| 週次 |
授課內容 |
| 第1週 |
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| 第2週 |
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| 第3週 |
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| 第4週 |
9月26日
課前準備:
-Bring own device (Windows 10 or MacOS, no tablets)
-All software/applications used in this class are open-sourced
-Programming in cloud platforms (RStudio cloud, Google Colab)
-Recommended accounts: GitHub
資料科學綜覽
a.What is Big Data?
b.Theory of Data Generation
c.How to equip for data scientist
d.Tools for professional data scientists |
| 第5週 |
10月3日
課前準備:
-Install R, RStudio, Python and Jupyter Notebook
-GitHub account
程式編寫的基礎
an introduction to the most commonly used programming languages in Data Science: Python, R and SQL
a.How to start programming?
b.Best programming practices
c.IDE (Integrated Development Environment)
d.Python programming basics
.Hands-on: use Python to download social media data
e.R programming basics
. Hands-on: Build visualization dashboard
f.SQL and database
. Hands-on: database management system on database server |
| 第6週 |
10月17日
資料蒐集方法
Introducing different data collection methods from small data (e.g. surveys, interview) to big data (e.g. social media, web data)
a.Made data and Found data
b.Data generation and data structure
c.Designing data collection scheme
d.API and non-API methods
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| 第7週 |
10月24日
資料視覺化
This course module covers basic theory of data visualization and emphasizes hands-on training in building charts and development data visualization applications.
a.Cognitive science of data visualization
b.Visual thinking
c.Exploratory Data Analysis
. Form follows functions, chart follows data types
d.Reactive and Interactive charts and applications |
| 第8週 |
10月31日
資料庫簡介
Introducing database concepts and systems, with hands-on database management practices and maintenance
a.Relational database
b.Entity-Relation Model (ERM)
c.Build database server
d.Database management and applications |
| 第9週 |
11月7日
機器學習簡介
This course module introduces concepts of machine learning and surveys different methods and applications.
a.Machine learning and Statistics
. Data model vs. Algorithmic model
. Explanation, Prediction, and Inference
b.Supervised learning
c.Unsupervised learning
d.Model selection
e.Interpretable Machine Learning
f.Machine learning applications |
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| 第18週 |
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