Type of Credit: Required
Credit(s)
Number of Students
This course introduces programming using two of the most popular languages in data science: R and Python.
能力項目說明
Students are expected to learn the fundamental concepts of programming, including data types, control structures, functions, and object-oriented programming. They will also learn how to use R and Python to run simulations, manipulate data, perform statistical analysis, and create visualizations.
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
|
Week |
Topic |
Content and Reading Assignment |
Teaching Activities and Homework |
|
1 |
Introduction to R（程式環境介紹） |
Introduce the environment and applications of R program |
PowerPoint with case studies and assign homework to using basic mathematical R functions |
|
2 |
Control Flow and Loops in R（控制流程與迴圈） |
Introduce basic methods to code control flow and loops in R |
PowerPoint with case studies and assign homework to writing self-defined R functions |
|
3 |
Functions and Packages in R-I（函數與套件） |
Introduce functions and packages of statistical/mathematical methods in R |
PowerPoint with case studies and assign homework to using basic statistical R functions |
|
4 |
Functions and Packages in R-II（函數與套件） |
Introduce functions and packages of statistical/mathematical methods in R |
PowerPoint with case studies and assign homework to using advanced statistical R functions |
|
5 |
Data Manipulation and Visualization in R-I（資料處理與視覺化） |
Introduce functions and packages to import data and plot figures for visualization in R |
PowerPoint with case studies and assign homework to load/save data files for basic analysis in R |
|
6 |
Data Manipulation and Visualization in R-II（資料處理與視覺化） |
Introduce functions and packages to import data and plot figures for visualization in R |
PowerPoint with case studies and assign homework with more plot functions for basic data analysis in R |
|
7 |
Statistical Analysis in R-I（統計分析方法） |
Apply the functions and packages of statistical methods in R to data analysis |
PowerPoint with case studies and assign homework of advanced statistical analysis in R |
|
8 |
Statistical Analysis in R-II（統計分析方法） |
Apply the functions and packages of statistical methods in R to data analysis |
PowerPoint with case studies and assign homework of more advanced statistical analysis in R |
|
9 |
Midterm Exam （期中考） |
Midterm Exam |
Midterm Exam |
|
10 |
Introduction to Python（程式環境介紹） |
Introduce the environment and applications of Python |
PowerPoint with case studies and assign homework to using basic mathematical Python functions |
|
11 |
Control Flow and Loops in Python（控制流程與迴圈） |
Introduce basic methods to write control flow and loops in Python |
PowerPoint with case studies and assign homework to writing self-defined Python functions |
|
12 |
Functions and Modules in Python-I（函式與模組） |
Introduce functions and modules of machine learning methods in Python |
PowerPoint with case studies and assign homework to using basic machine learning Python functions |
|
13 |
Functions and Modules in Python-II（函式與模組） |
Introduce functions and modules of machine learning methods in Python |
PowerPoint with case studies and assign homework to using advanced machine learning Python functions |
|
14 |
Files and Exceptions in Python（檔案與錯誤處理） |
Introduce to read, write and append data files, and realize the exceptions in Python |
PowerPoint with case studies and assign homework to load/save data files for basic analysis in Python |
|
15 |
Object-Oriented Programming in Python（物件導向程式設計） |
Introduce basic conceptual ideas of object-oriented programming in Python |
PowerPoint with case studies and assign homework of advanced statistical analysis in Python |
|
16 |
Final Exam（期末考） |
Final Exam |
Final Exam |
|
17 |
Review of R program（R軟體回顧） |
A comprehensive review of R program by students |
Review |
|
18 |
Review of Python（Python回顧） |
A comprehensive review of Python by students |
Review |
1."R for Data Science" by Hadley Wickham and Garrett Grolemund.
2."Python for Data Analysis" by Wes McKinney.
URL of R reference book: https://r4ds.hadley.nz/ URL of Python reference book: https://wesmckinney.com/book/