midterm report Computer project #1 report and presentation for each team Draft of project report with word and presentation by ppt for each team Present 1st paper by ppt for each student Week 10 Week #10:CNN applications for image classification, image retrieval and image segmentation Autoencoder and decorder (image transformer) Transfer Learning You Only Look Once(YoLo) RCNN/Fast-RCNN Multi-label/Multiclass/Multilabel Multitask CNNs are applied in the design of image classification, image retrieval, and image segmentation Week 11 Week #11: paper survey for CNN applications -Content-Based Image Retrieval Based on CNN and SVM --VAE-Y-Autoencoders-disentangling latent representations via sequential-encoding -Neural network-based multi-task learning for inpatient flow classification and length of stay prediction -DML-PL Deep metric learning based pseudo-labeling framework -Hard Sample Aware Noise Robust Learning Week 12 Week #12: CNN applications for Intelligent medical data analysis. -Cough sound -Lung sound (Respiratory Sound) -CXR images -CT images Week 13 Week #13: deep learning - Long short term memory model for the design of prediction models and practical exercises Deep Learning–RNN-LSTM recurrent neural networks(RNN) Long short-term memory (LSTM) Week 14 Week #14-#16: final-term report computer project #2 report with word format and presentation by ppt for each team Present 2nd paper by ppt for each student Week 15 Week #14-#16: final-term report Computer project #2 report with word format and presentation by ppt for each team Present 2nd paper by ppt for each student Week 16 Week #14-#16: final-term report Computer project #2 report with word format and presentation by ppt for each team Final project report with ”word” (NOT pdf) format and presentation by ppt for each team, which highlights (survey) at least 15 sci/ssci journal papers (NOT Mega or open access Journals) for the topic of the computer projects self-directed learning    01.Participation in professional forums, lectures, and corporate sharing sessions related to industry-government-academia-research exchange activities.    03.Preparing presentations or reports related to industry and academia.    05.Participation in various workshops organized by different departments of NCHU.
Evaluation
Participation: 10%; Paper presentation: 30%; Computer assignment and report: 20%; Project (Project proposal, result and report): 40%; Bonus: 20%
Textbook & other References
==Textbook 大數據分析與資料挖礦 2/e，作者： 簡禎富 、 許嘉裕，(前程文化) Big-data-analytics-Data-mining 2/e, authors: Hsu, Chia-yu and Chien, Chen-fu (https://www.fcmc.com.tw/) 製造數據科學，作者：李家岩、洪佑鑫 (前程文化) Data Science in Manufacturing, authors: Hung, Yu-Hsin Jeff and Lee, Chia-Yen (https://www.fcmc.com.tw/) ===References== Data Science from Scratch: First Principles with Python, 2/e (O’Reilly) Data Science from Scratch｜用 Python 學資料科學, 2/e (中文版) (碁峰資訊) https://github.com/joelgrus/data-science-from-scratch ====== Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.) 書刊名: 精通機器學習使用Scikit-Learn, Keras與TensorFlow，作者: 杰龍 (Géron, Aurélien)，其他作者: 賴屹民，(歐禮萊) ====== https://scikit-learn.org/stable/user_guide.html === Lau et al., Principles and Techniques of Data Science. https://ds100.org/sp18/assets/lectures/lec01/01-intro-to-data100_v2.pdf == Python資料科學與機器學習：從入門到實作必備攻略 (博碩文化) ===== Raschka, Sebastian, and Vahid Mirjalili. Python Machine Learning, 3rd Ed. Packt Publishing, 2019. https://github.com/rasbt/python-machine-learning-book-3rd-edition Python機器學習第三版（上）譯者：劉立民、吳建華　譯(博碩文化) Python機器學習第三版（下）譯者：劉立民、吳建華　譯(博碩文化) ==== 資料科學的建模基礎 - 別急著coding！你知道模型的陷阱嗎？作者：江崎貴裕 著、王心薇 譯、施威銘研究室 監修(旗標) === Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Neural Networks: A Comprehensive Foundation, Simon Haykin https://airobot.ccu.edu.tw/chapter-2-%e5%be%9e%e6%84%9f%e7%9f%a5%e7%b6%b2%e8%b7%af%e8%aa%aa%e8%b5%b7/ == 深度學習-影像處理應用，彭彥璁、李偉華、陳彥蓉，全華圖書 (Deep learning for image processing applications), 2023.06.
Teaching Aids & Teacher's Website
http://www.amath.nchu.edu.tw/member_detail.php?Key=71
Office Hours
星期二 Tuesday 8th-9th classes
Sustainable Development Goals, SDGs(Link URL)
04.Quality Education include experience courses：N

Please respect the intellectual property rights and use the materials legally.Please respect gender equality.
Update Date, year/month/day：2025/09/07 23:15:18	Printed Date, year/month/day：2026 / 2 / 19
The second-hand book website：http://www.myub.com.tw/