Introduction to Data Science

Henry Horng-Shing Lu (盧鴻興)

Institute of Statistics
National Yang Ming Chiao Tung University

Tel: 03-5731870 Email: henryhslu@nycu.edu.tw

Goals:

This course will introduce data science with the computer languages of R and Python. Undergraduate knowledge of statistics will be helpful for the understanding of this course.

Course Outlines:

• Introduction of Data Science
• Introduction of R and Python
• Cleaning Data into R and Python
• Data Visualization
• Exploratory Data Analysis
• Regression (Supervised Learning)
• Classification (Supervised Learning)
• Text Mining
• Clustering (Unsupervised Learning)
• Neural Network and Deep Learning

Reference:

• M. A. Pathak, Beginning Data Science with R, 2014, Springer-Verlag.


• K.-T. Tsai, Machine Learning for Knowledge Discovery with R: Methodologies for Modeling, Inference and Prediction, 2021, Chapman and Hall/CRC.

Videos:

• The Human Face of Big Data
• Data Science (數據科學)
• Machine Learning (機器學習)
• Artificial Intelligence (人工智慧)
• Biomedical Applications (生醫運用)

Data Sources:

• Data in UCI Machine Learning Repository
• Kaggle Datasets

　

Evaluation:

• Homework: 70%
• Term Project: 30%

Links:

• Local links of statistical computing and statistics
• Wiki
• The Data Mine
• Data Mining Software Comparison
• Weka: Free Data Mining Software in Java
• Virtual library of statistics
• R: a free software environment for statistical computing and graphics